From b1cd3aa867ac6fc7387b84dfe782584e98c45c48 Mon Sep 17 00:00:00 2001
From: Pawel Raasz <pawel.raasz@intel.com>
Date: Wed, 18 Sep 2024 14:49:55 +0200
Subject: [PATCH 01/49] [core] Improve Constant get_vector for low precisions
 (#26177)

### Details:
 - The Constant `get_vector` works correctly for low precisions.
- Initialize not used bits in Constant buffer for low precisions to
avoid undefined values.

### Tickets:
 - CVS-149867
---
 src/core/include/openvino/op/constant.hpp     | 39 +++++++--
 src/core/src/op/constant.cpp                  | 37 +++++++-
 src/core/tests/constant.cpp                   | 86 +++++++++++--------
 .../ir/tests/frontend_test_basic.cpp          | 86 +++++++++++++++++++
 4 files changed, 206 insertions(+), 42 deletions(-)

diff --git a/src/core/include/openvino/op/constant.hpp b/src/core/include/openvino/op/constant.hpp
index 8247e41cd3ff07..df630e96576db7 100644
--- a/src/core/include/openvino/op/constant.hpp
+++ b/src/core/include/openvino/op/constant.hpp
@@ -277,13 +277,33 @@ class OPENVINO_API Constant : public Op {
     /// \return The initialization literals for the tensor constant.
     std::vector<std::string> get_value_strings() const;
 
-    template <typename T>
+    /// @brief Get constant buffer as vector of element type T.
+    ///
+    /// For low precision the vector do not perform bit unpacks.
+    /// The returned vector has N elements where:
+    /// - N is (elements count * (precision byte size / T byte size)) for standard precisions.
+    /// - N is (byte size) for low precisions.
+    ///
+    /// @tparam T Output vector type which byte size must be less or equal of byte size of Constant's precision.
+    /// @return Vector of N elements of Type T.
+    template <typename T, typename std::enable_if<!std::is_same<bool, T>::value>::type* = nullptr>
     std::vector<T> get_vector() const {
-        const T* p = get_data_ptr<T>();
-        if (p == nullptr) {
-            OPENVINO_THROW("Cannot create vector! Buffer is not allocated.");
+        const auto p = get_data_ptr<T>();
+        OPENVINO_ASSERT(p != nullptr, "Cannot create vector! Buffer is not allocated.");
+        auto v = std::vector<T>(p, p + (get_byte_size() / sizeof(T)));
+        if (!m_alloc_buffer_on_visit_attributes) {
+            // result vector requires update when Constant share data (e.g read weight from IR binary file)
+            set_unused_bits(v.data());
         }
-        return std::vector<T>(p, p + shape_size(m_shape));
+        return v;
+    }
+
+    template <typename T, typename std::enable_if<std::is_same<bool, T>::value>::type* = nullptr>
+    std::vector<T> get_vector() const {
+        const auto p = get_data_ptr<T>();
+        OPENVINO_ASSERT(p != nullptr, "Cannot create vector! Buffer is not allocated.");
+        auto v = std::vector<T>(p, p + (get_byte_size() / sizeof(T)));
+        return v;
     }
 
     /// \brief Return the Constant's value as a vector cast to type T
@@ -403,7 +423,7 @@ class OPENVINO_API Constant : public Op {
     void alloc_buffer_on_visit_attributes(bool val);
 
     /// @brief Get view on constant data as tensor.
-    /// @return OV::Tensor with constant data.
+    /// @return ov::Tensor with constant data.
     const Tensor get_tensor_view() const;
 
     /// @return Constant's strides in bytes.
@@ -414,6 +434,13 @@ class OPENVINO_API Constant : public Op {
 
     size_t get_num_elements_to_cast(const int64_t n) const;
 
+    /// \brief Sets buffer's not used bits to zero.
+    ///
+    /// In case of low precision there can be some storage area which is not used (not defined state).
+    ///
+    /// \param buffer  Pointer to buffer with Constant values.
+    void set_unused_bits(void* buffer) const;
+
 #ifdef __clang__
 #    pragma clang diagnostic push
 #    ifdef __has_warning
diff --git a/src/core/src/op/constant.cpp b/src/core/src/op/constant.cpp
index fb3086553782ca..90b293fb564672 100644
--- a/src/core/src/op/constant.cpp
+++ b/src/core/src/op/constant.cpp
@@ -258,9 +258,41 @@ void Constant::allocate_buffer(bool memset_allocation) {
     if (m_element_type == ov::element::string) {
         m_data = std::make_shared<StringAlignedBuffer>(num_elements, byte_size, host_alignment(), memset_allocation);
     } else {
+        constexpr uint8_t init_value = 0;
         m_data = std::make_shared<AlignedBuffer>(byte_size, host_alignment());
+
         if (memset_allocation) {
-            std::memset(m_data->get_ptr(), 0, m_data->size());
+            std::memset(m_data->get_ptr(), init_value, m_data->size());
+        } else {
+            set_unused_bits(m_data->get_ptr());
+        }
+    }
+}
+
+void Constant::set_unused_bits(void* buffer) const {
+    const auto byte_size = m_data->size();
+
+    if (byte_size > 0) {
+        const auto num_elements = shape_size(m_shape);
+
+        if (element::is_bit_type(m_element_type)) {
+            constexpr size_t storage_unit_byte_size = 1;
+            const auto not_aligned_elements = num_elements % (8 / m_element_type.bitwidth());
+            const uint8_t not_used_bits_mask = 0xff >> (m_element_type.bitwidth() * not_aligned_elements);
+            reinterpret_cast<uint8_t*>(buffer)[byte_size - storage_unit_byte_size] &= ~not_used_bits_mask;
+        } else if (element::is_nibble_type(m_element_type) && (num_elements % 2)) {
+            constexpr size_t storage_unit_byte_size = 1;
+            reinterpret_cast<uint8_t*>(buffer)[byte_size - storage_unit_byte_size] &= 0x0FU;
+        } else if (element::is_split_bit_type(m_element_type)) {
+            constexpr size_t storage_unit_byte_size = 3;
+            const auto num_values = (24U / m_element_type.bitwidth());
+            const auto not_aligned_elements = num_elements % num_values;
+            const uint16_t not_used_upper_mask = ~(0xffff >> (not_aligned_elements * (16U / num_values)));
+
+            auto ptr = reinterpret_cast<uint8_t*>(buffer) + (byte_size - storage_unit_byte_size);
+            ptr[0] &= not_used_upper_mask >> 8U;
+            ptr[1] &= not_used_upper_mask & 0x00ff;
+            ptr[2] &= ~(0xff >> (not_aligned_elements * (8U / num_values)));
         }
     }
 }
@@ -290,7 +322,8 @@ Constant::Constant(const Constant& other)
       m_byte_strides{other.m_byte_strides},
       m_data{other.m_data},
       m_all_elements_bitwise_identical{other.m_all_elements_bitwise_identical.load()},
-      m_all_elements_bitwise_identical_checked{other.m_all_elements_bitwise_identical_checked.load()} {
+      m_all_elements_bitwise_identical_checked{other.m_all_elements_bitwise_identical_checked.load()},
+      m_alloc_buffer_on_visit_attributes{other.m_alloc_buffer_on_visit_attributes} {
     constructor_validate_and_infer_types();
 }
 
diff --git a/src/core/tests/constant.cpp b/src/core/tests/constant.cpp
index aaf91a4335f798..012a2aab6cfa13 100644
--- a/src/core/tests/constant.cpp
+++ b/src/core/tests/constant.cpp
@@ -100,7 +100,7 @@ TEST(constant, boolean_vector) {
     EXPECT_EQ(v[2], 1);
     EXPECT_EQ(v[3], 0);
 
-    const char* p = c.get_data_ptr<char>();
+    const auto p = c.get_vector<char>();
     EXPECT_EQ(p[0], 1);
     EXPECT_EQ(p[1], 0);
     EXPECT_EQ(p[2], 1);
@@ -301,7 +301,7 @@ TEST(constant, int4_string) {
 
     const auto p = c.get_data_ptr<uint8_t>();
     EXPECT_EQ(0x01, p[0]);
-    EXPECT_EQ(0x0F, p[1] & 0x0F);
+    EXPECT_EQ(0x0F, p[1]);
 
     EXPECT_EQ(input, c.get_value_strings());
 
@@ -322,7 +322,7 @@ TEST(constant, int4_string_broadcast_negative_number) {
 
     const auto p = c.get_data_ptr<uint8_t>();
     EXPECT_EQ(0xFF, p[0]);
-    EXPECT_EQ(0x0F, p[1] & 0x0F);
+    EXPECT_EQ(0x0F, p[1]);
 
     EXPECT_EQ(std::vector<std::string>(3, "-1"), c.get_value_strings());
 }
@@ -338,7 +338,7 @@ TEST(constant, int4_string_broadcast_positive_number) {
 
     const auto p = c.get_data_ptr<uint8_t>();
     EXPECT_EQ(0x11, p[0]);
-    EXPECT_EQ(0x01, p[1] & 0x0F);
+    EXPECT_EQ(0x01, p[1]);
 
     EXPECT_EQ(std::vector<std::string>(3, "1"), c.get_value_strings());
 }
@@ -354,7 +354,7 @@ TEST(constant, int4_vector_negative_number) {
 
     const auto p = c.get_data_ptr<uint8_t>();
     EXPECT_EQ(0xEF, p[0]);
-    EXPECT_EQ(0x0F, p[1] & 0x0F);
+    EXPECT_EQ(0x0F, p[1]);
 }
 
 TEST(constant, int4_vector_positive_number) {
@@ -368,7 +368,7 @@ TEST(constant, int4_vector_positive_number) {
 
     const auto p = c.get_data_ptr<uint8_t>();
     EXPECT_EQ(0x21, p[0]);
-    EXPECT_EQ(0x05, p[1] & 0x0F);
+    EXPECT_EQ(0x05, p[1]);
 }
 
 TEST(constant, int4_vector_broadcast_negative_number) {
@@ -382,7 +382,7 @@ TEST(constant, int4_vector_broadcast_negative_number) {
 
     const auto p = c.get_data_ptr<uint8_t>();
     EXPECT_EQ(0xFF, p[0]);
-    EXPECT_EQ(0x0F, p[1] & 0x0F);
+    EXPECT_EQ(0x0F, p[1]);
 }
 
 TEST(constant, int4_vector_broadcast_positive_number) {
@@ -394,9 +394,9 @@ TEST(constant, int4_vector_broadcast_positive_number) {
     EXPECT_EQ(v[1], int8_t(3));
     EXPECT_EQ(v[2], int8_t(3));
 
-    const auto p = c.get_data_ptr<uint8_t>();
+    const auto p = c.get_vector<uint8_t>();
     EXPECT_EQ(0x33, p[0]);
-    EXPECT_EQ(0x03, p[1] & 0x0F);
+    EXPECT_EQ(0x03, p[1]);
 }
 
 TEST(constant, int4_input_value_validation) {
@@ -447,8 +447,6 @@ TEST(constant, int8_string) {
 
     EXPECT_EQ(input, c.get_value_strings());
 
-    EXPECT_EQ(input, c.get_value_strings());
-
     for (unsigned i = 0; i != input.size(); ++i) {
         EXPECT_EQ(input[i], c.convert_value_to_string(i));
     }
@@ -1004,8 +1002,8 @@ TEST(constant, uint3_string_broadcast) {
 
     const auto p = c.get_data_ptr<uint8_t>();
     EXPECT_EQ(p[0], 0b01010101);
-    EXPECT_EQ(p[1] & 0b11000000, 0b01000000);
-    EXPECT_EQ(p[2] & 0b11000000, 0b11000000);
+    EXPECT_EQ(p[1], 0b01000000);
+    EXPECT_EQ(p[2], 0b11111000);
 }
 
 TEST(constant, uint3_vector_less_than_one_storage_unit) {
@@ -1018,10 +1016,10 @@ TEST(constant, uint3_vector_less_than_one_storage_unit) {
     ASSERT_EQ(v.size(), shape_size(shape));
     EXPECT_THAT(v, ElementsAre(5, 3, 1));
 
-    const auto p = c.get_data_ptr<uint8_t>();
-    EXPECT_EQ(p[0] & 0b11111100, 0b01110100);
-    // p[1] ignore
-    EXPECT_EQ(p[2] & 0b11100000, 0b10000000);
+    const auto p = c.get_vector<uint8_t>();
+    EXPECT_EQ(p[0], 0b01110100);
+    EXPECT_EQ(p[1], 0);
+    EXPECT_EQ(p[2], 0b10000000);
 }
 
 TEST(constant, uint3_vector_greater_than_one_storage_unit) {
@@ -1034,14 +1032,14 @@ TEST(constant, uint3_vector_greater_than_one_storage_unit) {
     ASSERT_EQ(v.size(), shape_size(shape));
     EXPECT_THAT(v, ElementsAre(2, 3, 1, 0, 4, 5, 6, 7, 5, 2));
 
-    const auto p = c.get_data_ptr<uint8_t>();
+    const auto p = c.get_vector<uint8_t>();
     EXPECT_EQ(p[0], 0b10110100);
     EXPECT_EQ(p[1], 0b00011011);
     EXPECT_EQ(p[2], 0b00001111);
 
-    EXPECT_EQ(p[3] & 0b11110000, 0b01100000);
-    // p[4] ignore
-    EXPECT_EQ(p[5] & 0b11000000, 0b10000000);
+    EXPECT_EQ(p[3], 0b01100000);
+    EXPECT_EQ(p[4], 0);
+    EXPECT_EQ(p[5], 0b10000000);
 }
 
 TEST(constant, uint3_vector_broadcast) {
@@ -1143,7 +1141,7 @@ TEST(constant, uint4_vector) {
     EXPECT_EQ(v[2], 1);
     EXPECT_EQ(v[3], 0);
 
-    const auto p = c.get_data_ptr<uint8_t>();
+    const auto p = c.get_vector<uint8_t>();
     EXPECT_EQ(p[0], 0x01);
     EXPECT_EQ(p[1], 0x01);
 }
@@ -1159,9 +1157,29 @@ TEST(constant, uint4_vector_broadcast) {
 
     const auto p = c.get_data_ptr<uint8_t>();
     const auto first_byte = p[0];
-    const auto second_byte = p[1] & 0x0F;
+    const auto second_byte = p[1];
     EXPECT_EQ(0x11, first_byte);
     EXPECT_EQ(0x01, second_byte);
+
+    const auto vector = c.get_vector<uint8_t>();
+    EXPECT_EQ(vector[0], 0x11);
+    EXPECT_EQ(vector[1], 0x01);
+}
+
+TEST(constant, uint4_get_vector_from_one_element) {
+    auto c = std::make_shared<op::v0::Constant>(element::u4, Shape{1}, 9);
+    auto v = c->get_vector<uint8_t>();
+
+    ASSERT_EQ(v.size(), 1);
+    EXPECT_EQ(v[0], 0x09);
+}
+
+TEST(constant, uint4_get_vector_from_scalar) {
+    auto c = std::make_shared<op::v0::Constant>(element::u4, Shape{}, 8);
+    auto v = c->get_vector<uint8_t>();
+
+    ASSERT_EQ(v.size(), 1);
+    EXPECT_EQ(v[0], 0x08);
 }
 
 TEST(constant, uint4_input_value_validation) {
@@ -1240,8 +1258,8 @@ TEST(constant, uint6_vector_less_than_one_storage_unit) {
 
     const auto p = c.get_data_ptr<uint8_t>();
     EXPECT_EQ(p[0], 0x57);
-    EXPECT_EQ(p[1] & 0xF0, 0x10);
-    EXPECT_EQ(p[2] & 0b1111100, 0b00010000);
+    EXPECT_EQ(p[1], 0x10);
+    EXPECT_EQ(p[2], 0b00010000);
 }
 
 TEST(constant, uint6_vector_greater_than_one_storage_unit) {
@@ -1254,14 +1272,14 @@ TEST(constant, uint6_vector_greater_than_one_storage_unit) {
     ASSERT_EQ(v.size(), shape_size(shape));
     EXPECT_THAT(v, ElementsAre(25, 3, 1, 0, 45, 5));
 
-    const auto p = c.get_data_ptr<uint8_t>();
+    const auto p = c.get_vector<uint8_t>();
     EXPECT_EQ(p[0], 0x93);
     EXPECT_EQ(p[1], 0x10);
     EXPECT_EQ(p[2], 0b01000000);
 
     EXPECT_EQ(p[3], 0xd5);
-    // p[4] ignore
-    EXPECT_EQ(p[5] & 0b11110000, 0b10000000);
+    EXPECT_EQ(p[4], 0);
+    EXPECT_EQ(p[5], 0b10000000);
 }
 
 TEST(constant, uint6_vector_broadcast) {
@@ -1667,7 +1685,7 @@ TEST(constant, nf4_write_custom_type) {
     auto p = c.get_data_ptr<uint8_t>();
 
     EXPECT_EQ(p[0], 0x20);
-    EXPECT_EQ(p[1] & 0x0f, 0x0f);
+    EXPECT_EQ(p[1], 0x0f);
     EXPECT_THROW(c.get_strides(), Exception);
 }
 
@@ -2016,7 +2034,7 @@ TEST(constant, f4e2m1_vector) {
     const auto p = c.get_data_ptr<uint8_t>();
     EXPECT_EQ(p[0], 0x6b);
     EXPECT_EQ(p[1], 0x3c);
-    EXPECT_EQ(p[2] & 0x0F, 0x0d);
+    EXPECT_EQ(p[2], 0x0d);
 }
 
 TEST(constant, f4e2m1_from_float_vector) {
@@ -2024,10 +2042,10 @@ TEST(constant, f4e2m1_from_float_vector) {
     auto v = c.cast_vector<float>();
     EXPECT_THAT(v, ElementsAre(-1.5f, 4.0f, -2.0f, 1.5f, -3.0f));
 
-    const auto p = c.get_data_ptr<uint8_t>();
+    const auto p = c.get_vector<uint8_t>();
     EXPECT_EQ(p[0], 0x6b);
     EXPECT_EQ(p[1], 0x3c);
-    EXPECT_EQ(p[2] & 0x0F, 0x0d);
+    EXPECT_EQ(p[2], 0x0d);
 }
 
 TEST(constant, f4e2m1_vector_broadcast) {
@@ -2039,7 +2057,7 @@ TEST(constant, f4e2m1_vector_broadcast) {
 
     const auto p = c.get_data_ptr<uint8_t>();
     EXPECT_EQ(0x33, p[0]);
-    EXPECT_EQ(0x03, p[1] & 0x0F);
+    EXPECT_EQ(0x03, p[1]);
 }
 
 TEST(constant, f4e2m1_write_then_cast_custom_type) {
@@ -2109,7 +2127,7 @@ TEST(constant, f8e8m0_from_float_vector) {
     auto v = c.cast_vector<float>();
     EXPECT_THAT(v, ElementsAre(std::numeric_limits<float>::min() / 2, 4.0f, 2.0f, 2.0f, 2.0f));
 
-    const auto p = c.get_data_ptr<uint8_t>();
+    const auto p = c.get_vector<uint8_t>();
     EXPECT_EQ(p[0], 0x00);
     EXPECT_EQ(p[1], 0x81);
     EXPECT_EQ(p[2], 0x80);
diff --git a/src/frontends/ir/tests/frontend_test_basic.cpp b/src/frontends/ir/tests/frontend_test_basic.cpp
index 5780168ce80dba..ca1a244ffc7900 100644
--- a/src/frontends/ir/tests/frontend_test_basic.cpp
+++ b/src/frontends/ir/tests/frontend_test_basic.cpp
@@ -356,6 +356,92 @@ TEST_P(IRFrontendMMapTests, model_with_weights_reading_from_disk) {
     EXPECT_TRUE(res.valid) << res.message;
 }
 
+TEST_P(IRFrontendMMapTests, model_with_lp_weights_reading_from_disk) {
+    std::string xmlModel = R"V0G0N(
+<?xml version="1.0" ?>
+<net name="Model" version="11">
+	<layers>
+		<layer id="0" name="A" type="Parameter" version="opset1">
+			<data shape="" element_type="u4" />
+			<output>
+				<port id="0" precision="U4" names="" />
+			</output>
+		</layer>
+		<layer id="1" name="my_const" type="Const" version="opset1">
+			<data element_type="u4" shape="" offset="0" size="1" />
+			<output>
+				<port id="0" precision="U4" />
+			</output>
+		</layer>
+		<layer id="2" name="Add_4" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="U4" />
+				<port id="1" precision="U4" />
+			</input>
+			<output>
+				<port id="2" precision="U4" />
+			</output>
+		</layer>
+		<layer id="3" name="Result_5" type="Result" version="opset1">
+			<input>
+				<port id="0" precision="U4" />
+			</input>
+		</layer>
+	</layers>
+	<edges>
+		<edge from-layer="0" from-port="0" to-layer="2" to-port="0" />
+		<edge from-layer="1" from-port="0" to-layer="2" to-port="1" />
+		<edge from-layer="2" from-port="2" to-layer="3" to-port="0" />
+	</edges>
+	<rt_info />
+</net>
+)V0G0N";
+
+    std::vector<unsigned char> buffer(1, 0);
+    auto buffer_ptr = reinterpret_cast<uint8_t*>(buffer.data());
+    buffer_ptr[0] = 0x18;
+    auto t = ov::Tensor(ov::element::u4, ov::Shape{}, buffer.data());
+
+    createTemporalModelFile(xmlModel, buffer);
+
+    std::shared_ptr<ov::Model> model;
+
+    ov::Core new_core;
+    new_core.set_property(ov::enable_mmap(GetParam()));
+    OV_ASSERT_NO_THROW(model = new_core.read_model(xmlFileName, binFileName));
+    ASSERT_TRUE(!!model);
+
+    std::shared_ptr<ov::Model> modelRef;
+    {
+        auto parameter = std::make_shared<ov::opset1::Parameter>(ov::element::u4, ov::Shape{});
+        parameter->set_friendly_name("A");
+        auto constant = std::make_shared<ov::opset1::Constant>(t);
+        constant->set_friendly_name("my_const");
+        auto transpose = std::make_shared<ov::opset1::Add>(parameter, constant);
+        transpose->set_friendly_name("Add_4");
+        auto result = std::make_shared<ov::opset1::Result>(transpose);
+        result->set_friendly_name("Result_5");
+        modelRef = std::make_shared<ov::Model>(ov::NodeVector{result}, ov::ParameterVector{parameter});
+    }
+
+    const auto fc = FunctionsComparator::with_default()
+                        .enable(FunctionsComparator::ATTRIBUTES)
+                        .enable(FunctionsComparator::PRECISIONS)
+                        .enable(FunctionsComparator::RUNTIME_KEYS)
+                        .enable(FunctionsComparator::NAMES)
+                        .enable(FunctionsComparator::CONST_VALUES);
+    const auto res = fc.compare(model, modelRef);
+    EXPECT_TRUE(res.valid) << res.message;
+
+    for (auto&& op : model->get_ops()) {
+        if (auto c = ov::as_type<ov::op::v0::Constant>(op.get())) {
+            const auto v = c->get_vector<uint8_t>();
+            EXPECT_EQ(v[0], 0x08);
+        }
+    }
+}
+
 INSTANTIATE_TEST_SUITE_P(EnableMMapPropery, IRFrontendMMapTests, ::testing::Bool());
 
 TEST_F(IRFrontendTests, model_without_weights_reading_from_disk) {

From f081f099630ef9282efe87ad881b59e47ed932d0 Mon Sep 17 00:00:00 2001
From: Alina Kladieva <alina.kladieva@intel.com>
Date: Wed, 18 Sep 2024 14:55:57 +0200
Subject: [PATCH 02/49] [GHA] Actualize docker docs & add section about
 reproducibility (#26597)

Co-authored-by: Andrei Kashchikhin <andrey.kashchikhin@intel.com>
---
 docs/dev/ci/github_actions/docker_images.md | 140 ++++++++++++++++----
 1 file changed, 117 insertions(+), 23 deletions(-)

diff --git a/docs/dev/ci/github_actions/docker_images.md b/docs/dev/ci/github_actions/docker_images.md
index ee2ddcc74845f8..5bc51c3b85b3a8 100644
--- a/docs/dev/ci/github_actions/docker_images.md
+++ b/docs/dev/ci/github_actions/docker_images.md
@@ -44,39 +44,133 @@ Here are some pros and cons of having your own container registry:
     * The registry does not automatically mirror images available on Docker Hub
     * The necessary images must be manually added to the registry
 
-The available Docker images use the following pattern for their names: `openvinogithubactions.azurecr.io/dockerhub/<image-name>:<image-tag>`.
+The available Docker images use the following pattern for their names: `openvinogithubactions.azurecr.io/<image-name>:<image-tag>` or `openvinogithubactions.azurecr.io/library/<image-name>:<image-tag>`
 
-Most of the images on the OpenVINO ACR are mirrors of the images with the same names on Docker Hub.
+Most of the images on the OpenVINO ACR are either mirrors of the images from Docker Hub or based on them.
 
 Examples:
-* `openvinogithubactions.azurecr.io/dockerhub/ubuntu:20.04` corresponds to `ubuntu:20.04` from Docker Hub
-* `openvinogithubactions.azurecr.io/dockerhub/ubuntu:22.04` corresponds to `ubuntu:22.04` from Docker Hub
-* `openvinogithubactions.azurecr.io/dockerhub/nvidia/cuda:11.8.0-runtime-ubuntu20.04` corresponds to `nvidia/cuda:11.8.0-runtime-ubuntu20.04` from Docker Hub
+* `openvinogithubactions.azurecr.io/library/ubuntu:22.04` corresponds to `ubuntu:22.04` from Docker Hub
+* `openvinogithubactions.azurecr.io/nvidia/cuda:11.8.0-runtime-ubuntu20.04` corresponds to `nvidia/cuda:11.8.0-runtime-ubuntu20.04` from Docker Hub
+
+Image mirroring is performed automatically via [ACR artifact cache rules](https://learn.microsoft.com/en-us/azure/container-registry/container-registry-artifact-cache),
+so no manual upload from a public DockerHub to OpenVINO ACR instance is required. 
+
+## Custom docker images and handle_docker action
+
+To optimize the time required to install dependencies in workflows and to make local reproduction of workflow steps easier, 
+we create custom Docker images for different types of validation and use them in our workflows. 
+The dockerfiles for these images are stored in the OpenVINO repository in [`.github/dockerfiles`](./../../../../.github/dockerfiles) 
+folder. Dockerfiles are organized as follows:
+
+* [`.github/dockerfiles/ov_build`](./../../../../.github/dockerfiles/ov_build) - contains environment setup for 
+build workflows for different OSes (e.g., installation of [install_build_dependencies.sh](./../../../../install_build_dependencies.sh))
+* [`.github/dockerfiles/ov_test`](./../../../../.github/dockerfiles/ov_test) - contains environment setup for 
+test workflows for different OSes (e.g., installation of [scripts/install_dependencies/install_openvino_dependencies.sh](./../../../../scripts/install_dependencies/install_openvino_dependencies.sh))
+
+The changes to these dockerfiles are getting checked and applied automatically in pre-commits via a custom action
+[handle_docker](./../../../../.github/actions/handle_docker), which is executed in workflows before starting actual 
+validation in a separate job called Docker. The action checks if a pull request changes either dockerfiles or 
+environment setup scripts, and if so - triggers affected docker images build and validation with the updated images.
+The images are tagged with the ID of the pull request that changes them, and the tag must be updated in git by changing 
+PR ID in [.github/dockerfiles/docker_tag](./../../../../.github/dockerfiles/docker_tag). The action will prompt you 
+to do that once you change something that alters docker environment.
+
+**Important**: If you add a new environment configuration script to be used in dockerfiles, please, add its path under 
+`category: docker_env` key in [.github/labeler.yml](./../../../../.github/labeler.yml) (this will 
+ensure that the changes to this script are detected by handle_docker action and applied automatically), and exclude 
+the path to this script from [.dockerignore](./../../../../.dockerignore) (this will make sure that Docker itself 
+detects a script file).
+
+The action accepts a list of the desired images to build as an input and outputs fully-qualified Docker image references
+to use in workflow jobs.
+
+### Using custom images in workflow jobs
+
+* Make sure that Docker job is called in your workflow. Pass a path or multiple paths to the folders with dockerfiles, 
+that are going to be used further in a workflow, to `images` parameter of the `handle_docker` action. Example:
+```yaml
+  Docker:
+    needs: Smart_CI
+    runs-on: aks-linux-4-cores-16gb-docker-build
+    container:
+      image: openvinogithubactions.azurecr.io/docker_build:0.2
+      volumes:
+        - /mount:/mount
+    outputs:
+      images: "${{ steps.handle_docker.outputs.images }}"
+    steps:
+      - name: Checkout
+        uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7
+
+      - uses: ./.github/actions/handle_docker
+        id: handle_docker
+        with:
+          images: |
+            ov_build/ubuntu_22_04_x64
+            ov_build/ubuntu_22_04_x64_nvidia
+            ov_test/ubuntu_22_04_x64
+          registry: 'openvinogithubactions.azurecr.io'
+          dockerfiles_root_dir: '.github/dockerfiles'
+          changed_components: ${{ needs.smart_ci.outputs.changed_components }}
+```
+* Add "Docker" to the `needs:` block of the job that will be executed with the desired custom image and set 
+`container.image` key in this job to point to the docker image taken from `handle_docker`'s outputs, like that: 
+```yaml
+  Build:
+    needs: [Smart_CI, Docker]
+    ...
+    runs-on: aks-linux-16-cores-32gb
+    container:
+      image: ${{ fromJSON(needs.docker.outputs.images).ov_build.ubuntu_22_04_x64 }}
+```
+**Note**, that Azure-based runner (aks-***) is required to use custom Docker images, since the access to our 
+Azure Container Registry is enabled only from this type of runners.
+
 
 ## How to Choose an Image
 
-The Docker image choice depends on the nature of the job and the configuration being tested.
+The Docker image choice depends on the nature of the job and the configuration being tested. Please, review the images 
+in [.github/dockerfiles](./../../../../.github/dockerfiles). Typically for jobs that require compilation you'll
+choose **ov_build** images, and **ov_test** for others, as described in the previous section.
 
-An example `Build` job from the [`ubuntu_22.yml`](./../../../../.github/workflows/ubuntu_22.yml) workflow:
+If no existing images suit your needs, and you need a separate Docker image with their own dependencies installed, 
+feel free to add a new dockerfile under `.github/dockerfiles/<validation_type>/<platform>` folder and refer to it 
+in a workflow as described above.
+
+If you don't need any system dependencies to be installed in your pipeline, it might be enough to use a plain
+image from DockerHub. In this case, refer to the mirror of this DockerHub image in our 
+private Azure Container Registry. For example, if you need to use the `ubuntu:22.04` image, which you locally pull like that:
+```
+docker pull ubuntu:22.04
+...
+Status: Downloaded newer image for ubuntu:22.04
+docker.io/library/ubuntu:22.04
+```
+Note the last line starting with `docker.io`, replace `docker.io` with `openvinogithubactions.azurecr.io` and use the 
+resulting image reference in your workflow:
 ```yaml
-Build:
-  ...
-  container:
-    image: openvinogithubactions.azurecr.io/dockerhub/ubuntu:20.04
-    volumes:
-      - /mount:/mount
-  ...
+  SomeJob:
+    ...
+    container:
+      image: openvinogithubactions.azurecr.io/library/ubuntu:22.04
 ```
+The image will be mirrored from DockerHub to our Azure Container Registry automatically.
 
-The `openvinogithubactions.azurecr.io/dockerhub/ubuntu:20.04` Docker image is used for this job
-and **for other jobs in the workflow**.
-Usually, if one Docker image is used for the building job, other jobs use the same image for testing.
 
-If the tests do not require any specific OS or distribution, it is recommended to use
-the available images, for example, `openvinogithubactions.azurecr.io/dockerhub/ubuntu:20.04`.
+## Local reproducibility
 
-If testing requires a specific OS or distribution (for example, `fedora`),
-the Docker image for this flavor should be uploaded to the OpenVINO ACR first and
-then used in a workflow.
+Custom docker images can also be built locally to reproduce CI results in the same environment:
+1. Install [Docker](https://docs.docker.com/engine/install) on your local machine (Linux is the best option).
+2. If you're running behind proxy on a host where you run a docker build, 
+[add proxy](https://docs.docker.com/engine/cli/proxy/#configure-the-docker-client) to your `~/.docker/config.json`.
+3. Clone openvino repo, go to the root of the repository, and execute `docker build` for a desired image, for example:
+```
+cd _git/openvino
+docker build -f .github/dockerfiles/ov_test/ubuntu_22_04_x64/Dockerfile -t my_local_ov_test_build .
+```
+4. To run an interactive shell session within a built image:
+```
+docker run --entrypoint bash -it my_local_ov_test_build
+```
 
-Contact a member of the CI team for assistance with the image uploading.
+Please, contact a member of the CI team for any assistance regarding Docker environment.

From 6570afa030674e014a664d7f773d0bb2265abced Mon Sep 17 00:00:00 2001
From: "Anastasiya(Asya) Pronina" <anastasiya.pronina@intel.com>
Date: Wed, 18 Sep 2024 16:54:25 +0200
Subject: [PATCH 03/49] [NPUW]: Adding functional tests for NPUW with
 partitioning (#25997)

### Details:
- *This PR adds functional tests for NPUW launched with online
partitioning, mostly the same tests that were added for unpartitioned
NPUW, except for some interesting ones for folding and pipelining*
- *This PR also introduces 1 accuracy test, that, however, checked on
simple (in terms of computations, not structure) model for now*

### Tickets:
 - *ticket-id*
---
 .../behavior/npuw/behavior_tests.cpp          | 667 ++++++++++++++----
 .../behavior/npuw/behavior_tests.hpp          |  59 +-
 .../behavior/npuw/comparators/nrmse.cpp       | 137 ++++
 .../behavior/npuw/comparators/nrmse.hpp       |  27 +
 .../npuw/generators/model_generator.cpp       | 180 +++++
 .../npuw/generators/model_generator.hpp       |  25 +
 .../behavior/npuw/mocks/mock_plugins.cpp      |  54 +-
 .../behavior/npuw/mocks/mock_plugins.hpp      |   9 +-
 8 files changed, 996 insertions(+), 162 deletions(-)
 create mode 100644 src/plugins/intel_npu/tests/functional/behavior/npuw/comparators/nrmse.cpp
 create mode 100644 src/plugins/intel_npu/tests/functional/behavior/npuw/comparators/nrmse.hpp
 create mode 100644 src/plugins/intel_npu/tests/functional/behavior/npuw/generators/model_generator.cpp
 create mode 100644 src/plugins/intel_npu/tests/functional/behavior/npuw/generators/model_generator.hpp

diff --git a/src/plugins/intel_npu/tests/functional/behavior/npuw/behavior_tests.cpp b/src/plugins/intel_npu/tests/functional/behavior/npuw/behavior_tests.cpp
index 3471d832d3bf36..31d83e4f0d21d5 100644
--- a/src/plugins/intel_npu/tests/functional/behavior/npuw/behavior_tests.cpp
+++ b/src/plugins/intel_npu/tests/functional/behavior/npuw/behavior_tests.cpp
@@ -3,36 +3,58 @@
 //
 
 #include "behavior_tests.hpp"
-#include "openvino/opsets/opset11.hpp"
-#include "openvino/runtime/exec_model_info.hpp"
-#include "openvino/runtime/internal_properties.hpp"
-#include "openvino/runtime/make_tensor.hpp"
-#include "openvino/runtime/properties.hpp"
-#include "openvino/util/file_util.hpp"
+#include "comparators/nrmse.hpp"
 #include "openvino/util/shared_object.hpp"
-#include "npuw_private_properties.hpp"
+#include "openvino/util/file_util.hpp"
+
+#include <filesystem>
 
 using namespace testing;
 using namespace ov::npuw::tests;
+using namespace ov::intel_npu::npuw;
 
-std::shared_ptr<ov::Model> BehaviorTestsNPUW::create_example_model() {
-    auto param = std::make_shared<ov::opset11::Parameter>(ov::element::i64, ov::PartialShape{1, 3, 2, 2});
-    param->set_friendly_name("input");
-    auto const_value = ov::opset11::Constant::create(ov::element::i64, ov::Shape{1, 1, 1, 1}, {1});
-    const_value->set_friendly_name("const_val");
-    auto add = std::make_shared<ov::opset11::Add>(param, const_value);
-    add->set_friendly_name("add");
-    auto result = std::make_shared<ov::opset11::Result>(add);
-    result->set_friendly_name("res");
-    return std::make_shared<ov::Model>(ov::ResultVector{result}, ov::ParameterVector{param});
-}
+#define TIMES(times) times 
+
+#define THROW(...) \
+    .WillOnce(Throw(std::runtime_error(__VA_ARGS__)))
+
+#define EXPECT_COMPILE_MODEL(device, times, ...)                        \
+    EXPECT_CALL(*device##_plugin,                                       \
+        compile_model(A<const std::shared_ptr<const ov::Model>&>(), _)) \
+        .Times(times)                                                   \
+        __VA_ARGS__                                                     \
+
+#define MODEL(idx) idx
+
+#define INFER_REQ(idx) idx
+
+#define EXPECT_CREATE_SYNC_INFER_REQ(device, model_idx, times, ...)                            \
+    device##_plugin->set_expectations_to_comp_models(model_idx, [](MockCompiledModel& model) { \
+        EXPECT_CALL(model, create_sync_infer_request())                                        \
+        .Times(times)                                                                          \
+        __VA_ARGS__;                                                                           \
+    })
+
+#define EXPECT_INFER(device, model_idx, times, ...) \
+    device##_plugin->set_expectations_to_infer_reqs(model_idx, 0, [](MockInferRequest& request) { \
+        EXPECT_CALL(request, infer())                                                             \
+        .Times(times)                                                                             \
+        __VA_ARGS__;                                                                              \
+    });
+
+#define EXPECT_INFER_FOR(device, model_idx, req_idx, times, ...) \
+    device##_plugin->set_expectations_to_infer_reqs(model_idx, req_idx, [](MockInferRequest& request) { \
+        EXPECT_CALL(request, infer())                                                                   \
+        .Times(times)                                                                                   \
+        __VA_ARGS__;                                                                                    \
+    });
 
 TEST_F(BehaviorTestsNPUW, TestInfrastructureIsCorrect) {
     // Set expectations first:
-    EXPECT_CALL(*npu_plugin, get_property).Times(AnyNumber());
-    EXPECT_CALL(*npu_plugin, get_property(std::string("AVAILABLE_DEVICES"), _)).Times(1);
-    EXPECT_CALL(*cpu_plugin, get_property).Times(AnyNumber());
-    EXPECT_CALL(*cpu_plugin, get_property(std::string("AVAILABLE_DEVICES"), _)).Times(1);
+    EXPECT_CALL(*mock_npu_plugin, get_property).Times(AnyNumber());
+    EXPECT_CALL(*mock_npu_plugin, get_property(std::string("AVAILABLE_DEVICES"), _)).Times(1);
+    EXPECT_CALL(*mock_cpu_plugin, get_property).Times(AnyNumber());
+    EXPECT_CALL(*mock_cpu_plugin, get_property(std::string("AVAILABLE_DEVICES"), _)).Times(1);
 
     // Register mock objects as plugins in OpenVINO:
     register_mock_plugins_in_ov();
@@ -53,209 +75,602 @@ TEST_F(BehaviorTestsNPUW, TestInfrastructureIsCorrect) {
 }
 
 TEST_F(BehaviorTestsNPUW, CompilationIsSuccessful) {
+    model = model_generator.get_model_with_one_op();
+
     // Set expectations first:
-    EXPECT_CALL(*npu_plugin,
-        compile_model(A<const std::shared_ptr<const ov::Model>&>(), _)).Times(1);
-    EXPECT_CALL(*cpu_plugin,
-        compile_model(A<const std::shared_ptr<const ov::Model>&>(), _)).Times(0);
+    EXPECT_COMPILE_MODEL(mock_npu, TIMES(1));
+    EXPECT_COMPILE_MODEL(mock_cpu, TIMES(0));
 
     // Register mock objects as plugins in OpenVINO:
     register_mock_plugins_in_ov();
 
     // Do the actual test:
-    auto npuw_properties = 
-        ov::AnyMap{ov::intel_npu::use_npuw(true),
-                   ov::intel_npu::npuw::devices("MockNPU")};
-    EXPECT_NO_THROW(core.compile_model(model, "NPU", npuw_properties));
+    use_npuw_props.emplace(devices("MockNPU"));
+    EXPECT_NO_THROW(core.compile_model(model, "NPU", use_npuw_props));
 }
 
 TEST_F(BehaviorTestsNPUW, CompilationIsFailSafe) {
+    model = model_generator.get_model_with_one_op();
+
     // Set expectations first:
     {
         InSequence s;
 
-        EXPECT_CALL(*npu_plugin, compile_model(A<const std::shared_ptr<const ov::Model>&>(), _))
-                .Times(1)
-                .WillOnce(Throw(std::runtime_error("Compilation on MockNPU is failed")));
-        EXPECT_CALL(*cpu_plugin, compile_model(A<const std::shared_ptr<const ov::Model>&>(), _)).Times(1);
+        EXPECT_COMPILE_MODEL(mock_npu, TIMES(1), THROW("Compilation on MockNPU is failed"));
+        EXPECT_COMPILE_MODEL(mock_cpu, TIMES(1));
     }
 
     // Register mock objects as plugins in OpenVINO:
     register_mock_plugins_in_ov();
 
     // Do the actual test:
-    auto npuw_properties = 
-        ov::AnyMap{ov::intel_npu::use_npuw(true),
-                   ov::intel_npu::npuw::devices("MockNPU,MockCPU")};
-    EXPECT_NO_THROW(core.compile_model(model, "NPU", npuw_properties));
+    use_npuw_props.emplace(devices("MockNPU,MockCPU"));
+    EXPECT_NO_THROW(core.compile_model(model, "NPU", use_npuw_props));
 }
 
 TEST_F(BehaviorTestsNPUW, CompilationIsFailed) {
+    model = model_generator.get_model_with_one_op();
+
     // Set expectations first:
-    EXPECT_CALL(*npu_plugin, compile_model(A<const std::shared_ptr<const ov::Model>&>(), _))
-            .Times(1)
-            .WillOnce(Throw(std::runtime_error("Compilation on MockNPU is failed")));
-    EXPECT_CALL(*cpu_plugin, compile_model(A<const std::shared_ptr<const ov::Model>&>(), _)).Times(0);
+    EXPECT_COMPILE_MODEL(mock_npu, TIMES(1), THROW("Compilation on MockNPU is failed"));
+    EXPECT_COMPILE_MODEL(mock_cpu, TIMES(0));
 
     // Register mock objects as plugins in OpenVINO:
     register_mock_plugins_in_ov();
 
     // Do the actual test:
-    auto npuw_properties = 
-        ov::AnyMap{ov::intel_npu::use_npuw(true),
-                   ov::intel_npu::npuw::devices("MockNPU")};
-    EXPECT_ANY_THROW(core.compile_model(model, "NPU", npuw_properties));
+    use_npuw_props.emplace(devices("MockNPU"));
+    EXPECT_ANY_THROW(core.compile_model(model, "NPU", use_npuw_props));
 }
 
 TEST_F(BehaviorTestsNPUW, InferRequestCreationIsSuccessful) {
+    model = model_generator.get_model_with_one_op();
+
     // Set expectations first:
-    EXPECT_CALL(*npu_plugin, compile_model(A<const std::shared_ptr<const ov::Model>&>(), _)).Times(1);
-    EXPECT_CALL(*cpu_plugin, compile_model(A<const std::shared_ptr<const ov::Model>&>(), _)).Times(0);
-    npu_plugin->set_expectations_to_comp_models(0, [](MockCompiledModel& model) {
-        EXPECT_CALL(model, create_sync_infer_request()).Times(1);
-    });
+    EXPECT_COMPILE_MODEL(mock_npu, TIMES(1));
+    EXPECT_COMPILE_MODEL(mock_cpu, TIMES(0));
+    EXPECT_CREATE_SYNC_INFER_REQ(mock_npu, MODEL(0), TIMES(1));
 
     // Register mock objects as plugins in OpenVINO:
     register_mock_plugins_in_ov();
 
     // Do the actual test:
-    auto npuw_properties = 
-        ov::AnyMap{ov::intel_npu::use_npuw(true),
-                   ov::intel_npu::npuw::devices("MockNPU")};
-    auto compiled_model = core.compile_model(model, "NPU", npuw_properties);
+    use_npuw_props.emplace(devices("MockNPU")); 
+    auto compiled_model = core.compile_model(model, "NPU", use_npuw_props);
     EXPECT_NO_THROW(compiled_model.create_infer_request());
 }
 
 TEST_F(BehaviorTestsNPUW, InferRequestCreationIsFailSafe) {
+    model = model_generator.get_model_with_one_op();
+
     // Set expectations first:
     {
         InSequence s;
-        EXPECT_CALL(*npu_plugin, compile_model(A<const std::shared_ptr<const ov::Model>&>(), _)).Times(1);
-        EXPECT_CALL(*cpu_plugin, compile_model(A<const std::shared_ptr<const ov::Model>&>(), _)).Times(1);
+        EXPECT_COMPILE_MODEL(mock_npu, TIMES(1));
+        EXPECT_COMPILE_MODEL(mock_cpu, TIMES(1));
     }
-    npu_plugin->set_expectations_to_comp_models(0, [](MockCompiledModel& model) {
-        EXPECT_CALL(model, create_sync_infer_request())
-            .Times(1)
-            .WillOnce(Throw(std::runtime_error("Infer request creation on MockNPU is failed")));
-    });
-    cpu_plugin->set_expectations_to_comp_models(0, [](MockCompiledModel& model) {
-        EXPECT_CALL(model, create_sync_infer_request()).Times(1);
-    });
+    EXPECT_CREATE_SYNC_INFER_REQ(mock_npu, MODEL(0), TIMES(1), THROW("Infer request creation on MockNPU is failed"));
+    EXPECT_CREATE_SYNC_INFER_REQ(mock_cpu, MODEL(0), TIMES(1));
 
     // Register mock objects as plugins in OpenVINO:
     register_mock_plugins_in_ov();
 
     // Do the actual test:
-    auto npuw_properties = 
-        ov::AnyMap{ov::intel_npu::use_npuw(true),
-                   ov::intel_npu::npuw::devices("MockNPU,MockCPU")};
-    auto compiled_model = core.compile_model(model, "NPU", npuw_properties);
+    use_npuw_props.emplace(devices("MockNPU,MockCPU"));
+    auto compiled_model = core.compile_model(model, "NPU", use_npuw_props);
     EXPECT_NO_THROW(compiled_model.create_infer_request());
 }
 
 TEST_F(BehaviorTestsNPUW, InferRequestCreationIsFailed) {
+    model = model_generator.get_model_with_one_op();
+
     // Set expectations first:
-    EXPECT_CALL(*npu_plugin, compile_model(A<const std::shared_ptr<const ov::Model>&>(), _)).Times(1);
-    EXPECT_CALL(*cpu_plugin, compile_model(A<const std::shared_ptr<const ov::Model>&>(), _)).Times(0);
+    EXPECT_COMPILE_MODEL(mock_npu, TIMES(1));
+    EXPECT_COMPILE_MODEL(mock_cpu, TIMES(0));
 
-    npu_plugin->set_expectations_to_comp_models(0, [](MockCompiledModel& model) {
-        EXPECT_CALL(model, create_sync_infer_request())
-                .Times(1)
-                .WillOnce(Throw(std::runtime_error("Infer request creation on MockNPU is failed")));
-    });
+    EXPECT_CREATE_SYNC_INFER_REQ(mock_npu, MODEL(0), TIMES(1), THROW("Infer request creation on MockNPU is failed"));
 
     // Register mock objects as plugins in OpenVINO:
     register_mock_plugins_in_ov();
 
     // Do the actual test:
-    auto npuw_properties = 
-        ov::AnyMap{ov::intel_npu::use_npuw(true),
-                   ov::intel_npu::npuw::devices("MockNPU")};
-    auto compiled_model = core.compile_model(model, "NPU", npuw_properties);
+    use_npuw_props.emplace(devices("MockNPU"));
+    auto compiled_model = core.compile_model(model, "NPU", use_npuw_props);
     EXPECT_ANY_THROW(compiled_model.create_infer_request());
 }
 
 TEST_F(BehaviorTestsNPUW, InferIsSuccessful) {
+    model = model_generator.get_model_with_one_op();
+
     // Set expectations first:
-    EXPECT_CALL(*npu_plugin, compile_model(A<const std::shared_ptr<const ov::Model>&>(), _)).Times(1);
-    EXPECT_CALL(*cpu_plugin, compile_model(A<const std::shared_ptr<const ov::Model>&>(), _)).Times(0);
-    npu_plugin->set_expectations_to_comp_models(0, [](MockCompiledModel& model) {
-        EXPECT_CALL(model, create_sync_infer_request()).Times(1);
-    });
-    npu_plugin->set_expectations_to_infer_reqs(0, [](MockInferRequest& request) {
-        EXPECT_CALL(request, infer()).Times(1);
-    });
+    EXPECT_COMPILE_MODEL(mock_npu, TIMES(1));
+    EXPECT_COMPILE_MODEL(mock_cpu, TIMES(0));
+    EXPECT_CREATE_SYNC_INFER_REQ(mock_npu, MODEL(0), TIMES(1));
+    EXPECT_INFER(mock_npu, MODEL(0), TIMES(1));
 
     // Register mock objects as plugins in OpenVINO:
     register_mock_plugins_in_ov();
 
     // Do the actual test:
-    auto npuw_properties = 
-        ov::AnyMap{ov::intel_npu::use_npuw(true),
-                   ov::intel_npu::npuw::devices("MockNPU")};
-    auto compiled_model = core.compile_model(model, "NPU", npuw_properties);
+    use_npuw_props.emplace(devices("MockNPU"));
+    auto compiled_model = core.compile_model(model, "NPU", use_npuw_props);
     auto infer_request = compiled_model.create_infer_request();
     EXPECT_NO_THROW(infer_request.infer());
 }
 
 TEST_F(BehaviorTestsNPUW, InferIsFailSafe) {
+    model = model_generator.get_model_with_one_op();
+
     // Set expectations first:
     {
         InSequence seq;
-        EXPECT_CALL(*npu_plugin, compile_model(A<const std::shared_ptr<const ov::Model>&>(), _)).Times(1);
-        EXPECT_CALL(*cpu_plugin, compile_model(A<const std::shared_ptr<const ov::Model>&>(), _)).Times(1);
+        EXPECT_COMPILE_MODEL(mock_npu, TIMES(1));
+        EXPECT_COMPILE_MODEL(mock_cpu, TIMES(1));
     }
-    npu_plugin->set_expectations_to_comp_models(0, [](MockCompiledModel& model) {
-        EXPECT_CALL(model, create_sync_infer_request()).Times(1);
-    });
-    npu_plugin->set_expectations_to_infer_reqs(0, [](MockInferRequest& request) {
-        EXPECT_CALL(request, infer())
-            .Times(1)
-            .WillOnce(Throw(std::runtime_error("Infer on MockNPU is failed")));
-    });   
-    cpu_plugin->set_expectations_to_comp_models(0, [](MockCompiledModel& model) {
-        EXPECT_CALL(model, create_sync_infer_request()).Times(1);
-    });
-    cpu_plugin->set_expectations_to_infer_reqs(0, [](MockInferRequest& request) {
-        EXPECT_CALL(request, infer()).Times(1);
-    });
+    EXPECT_CREATE_SYNC_INFER_REQ(mock_npu, MODEL(0), TIMES(1));
+    EXPECT_INFER(mock_npu, MODEL(0), TIMES(1), THROW("Infer on MockNPU is failed"));
+    EXPECT_CREATE_SYNC_INFER_REQ(mock_cpu, MODEL(0), TIMES(1));
+    EXPECT_INFER(mock_cpu, MODEL(0), TIMES(1));
 
     // Register mock objects as plugins in OpenVINO:
     register_mock_plugins_in_ov();
 
     // Do the actual test:
-    auto npuw_properties = 
-        ov::AnyMap{ov::intel_npu::use_npuw(true),
-                   ov::intel_npu::npuw::devices("MockNPU,MockCPU")};
-    auto compiled_model = core.compile_model(model, "NPU", npuw_properties);
+    use_npuw_props.emplace(devices("MockNPU,MockCPU"));
+    auto compiled_model = core.compile_model(model, "NPU", use_npuw_props);
     auto infer_request = compiled_model.create_infer_request();
     EXPECT_NO_THROW(infer_request.infer());
 }
 
 TEST_F(BehaviorTestsNPUW, InferIsFailed) {
+    model = model_generator.get_model_with_one_op();
+
+    // Set expectations first:
+    EXPECT_COMPILE_MODEL(mock_npu, TIMES(1));
+    EXPECT_COMPILE_MODEL(mock_cpu, TIMES(0));
+
+    EXPECT_CREATE_SYNC_INFER_REQ(mock_npu, MODEL(0), TIMES(1));
+    EXPECT_INFER(mock_npu, MODEL(0), TIMES(1), THROW("Infer on MockNPU is failed"));
+
+    // Register mock objects as plugins in OpenVINO:
+    register_mock_plugins_in_ov();
+
+    // Do the actual test:
+    use_npuw_props.emplace(devices("MockNPU"));
+    auto compiled_model = core.compile_model(model, "NPU", use_npuw_props);
+    auto infer_request = compiled_model.create_infer_request();
+    EXPECT_ANY_THROW(infer_request.infer());
+}
+
+using BehaviorTestsNPUWOnlinePartitioning = BehaviorTestsNPUW;
+TEST_F(BehaviorTestsNPUWOnlinePartitioning, CompilationIsSuccessful) {
+    model = model_generator.get_model_with_repeated_blocks();
+
+    // Set expectations first:
+    EXPECT_COMPILE_MODEL(mock_npu, TIMES(12));
+    EXPECT_COMPILE_MODEL(mock_cpu, TIMES(0));
+
+    // Register mock objects as plugins in OpenVINO:
+    register_mock_plugins_in_ov();
+
+    // Do the actual test:
+    use_npuw_props.emplace(devices("MockNPU"));    
+    use_npuw_props.emplace(partitioning::online::min_size(12));
+    EXPECT_NO_THROW(core.compile_model(model, "NPU", use_npuw_props));
+}
+
+TEST_F(BehaviorTestsNPUWOnlinePartitioning, CompilationIsFailSafe) {
+    model = model_generator.get_model_with_repeated_blocks();
+    // Set expectations first:
+    {
+        InSequence s;
+
+        EXPECT_COMPILE_MODEL(mock_npu, TIMES(1), THROW("Compilation on MockNPU is failed"));
+        EXPECT_COMPILE_MODEL(mock_cpu, TIMES(1));
+        EXPECT_COMPILE_MODEL(mock_npu, TIMES(11));
+        
+    }
+
+    // Register mock objects as plugins in OpenVINO:
+    register_mock_plugins_in_ov();
+
+    // Do the actual test:
+    use_npuw_props.emplace(devices("MockNPU,MockCPU"));
+    use_npuw_props.emplace(partitioning::online::min_size(12));
+    EXPECT_NO_THROW(core.compile_model(model, "NPU", use_npuw_props));
+}
+
+TEST_F(BehaviorTestsNPUWOnlinePartitioning, CompilationIsFailed) {
+    model = model_generator.get_model_with_repeated_blocks();
+
+    // Set expectations first:
+    EXPECT_COMPILE_MODEL(mock_npu, TIMES(1), THROW("Compilation on MockNPU is failed"));
+    EXPECT_COMPILE_MODEL(mock_cpu, TIMES(0));    
+
+    // Register mock objects as plugins in OpenVINO:
+    register_mock_plugins_in_ov();
+
+    // Do the actual test:
+    use_npuw_props.emplace(devices("MockNPU"));
+    use_npuw_props.emplace(partitioning::online::min_size(12));
+    EXPECT_ANY_THROW(core.compile_model(model, "NPU", use_npuw_props));
+}
+
+TEST_F(BehaviorTestsNPUWOnlinePartitioning, InferRequestCreationIsSuccessful) {
+    model = model_generator.get_model_with_repeated_blocks();
+
+    // Set expectations first:
+    EXPECT_COMPILE_MODEL(mock_npu, TIMES(12));
+    EXPECT_COMPILE_MODEL(mock_cpu, TIMES(0));
+
+    for (int i = 0;  i < 12; i++) {
+        EXPECT_CREATE_SYNC_INFER_REQ(mock_npu, MODEL(i), TIMES(1));
+    }
+
+    // Register mock objects as plugins in OpenVINO:
+    register_mock_plugins_in_ov();
+
+    // Do the actual test:
+    use_npuw_props.emplace(devices("MockNPU"));
+    use_npuw_props.emplace(partitioning::online::min_size(12));
+    auto compiled_model = core.compile_model(model, "NPU", use_npuw_props);
+    EXPECT_NO_THROW(compiled_model.create_infer_request());
+}
+
+TEST_F(BehaviorTestsNPUWOnlinePartitioning, InferRequestCreationIsFailSafe) {
+    model = model_generator.get_model_with_repeated_blocks();
+    // Set expectations first:
+    {
+        InSequence s;
+
+        EXPECT_COMPILE_MODEL(mock_npu, TIMES(12));
+        EXPECT_COMPILE_MODEL(mock_cpu, TIMES(1));
+    }
+
+    for (int i = 0;  i < 11; i++) {
+        EXPECT_CREATE_SYNC_INFER_REQ(mock_npu, MODEL(i), TIMES(1));
+    }
+    EXPECT_CREATE_SYNC_INFER_REQ(mock_npu, MODEL(11), TIMES(1), THROW("Infer request creation on MockNPU is failed"));
+    EXPECT_CREATE_SYNC_INFER_REQ(mock_cpu, MODEL(0), TIMES(1));
+
+    // Register mock objects as plugins in OpenVINO:
+    register_mock_plugins_in_ov();
+
+    // Do the actual test:
+    use_npuw_props.emplace(devices("MockNPU,MockCPU"));
+    use_npuw_props.emplace(partitioning::online::min_size(12));
+    auto compiled_model = core.compile_model(model, "NPU", use_npuw_props);
+    EXPECT_NO_THROW(compiled_model.create_infer_request());
+}
+
+TEST_F(BehaviorTestsNPUWOnlinePartitioning, InferRequestCreationIsFailed) {
+    model = model_generator.get_model_with_repeated_blocks();
+
+    // Set expectations first:
+    EXPECT_COMPILE_MODEL(mock_npu, TIMES(12));
+    EXPECT_COMPILE_MODEL(mock_cpu, TIMES(0));
+
+    for (int i = 0;  i < 11; i++) {
+        EXPECT_CREATE_SYNC_INFER_REQ(mock_npu, MODEL(i), TIMES(1));
+    }
+    EXPECT_CREATE_SYNC_INFER_REQ(mock_npu, MODEL(11), TIMES(1), THROW("Infer request creation on MockNPU is failed"));
+
+    // Register mock objects as plugins in OpenVINO:
+    register_mock_plugins_in_ov();
+
+    // Do the actual test:
+    use_npuw_props.emplace(devices("MockNPU"));
+    use_npuw_props.emplace(partitioning::online::min_size(12));
+    auto compiled_model = core.compile_model(model, "NPU", use_npuw_props);
+    EXPECT_ANY_THROW(compiled_model.create_infer_request());
+}
+
+TEST_F(BehaviorTestsNPUWOnlinePartitioning, InferIsSuccessful) {
+    model = model_generator.get_model_with_repeated_blocks();
+
+    // Set expectations first:
+    EXPECT_COMPILE_MODEL(mock_npu, TIMES(12));
+    EXPECT_COMPILE_MODEL(mock_cpu, TIMES(0));
+
+    for (int i = 0;  i < 12; i++) {
+        EXPECT_CREATE_SYNC_INFER_REQ(mock_npu, MODEL(i), TIMES(1));
+        EXPECT_INFER(mock_npu, MODEL(i), TIMES(1));
+    }
+
+    // Register mock objects as plugins in OpenVINO:
+    register_mock_plugins_in_ov();
+
+    // Do the actual test:
+    use_npuw_props.emplace(devices("MockNPU"));
+    use_npuw_props.emplace(partitioning::online::min_size(12));
+    auto compiled_model = core.compile_model(model, "NPU", use_npuw_props);
+    auto infer_request = compiled_model.create_infer_request();
+    EXPECT_NO_THROW(infer_request.infer());
+}
+
+TEST_F(BehaviorTestsNPUWOnlinePartitioning, InferIsFailSafe) {
+    model = model_generator.get_model_with_repeated_blocks();
+
     // Set expectations first:
     {
         InSequence seq;
-        EXPECT_CALL(*npu_plugin, compile_model(A<const std::shared_ptr<const ov::Model>&>(), _)).Times(1);
-        EXPECT_CALL(*cpu_plugin, compile_model(A<const std::shared_ptr<const ov::Model>&>(), _)).Times(0);
+        EXPECT_COMPILE_MODEL(mock_npu, TIMES(12));
+        EXPECT_COMPILE_MODEL(mock_cpu, TIMES(1));
     }
-    npu_plugin->set_expectations_to_comp_models(0, [](MockCompiledModel& model) {
-        EXPECT_CALL(model, create_sync_infer_request()).Times(1);
-    });
-    npu_plugin->set_expectations_to_infer_reqs(0, [](MockInferRequest& request) {
-        EXPECT_CALL(request, infer())
-            .Times(1)
-            .WillOnce(Throw(std::runtime_error("Infer on MockNPU is failed")));
-    });
+    for (int i = 0;  i < 12; i++) {
+       EXPECT_CREATE_SYNC_INFER_REQ(mock_npu, MODEL(i), TIMES(1));
+    }
+    for (int i = 0;  i < 11; i++) {
+        EXPECT_INFER(mock_npu, MODEL(i), TIMES(1));
+    }
+    EXPECT_INFER(mock_npu, MODEL(11), TIMES(1), THROW("Infer on MockNPU is failed"));
+
+    EXPECT_CREATE_SYNC_INFER_REQ(mock_cpu, MODEL(0), TIMES(1));
+    EXPECT_INFER(mock_cpu, MODEL(0), TIMES(1));
+
+    // Register mock objects as plugins in OpenVINO:
+    register_mock_plugins_in_ov();
+
+    // Do the actual test:
+    use_npuw_props.emplace(devices("MockNPU,MockCPU"));
+    use_npuw_props.emplace(partitioning::online::min_size(12));
+    auto compiled_model = core.compile_model(model, "NPU", use_npuw_props);
+    auto infer_request = compiled_model.create_infer_request();
+    EXPECT_NO_THROW(infer_request.infer());
+}
+
+TEST_F(BehaviorTestsNPUWOnlinePartitioning, InferIsFailed) {
+    model = model_generator.get_model_with_repeated_blocks();
+
+    // Set expectations first:
+    {
+        InSequence seq;
+        EXPECT_COMPILE_MODEL(mock_npu, TIMES(12));
+        EXPECT_COMPILE_MODEL(mock_cpu, TIMES(0));
+    }
+    for (int i = 0;  i < 12; i++) {
+        EXPECT_CREATE_SYNC_INFER_REQ(mock_npu, MODEL(i), TIMES(1));
+    }
+    for (int i = 0;  i < 11; i++) {
+        EXPECT_INFER(mock_npu, MODEL(i), TIMES(1));
+    }
+    EXPECT_INFER(mock_npu, MODEL(11), TIMES(1), THROW("Infer on MockNPU is failed"));
 
     // Register mock objects as plugins in OpenVINO:
     register_mock_plugins_in_ov();
 
     // Do the actual test:
-    auto npuw_properties = 
-        ov::AnyMap{ov::intel_npu::use_npuw(true),
-                   ov::intel_npu::npuw::devices("MockNPU")};
-    auto compiled_model = core.compile_model(model, "NPU", npuw_properties);
+    use_npuw_props.emplace(devices("MockNPU"));
+    use_npuw_props.emplace(partitioning::online::min_size(12));
+    auto compiled_model = core.compile_model(model, "NPU", use_npuw_props);
     auto infer_request = compiled_model.create_infer_request();
     EXPECT_ANY_THROW(infer_request.infer());
 }
+
+TEST_F(BehaviorTestsNPUWOnlinePartitioning, RepeatedBlocksAreFolded) {
+    model = model_generator.get_model_with_repeated_blocks();
+
+    // Set expectations first:
+    {
+        InSequence seq;
+        EXPECT_COMPILE_MODEL(mock_npu, TIMES(3));
+        EXPECT_COMPILE_MODEL(mock_cpu, TIMES(0));
+    }
+    for (int i = 0;  i < 3; i++) {
+        EXPECT_CREATE_SYNC_INFER_REQ(mock_npu, MODEL(i), TIMES(1));
+    }
+
+    // 1st model infer request is called once -- head
+    EXPECT_INFER(mock_npu, MODEL(0), TIMES(1));
+    // 2nd model infer request is called 10 times -- repeated block
+    EXPECT_INFER(mock_npu, MODEL(1), TIMES(10));
+    // 3rd model infer request is called once -- tail
+    EXPECT_INFER(mock_npu, MODEL(2), TIMES(1));
+
+    // Register mock objects as plugins in OpenVINO:
+    register_mock_plugins_in_ov();
+
+    // Do the actual test:
+    use_npuw_props.emplace(devices("MockNPU"));
+    use_npuw_props.emplace(partitioning::online::min_size(12));
+    use_npuw_props.emplace(partitioning::fold(true));
+    auto compiled_model = core.compile_model(model, "NPU", use_npuw_props);
+    auto infer_request = compiled_model.create_infer_request();
+    EXPECT_NO_THROW(infer_request.infer());
+}
+
+TEST_F(BehaviorTestsNPUWOnlinePartitioning, FoldingAndPipelining) {
+    model = model_generator.get_model_with_repeated_blocks();
+
+    // Set expectations first:
+    {
+        InSequence seq;
+        EXPECT_COMPILE_MODEL(mock_npu, TIMES(3));
+        EXPECT_COMPILE_MODEL(mock_cpu, TIMES(0));
+    }
+
+    for (int i = 0;  i < 3; i++) {
+        // Here we will create 2 infer requests per model,
+        // so `create_sync_infer_request()` should be called twice
+        // per model:
+        EXPECT_CREATE_SYNC_INFER_REQ(mock_npu, MODEL(i), TIMES(2));
+    }
+
+    // 1st model 1st infer request is called once -- head
+    EXPECT_INFER_FOR(mock_npu, MODEL(0), INFER_REQ(0), TIMES(1));
+    // 1st model 2nd infer request is never called,
+    // it is not a function and is not repeated
+    EXPECT_INFER_FOR(mock_npu, MODEL(0), INFER_REQ(1), TIMES(0)); 
+
+    // Repeated block
+    // 2nd model 1st infer request is called 5 times
+    EXPECT_INFER_FOR(mock_npu, MODEL(1), INFER_REQ(0), TIMES(5));
+    // 2nd model 2nd infer request (brother of 1st one) is called 5 times
+    EXPECT_INFER_FOR(mock_npu, MODEL(1), INFER_REQ(1), TIMES(5));
+
+    // 3rd model 1st infer request is called once -- tail
+    EXPECT_INFER_FOR(mock_npu, MODEL(2), INFER_REQ(0), TIMES(1));
+    // 3rd model 2nd infer request is never called,
+    // it is not a function and is not repeated
+    EXPECT_INFER_FOR(mock_npu, MODEL(2), INFER_REQ(1), TIMES(0));
+
+    // Register mock objects as plugins in OpenVINO:
+    register_mock_plugins_in_ov();
+
+    // Do the actual test:
+    use_npuw_props.emplace(devices("MockNPU"));
+    use_npuw_props.emplace(partitioning::online::min_size(12));
+    use_npuw_props.emplace(partitioning::fold(true));
+    use_npuw_props.emplace(partitioning::dcoff_type("f16"));
+    use_npuw_props.emplace(partitioning::dcoff_with_scale(true));  
+    use_npuw_props.emplace(funcall_async(true));  
+    auto compiled_model = core.compile_model(model, "NPU", use_npuw_props);
+    auto infer_request = compiled_model.create_infer_request();
+    EXPECT_NO_THROW(infer_request.infer());
+}
+
+using BehaviorTestsNPUWOfflinePartitioning = BehaviorTestsNPUW;
+TEST_F(BehaviorTestsNPUWOfflinePartitioning, CompilationIsSuccessful) {
+    model = model_generator.get_model_with_repeated_blocks();
+
+    // Set expectations first:
+    // For plan generation and execution (twice compiled):
+    EXPECT_COMPILE_MODEL(mock_npu, TIMES(24));
+    EXPECT_COMPILE_MODEL(mock_cpu, TIMES(0));
+
+    // Register mock objects as plugins in OpenVINO:
+    register_mock_plugins_in_ov();
+
+    // Generate plan:
+    use_npuw_props.emplace(devices("MockNPU"));
+    use_npuw_props.emplace(partitioning::online::dump_plan("test_partitioning.xml"));
+
+    EXPECT_NO_THROW(core.compile_model(model, "NPU", use_npuw_props));
+    EXPECT_TRUE(std::filesystem::exists("test_partitioning.xml"));
+
+    // Do the actual test:
+    ov::AnyMap offline_plan_props = { ::ov::intel_npu::use_npuw(true),
+                                      devices("MockNPU"),
+                                      partitioning::plan("test_partitioning.xml") };
+    ov::CompiledModel compiled_model;
+    EXPECT_NO_THROW(compiled_model = core.compile_model(model, "NPU", offline_plan_props));
+    EXPECT_EQ("test_partitioning.xml",
+        compiled_model.get_property(partitioning::plan.name()).as<std::string>());
+    EXPECT_TRUE(std::filesystem::remove("test_partitioning.xml"));
+}
+
+TEST_F(BehaviorTestsNPUWOfflinePartitioning, InferRequestCreationIsSuccessful) {
+    model = model_generator.get_model_with_repeated_blocks();
+
+    // Set expectations first:
+    // For plan generation and execution (twice compiled):
+    EXPECT_COMPILE_MODEL(mock_npu, TIMES(24));
+    EXPECT_COMPILE_MODEL(mock_cpu, TIMES(0));
+
+    for (int i = 0;  i < 12; i++) {
+        // First 12 models are compiled only to generate partitioning plan:
+        EXPECT_CREATE_SYNC_INFER_REQ(mock_npu, MODEL(i + 12), 1);
+    }
+
+    // Register mock objects as plugins in OpenVINO:
+    register_mock_plugins_in_ov();
+
+    // Generate plan:
+    use_npuw_props.emplace(devices("MockNPU"));
+    use_npuw_props.emplace(partitioning::online::dump_plan("test_partitioning.xml"));
+    use_npuw_props.emplace(partitioning::online::pipeline("REP"));
+
+    EXPECT_NO_THROW(core.compile_model(model, "NPU", use_npuw_props));
+    EXPECT_TRUE(std::filesystem::exists("test_partitioning.xml"));
+
+    // Do the actual test:
+    ov::AnyMap offline_plan_props = { ::ov::intel_npu::use_npuw(true),
+                                      devices("MockNPU"),
+                                      partitioning::plan("test_partitioning.xml") };
+    auto compiled_model = core.compile_model(model, "NPU", offline_plan_props);
+    EXPECT_NO_THROW(compiled_model.create_infer_request());
+    EXPECT_EQ("test_partitioning.xml",
+        compiled_model.get_property(partitioning::plan.name()).as<std::string>());
+    EXPECT_TRUE(std::filesystem::remove("test_partitioning.xml"));
+}
+
+TEST_F(BehaviorTestsNPUWOfflinePartitioning, InferIsSuccessful) {
+    model = model_generator.get_model_with_repeated_blocks();
+
+    // Set expectations first:
+    // For plan generation and execution (twice compiled):
+    EXPECT_COMPILE_MODEL(mock_npu, TIMES(24));
+    EXPECT_COMPILE_MODEL(mock_cpu, TIMES(0));
+
+    for (int i = 0;  i < 12; i++) {
+        // First 12 models are compiled only to generate partitioning plan:
+        EXPECT_CREATE_SYNC_INFER_REQ(mock_npu, MODEL(i + 12), 1);
+        EXPECT_INFER(mock_npu, MODEL(i + 12), 1);
+    }
+
+    // Register mock objects as plugins in OpenVINO:
+    register_mock_plugins_in_ov();
+
+    // Generate plan:
+    use_npuw_props.emplace(devices("MockNPU"));
+    use_npuw_props.emplace(partitioning::online::dump_plan("test_partitioning.xml"));
+    use_npuw_props.emplace(partitioning::online::pipeline("REP"));
+
+    EXPECT_NO_THROW(core.compile_model(model, "NPU", use_npuw_props));
+    EXPECT_TRUE(std::filesystem::exists("test_partitioning.xml"));
+
+    // Do the actual test:
+    ov::AnyMap offline_plan_props = { ::ov::intel_npu::use_npuw(true),
+                                      devices("MockNPU"),
+                                      partitioning::plan("test_partitioning.xml") };
+    auto compiled_model = core.compile_model(model, "NPU", offline_plan_props);
+    auto infer_request = compiled_model.create_infer_request();
+    EXPECT_NO_THROW(infer_request.infer());
+    EXPECT_EQ("test_partitioning.xml",
+        compiled_model.get_property(partitioning::plan.name()).as<std::string>());
+    EXPECT_TRUE(std::filesystem::remove("test_partitioning.xml"));  
+}
+
+TEST(BehaviorTestsNPUWAccuracy, RepAndNonePartPipesGiveSameResults) {
+    // Register TEMPLATE plugin in OpenVINO:
+    ov::Core core;
+    auto plugin_path =
+        ov::util::make_plugin_library_name(ov::test::utils::getExecutableDirectory(),
+                                           std::string(ov::test::utils::TEMPLATE_LIB)
+                                           + OV_BUILD_POSTFIX);
+    if (!ov::util::file_exists(plugin_path)) {
+        OPENVINO_THROW("Plugin: " + plugin_path + " does not exists!");
+    }
+    core.register_plugin(plugin_path, ov::test::utils::DEVICE_TEMPLATE);
+
+    // Create model:
+    ModelGenerator model_generator;
+    const auto &model = model_generator.get_model_with_repeated_blocks(32);
+
+    // Do the test:
+    ov::AnyMap online_rep_props { ::ov::intel_npu::use_npuw(true),
+                                  devices("TEMPLATE"),
+                                  partitioning::online::pipeline("REP") };
+    ov::AnyMap online_none_props { ::ov::intel_npu::use_npuw(true),
+                                   devices("TEMPLATE"),
+                                   partitioning::online::pipeline("NONE") };
+
+    auto rep_compiled_model = core.compile_model(model, "NPU", online_rep_props);
+    auto none_compiled_model = core.compile_model(model, "NPU", online_none_props);
+    auto rep_infer_request = rep_compiled_model.create_infer_request();
+    auto none_infer_request = rep_compiled_model.create_infer_request();
+
+    set_random_inputs<int32_t>(rep_infer_request);
+    set_random_inputs<int32_t>(none_infer_request);
+
+    rep_infer_request.infer();
+    none_infer_request.infer();
+
+    metrics::NRMSE nrmse(0.01);
+
+    for (const auto& output : rep_infer_request.get_compiled_model().outputs()) {
+        const auto& rep_tensor = rep_infer_request.get_tensor(output);
+        const auto& none_tensor = none_infer_request.get_tensor(output);
+        EXPECT_TRUE(nrmse(rep_tensor, none_tensor));
+    }
+}
diff --git a/src/plugins/intel_npu/tests/functional/behavior/npuw/behavior_tests.hpp b/src/plugins/intel_npu/tests/functional/behavior/npuw/behavior_tests.hpp
index eb2cce0673f1c0..85658ac4a42dd1 100644
--- a/src/plugins/intel_npu/tests/functional/behavior/npuw/behavior_tests.hpp
+++ b/src/plugins/intel_npu/tests/functional/behavior/npuw/behavior_tests.hpp
@@ -4,15 +4,18 @@
 #pragma once
 
 #include <memory>
+#include <random>
 
 #include <gtest/gtest.h>
 
 #include "openvino/runtime/core.hpp"
 #include "openvino/runtime/iplugin.hpp"
 #include "openvino/runtime/make_tensor.hpp"
+#include "npuw_private_properties.hpp"
 
 #include "mocks/mock_plugins.hpp"
 #include "mocks/register_in_ov.hpp"
+#include "generators/model_generator.hpp"
 
 namespace ov {
 namespace npuw {
@@ -21,31 +24,63 @@ namespace tests {
 class BehaviorTestsNPUW : public ::testing::Test {
 public:
     ov::Core core;
-    std::shared_ptr<MockNpuPlugin> npu_plugin;
-    std::shared_ptr<MockCpuPlugin> cpu_plugin;
-    std::shared_ptr<ov::Model> model;
+    std::shared_ptr<MockNpuPlugin> mock_npu_plugin;
+    std::shared_ptr<MockCpuPlugin> mock_cpu_plugin;
 
+    ov::AnyMap use_npuw_props;
+    std::shared_ptr<ov::Model> model;
+    ModelGenerator model_generator;
 
     void SetUp() override {
-        model = create_example_model();
-        npu_plugin = std::make_shared<MockNpuPlugin>();
-        npu_plugin->create_implementation();
-        cpu_plugin = std::make_shared<MockCpuPlugin>();
-        cpu_plugin->create_implementation();
+        mock_npu_plugin = std::make_shared<MockNpuPlugin>();
+        mock_npu_plugin->create_implementation();
+        mock_cpu_plugin = std::make_shared<MockCpuPlugin>();
+        mock_cpu_plugin->create_implementation();
+        use_npuw_props = ov::AnyMap{ov::intel_npu::use_npuw(true)};
     }
 
     // Make sure it is called after expectations are set!
     void register_mock_plugins_in_ov() {
-        m_shared_objects.push_back(reg_plugin<MockNpuPlugin>(core, npu_plugin));
-        m_shared_objects.push_back(reg_plugin<MockCpuPlugin>(core, cpu_plugin));
+        m_shared_objects.push_back(reg_plugin<MockNpuPlugin>(core, mock_npu_plugin));
+        m_shared_objects.push_back(reg_plugin<MockCpuPlugin>(core, mock_cpu_plugin));
     }
 
-    std::shared_ptr<ov::Model> create_example_model();
-
 private:
     std::vector<std::shared_ptr<void>> m_shared_objects;
 };
 
+template <typename T>
+using uniform_distribution_common = typename std::conditional<
+    std::is_floating_point<T>::value,
+    std::uniform_real_distribution<T>,
+    typename std::conditional<std::is_integral<T>::value, std::uniform_int_distribution<T>, void>::type>::type;
+template <typename T, typename T2>
+
+ov::Tensor make_random_tensor(ov::element::Type type,
+                              ov::Shape shape,
+                              T rand_min = std::numeric_limits<uint8_t>::min(),
+                              T rand_max = std::numeric_limits<uint8_t>::max()) {
+    size_t tensor_size =
+        std::accumulate(shape.begin(), shape.end(), std::size_t(1), std::multiplies<size_t>());
+    auto tensor = ov::Tensor(type, shape);
+    auto data = tensor.data<T>();
+
+    std::mt19937 gen(0);
+    uniform_distribution_common<T2> distribution(rand_min, rand_max);
+    for (size_t i = 0; i < tensor_size; i++) {
+        data[i] = static_cast<T>(distribution(gen));
+    }
+    return tensor;
+}
+
+template<typename T>
+void set_random_inputs(ov::InferRequest infer_request) {
+    for (const auto& input : infer_request.get_compiled_model().inputs()) {
+        // TODO: Extend template header to two types.
+        //       For float16 call should be make_random_tensor<ov::float16, float>
+        infer_request.set_tensor(input, make_random_tensor<T, T>(input.get_element_type(), input.get_shape()));
+    }
+}
 }  // namespace tests
 }  // namespace npuw
 }  // namespace ov
diff --git a/src/plugins/intel_npu/tests/functional/behavior/npuw/comparators/nrmse.cpp b/src/plugins/intel_npu/tests/functional/behavior/npuw/comparators/nrmse.cpp
new file mode 100644
index 00000000000000..925352669b6a18
--- /dev/null
+++ b/src/plugins/intel_npu/tests/functional/behavior/npuw/comparators/nrmse.cpp
@@ -0,0 +1,137 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "nrmse.hpp"
+
+#include <algorithm>
+#include <openvino/runtime/make_tensor.hpp>
+#include <openvino/core/parallel.hpp>
+
+namespace {
+template <typename InT>
+void to_f32(const ov::Tensor& in, ov::Tensor& out) {
+    OPENVINO_ASSERT(in.is_continuous());
+    OPENVINO_ASSERT(out.is_continuous());
+    OPENVINO_ASSERT(in.get_shape() == out.get_shape());
+
+    if (ov::element::Type_t::f32 == in.get_element_type()) {
+        in.copy_to(out);
+        return;
+    }
+
+    const InT* in_buffer = in.data<InT>();
+    OPENVINO_ASSERT(in_buffer != nullptr);
+    const auto out_buffer = out.data<float>();
+    OPENVINO_ASSERT(out_buffer != nullptr);
+
+    // NOTE: ov::parallel_for takes care of splitting the work among threads such way,
+    //       that the passed lambda function will be called sequentially
+    //       on some part of "in.get_size()" range inside the each thread
+    ov::parallel_for(in.get_size(), [in_buffer, out_buffer](int64_t index) {
+        out_buffer[index] = static_cast<float>(in_buffer[index]);
+    });
+}
+
+void to_f32(const ov::Tensor& in, ov::Tensor& out) {
+    switch (in.get_element_type()) {
+    case ov::element::Type_t::f32:
+        ::to_f32<float>(in, out);
+        break;
+    case ov::element::Type_t::u64:
+        ::to_f32<uint64_t>(in, out);
+        break;
+    case ov::element::Type_t::i64:
+        ::to_f32<int64_t>(in, out);
+        break;
+    case ov::element::Type_t::u32:
+        ::to_f32<uint32_t>(in, out);
+        break;
+    case ov::element::Type_t::i32:
+        ::to_f32<int32_t>(in, out);
+        break;
+    case ov::element::Type_t::u16:
+        ::to_f32<uint16_t>(in, out);
+        break;
+    case ov::element::Type_t::i16:
+        ::to_f32<int16_t>(in, out);
+        break;
+    case ov::element::Type_t::u8:
+        ::to_f32<uint8_t>(in, out);
+        break;
+    case ov::element::Type_t::i8:
+        ::to_f32<int8_t>(in, out);
+        break;
+    case ov::element::Type_t::f16:
+        ::to_f32<ov::float16>(in, out);
+        break;
+    case ov::element::Type_t::bf16:
+        ::to_f32<ov::bfloat16>(in, out);
+        break;
+    default:
+        OPENVINO_THROW("Unsupported precision {0}", in.get_element_type().get_type_name());
+        break;
+    }
+}
+} // anynomous namespace
+
+ov::npuw::tests::metrics::NRMSE::NRMSE(double threshold) : m_threshold(threshold) {}
+
+bool ov::npuw::tests::metrics::NRMSE::operator()(const ov::Tensor& actual,
+                                                 const ov::Tensor& reference) const {
+    OPENVINO_ASSERT(actual.is_continuous());
+    OPENVINO_ASSERT(reference.is_continuous());
+    OPENVINO_ASSERT(actual.get_shape() == reference.get_shape());
+    // Check for alignment:
+    OPENVINO_ASSERT(actual.get_byte_size() == reference.get_byte_size());
+    // FIXME: Check for strides
+
+    ov::Tensor actual_f32;
+    ov::Tensor reference_f32;
+
+    if (ov::element::Type_t::f32 == actual.get_element_type()) {
+        actual_f32 = actual;
+    } else {
+        ov::Tensor dst(ov::element::Type_t::f32, actual.get_shape());
+        to_f32(actual, dst);
+        actual_f32 = std::move(dst);
+    }
+
+    if (ov::element::Type_t::f32 == reference.get_element_type()) {
+        reference_f32 = reference;
+    } else {
+        ov::Tensor dst(ov::element::Type_t::f32, reference.get_shape());
+        to_f32(reference, dst);
+        reference_f32 = dst;
+    }
+
+    float* actual_data = actual_f32.data<float>();
+    float* reference_data = reference_f32.data<float>();
+    const std::size_t size = actual_f32.get_size();
+
+    double squared_error{};
+    for (size_t i = 0; i < size; ++i) {
+        double diff = (actual_data[i] - reference_data[i]);
+        squared_error += (diff * diff);
+    }
+
+    if (squared_error <= std::numeric_limits<double>::epsilon()) {
+        std::cout << "NRMSE loss: 0.0, threshold: " << m_threshold << ".\n";
+        return true;
+    }
+
+    double rmse = sqrt(squared_error / size);
+    OPENVINO_ASSERT(rmse >= 0.0);
+
+    auto actual_min_max = std::minmax_element(actual_data, actual_data + size);
+    auto reference_min_max = std::minmax_element(reference_data, reference_data + size);
+    double den = std::max({0.001f,
+                           std::max(0.f, *reference_min_max.second) - std::min(0.f, *reference_min_max.first),
+                           std::max(0.f, *actual_min_max.second) - std::min(0.f, *actual_min_max.first)});
+
+    double nrmse = rmse / den;
+    std::cout << "NRMSE loss: " << nrmse << ", threshold: " << m_threshold << ".\n";
+
+    bool success = nrmse <= m_threshold;
+    return success;
+}
diff --git a/src/plugins/intel_npu/tests/functional/behavior/npuw/comparators/nrmse.hpp b/src/plugins/intel_npu/tests/functional/behavior/npuw/comparators/nrmse.hpp
new file mode 100644
index 00000000000000..dcf59292c877fa
--- /dev/null
+++ b/src/plugins/intel_npu/tests/functional/behavior/npuw/comparators/nrmse.hpp
@@ -0,0 +1,27 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "openvino/openvino.hpp"
+#include "openvino/runtime/icompiled_model.hpp"
+#include "openvino/runtime/so_ptr.hpp"
+
+namespace ov {
+namespace npuw {
+namespace tests {
+namespace metrics {
+class NRMSE {
+public:
+    explicit NRMSE(double threshold);
+    bool operator()(const ov::Tensor& actual, const ov::Tensor& reference) const;
+
+private:
+    double m_threshold{};
+};
+
+}  // namespace metrics
+}  // namespace tests 
+}  // namespace npuw
+}  // namespace ov
diff --git a/src/plugins/intel_npu/tests/functional/behavior/npuw/generators/model_generator.cpp b/src/plugins/intel_npu/tests/functional/behavior/npuw/generators/model_generator.cpp
new file mode 100644
index 00000000000000..39f7f9c1569efe
--- /dev/null
+++ b/src/plugins/intel_npu/tests/functional/behavior/npuw/generators/model_generator.cpp
@@ -0,0 +1,180 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "model_generator.hpp"
+
+#include "openvino/openvino.hpp"
+#include "openvino/opsets/opset11.hpp"
+#include "openvino/op/ops.hpp"
+
+
+std::shared_ptr<ov::Model> ModelGenerator::get_model_with_one_op() {
+    auto param = std::make_shared<ov::opset11::Parameter>(ov::element::i64, ov::PartialShape{1, 3, 2, 2});
+    param->set_friendly_name("input");
+    auto const_value = ov::opset11::Constant::create(ov::element::i64, ov::Shape{1, 1, 1, 1}, {1});
+    const_value->set_friendly_name("const_val");
+    auto add = std::make_shared<ov::opset11::Add>(param, const_value);
+    add->set_friendly_name("add");
+    auto result = std::make_shared<ov::opset11::Result>(add);
+    result->set_friendly_name("res");
+    return std::make_shared<ov::Model>(ov::ResultVector{result}, ov::ParameterVector{param});
+}
+
+std::shared_ptr<ov::Model> ModelGenerator::get_model_without_repeated_blocks() {
+    std::shared_ptr<ov::op::v0::Parameter> input = std::make_shared<ov::op::v0::Parameter>(ov::element::i32, ov::Shape{1, 1, 40});
+    m_nodes.push_back(input);
+    set_name(input);
+
+    std::shared_ptr<ov::Node> res = get_block(input);
+
+    auto result = std::make_shared<ov::op::v0::Result>(res);
+    m_nodes.push_back(result);
+    set_name(result);
+
+    ov::ParameterVector params = {input};
+    ov::ResultVector results = {result};
+
+    return std::make_shared<ov::Model>(results, params);
+}
+
+std::shared_ptr<ov::Model> ModelGenerator::get_model_with_repeated_blocks(std::size_t repetitions) {
+    // Generate head
+    std::shared_ptr<ov::op::v0::Parameter> input = std::make_shared<ov::op::v0::Parameter>(ov::element::i32, ov::Shape{1, 1, 40});
+    m_nodes.push_back(input);
+    set_name(input);
+
+    std::vector<std::shared_ptr<ov::Node>> head(7, nullptr);
+    head[0] = std::make_shared<ov::op::v1::Add>(input, input);
+    head[1] = std::make_shared<ov::op::v0::Constant>(ov::element::i32, ov::Shape{1}, std::vector<int>{2});
+    head[2] = std::make_shared<ov::op::v1::Divide>(head[0], head[1], true);
+    head[3] = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{4}, std::vector<int>{1, 1, 4, 10});
+    head[4] = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{3}, std::vector<int>{1, 1, 40});
+    head[5] = std::make_shared<ov::op::v1::Reshape>(head[2], head[3], false);
+    head[6] = std::make_shared<ov::op::v1::Reshape>(head[5], head[4], false);
+
+    for (const auto& h : head) {
+        m_nodes.push_back(h);
+        set_name(h);
+    }
+
+    // Generate repeated blocks
+    std::shared_ptr<ov::Node> output = get_block(head[6]);
+    std::vector<std::shared_ptr<ov::Node>> outputs;
+    outputs.push_back(output);
+
+    for (size_t i = 0; i < repetitions - 1; ++i) {
+        output = get_block(output);
+        outputs.push_back(output);
+    }
+
+    // Generate tail
+    std::vector<std::shared_ptr<ov::Node>> tail(6, nullptr);
+    tail[0] = std::make_shared<ov::op::v0::Concat>(outputs, -1);
+    tail[1] = std::make_shared<ov::op::v0::Constant>(ov::element::i32, ov::Shape{3}, std::vector<int>{1, 40, int(repetitions)});
+    tail[2] = std::make_shared<ov::op::v1::Reshape>(tail[0], tail[1], false);
+    tail[3] = std::make_shared<ov::op::v0::Constant>(ov::element::i32, ov::Shape{1, 1, 1});
+    tail[4] = std::make_shared<ov::op::v1::Multiply>(tail[2], tail[3]);
+    tail[5] = std::make_shared<ov::op::v1::Add>(tail[4], tail[4]);
+
+    for (const auto& t : tail) {
+        m_nodes.push_back(t);
+        set_name(t);
+    }
+
+    // Create model
+    auto result = std::make_shared<ov::op::v0::Result>(tail[5]);
+    m_nodes.push_back(result);
+    set_name(result);
+
+    ov::ParameterVector params = {input};
+    ov::ResultVector results = {result};
+
+    return std::make_shared<ov::Model>(results, params);
+}
+
+std::shared_ptr<ov::Model> ModelGenerator::get_model_with_repeated_blocks() {
+    return get_model_with_repeated_blocks(10);
+}
+
+std::shared_ptr<ov::Node> ModelGenerator::get_block(const std::shared_ptr<ov::Node>& input) {
+    // Parameters
+    // input
+
+    // Constants
+    std::vector<std::shared_ptr<ov::Node>> model_c(18, nullptr);
+    model_c[0] = std::make_shared<ov::op::v0::Constant>(ov::element::i32, ov::Shape{4}, std::vector<int>{0, 2, 1, 3});
+    model_c[1] = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{1}, std::vector<int>{1});
+    model_c[2] = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{1}, std::vector<int>{0});
+    model_c[3] = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{1}, std::vector<int>{2});
+    model_c[4] = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{1}, std::vector<int>{0});
+    model_c[5] = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{4}, std::vector<int>{1, 1, 1, 1});
+    model_c[6] = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{1}, std::vector<int>{1});
+    model_c[7] = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{1}, std::vector<int>{0});
+    model_c[8] = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{4}, std::vector<int>{1, 1, 1, 1});
+    model_c[9] = std::make_shared<ov::op::v0::Constant>(ov::element::i32, ov::Shape{4}, std::vector<int>{1, 1, 1, 2});
+    model_c[10] = std::make_shared<ov::op::v0::Constant>(ov::element::i32, ov::Shape{4}, std::vector<int>{1, 1, 1, 1});
+    model_c[11] = std::make_shared<ov::op::v0::Constant>(ov::element::i32, ov::Shape{4}, std::vector<int>{1, 1, 1, 2});
+    model_c[12] = std::make_shared<ov::op::v0::Constant>(ov::element::i32, ov::Shape{1, 1, 1, 1});
+    model_c[13] = std::make_shared<ov::op::v0::Constant>(ov::element::i32, ov::Shape{1, 1, 1, 1});
+    model_c[14] = std::make_shared<ov::op::v0::Constant>(ov::element::i32, ov::Shape{1, 1, 1, 1});
+    model_c[15] = std::make_shared<ov::op::v0::Constant>(ov::element::f32, ov::Shape{40, 40});
+    model_c[16] = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{4}, std::vector<int>{1, 1, 4, 10});
+    model_c[17] = std::make_shared<ov::op::v0::Constant>(ov::element::i32, ov::Shape{3}, std::vector<int>{1, 1, 40});
+
+    for (const auto& c : model_c) {
+        m_nodes.push_back(c);
+        set_name(c);
+    }
+
+    // Converts
+    std::vector<std::shared_ptr<ov::Node>> convert(3, nullptr);
+    convert[0] = std::make_shared<ov::op::v0::Convert>(model_c[15], ov::element::f16);
+    convert[1] = std::make_shared<ov::op::v0::Convert>(convert[0], ov::element::i32);
+    convert[2] = std::make_shared<ov::op::v0::Convert>(model_c[12], ov::element::i32);
+
+    for (const auto& c : convert) {
+        m_nodes.push_back(c);
+        set_name(c);
+    }
+
+    // Ops
+    std::vector<std::shared_ptr<ov::Node>> op(16, nullptr);
+    op[0] = std::make_shared<ov::op::v0::MatMul>(input, convert[1], false, true);
+    op[1] = std::make_shared<ov::op::v1::Reshape>(op[0], model_c[16], false);
+    op[2] = std::make_shared<ov::op::v1::Transpose>(op[1], model_c[0]);
+    op[3] = std::make_shared<ov::op::v0::ShapeOf>(op[2]);
+    op[4] = std::make_shared<ov::op::v1::Gather>(op[3], model_c[1], model_c[2]);
+    op[5] = std::make_shared<ov::op::v1::Divide>(op[4], model_c[3], true);
+    op[6] = std::make_shared<ov::op::v0::Floor>(op[5]);
+    op[7] = std::make_shared<ov::op::v3::ScatterUpdate>(model_c[5], model_c[6], op[6], model_c[7]);
+    op[8] = std::make_shared<ov::op::v1::StridedSlice>(op[2],
+                                                        model_c[8],
+                                                        op[7],
+                                                        model_c[9],
+                                                        std::vector<int64_t>{1, 1, 1, 1},
+                                                        std::vector<int64_t>{1, 1, 1, 1});
+    op[9] = std::make_shared<ov::op::v1::StridedSlice>(op[2],
+                                                        op[7],
+                                                        model_c[10],
+                                                        model_c[11],
+                                                        std::vector<int64_t>{1, 1, 1, 1},
+                                                        std::vector<int64_t>{1, 1, 1, 1});
+    op[10] = std::make_shared<ov::op::v1::Multiply>(op[9], convert[2]);
+    op[11] = std::make_shared<ov::op::v0::Concat>(std::vector<std::shared_ptr<ov::Node>>{op[10], op[8]}, -1);
+    op[12] = std::make_shared<ov::op::v1::Multiply>(model_c[13], op[11]);
+    op[13] = std::make_shared<ov::op::v1::Multiply>(model_c[14], op[2]);
+    op[14] = std::make_shared<ov::op::v1::Add>(op[13], op[12]);
+    op[15] = std::make_shared<ov::op::v1::Reshape>(op[14], model_c[17], false);
+
+    for (const auto& o : op) {
+        m_nodes.push_back(o);
+        set_name(o);
+    }
+
+    return op[15];
+}
+
+void ModelGenerator::set_name(const std::shared_ptr<ov::Node>& node) {
+    node->set_friendly_name("node_" + std::to_string(m_name_idx++));
+}
diff --git a/src/plugins/intel_npu/tests/functional/behavior/npuw/generators/model_generator.hpp b/src/plugins/intel_npu/tests/functional/behavior/npuw/generators/model_generator.hpp
new file mode 100644
index 00000000000000..ca5cd8a38bc023
--- /dev/null
+++ b/src/plugins/intel_npu/tests/functional/behavior/npuw/generators/model_generator.hpp
@@ -0,0 +1,25 @@
+// Copyright (C) 2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#pragma once
+
+#include "openvino/openvino.hpp"
+
+class ModelGenerator {
+public:
+    ModelGenerator() = default;
+
+    std::shared_ptr<ov::Model> get_model_with_one_op();
+    std::shared_ptr<ov::Model> get_model_without_repeated_blocks();
+    std::shared_ptr<ov::Model> get_model_with_repeated_blocks(std::size_t repetitions);
+    std::shared_ptr<ov::Model> get_model_with_repeated_blocks();
+
+private:
+    std::shared_ptr<ov::Node> get_block(const std::shared_ptr<ov::Node>& input);
+    void set_name(const std::shared_ptr<ov::Node>& node);
+
+private:
+    std::vector<std::shared_ptr<ov::Node>> m_nodes;
+    size_t m_name_idx;
+};
diff --git a/src/plugins/intel_npu/tests/functional/behavior/npuw/mocks/mock_plugins.cpp b/src/plugins/intel_npu/tests/functional/behavior/npuw/mocks/mock_plugins.cpp
index 666776a22d6800..ed4bf72a945f79 100644
--- a/src/plugins/intel_npu/tests/functional/behavior/npuw/mocks/mock_plugins.cpp
+++ b/src/plugins/intel_npu/tests/functional/behavior/npuw/mocks/mock_plugins.cpp
@@ -52,9 +52,9 @@ void MockInferRequestBase::create_implementation() {
 MockCompiledModelBase::MockCompiledModelBase(const std::shared_ptr<const ov::Model>& model,
                                              std::shared_ptr<ov::IPlugin> plugin,
                                              const ov::AnyMap& config,
-                                             std::shared_ptr<std::pair<std::function<void(MockInferRequest&)>, bool>> infer_req_expectations_ptr)
+                                             std::shared_ptr<std::map<int, std::pair<std::function<void(MockInferRequest&)>, bool>>> infer_reqs_to_expectations_ptr)
             : ov::ICompiledModel(model, plugin),
-            m_infer_req_expectations_ptr(infer_req_expectations_ptr),
+            m_infer_reqs_to_expectations_ptr(infer_reqs_to_expectations_ptr),
             m_model(model),
             m_config(config) {
     }
@@ -99,12 +99,17 @@ void MockCompiledModelBase::create_implementation() {
         auto mock_sync_infer_request = std::make_shared<MockInferRequest>(
                 std::dynamic_pointer_cast<const MockCompiledModel>(shared_from_this()));
         mock_sync_infer_request->create_implementation();
-        if (m_infer_req_expectations_ptr) {
-            auto& expectation = m_infer_req_expectations_ptr->first;
-            auto& status = m_infer_req_expectations_ptr->second;
+        if (m_infer_reqs_to_expectations_ptr &&
+            m_infer_reqs_to_expectations_ptr->count(m_num_created_infer_requests)) {
+            auto& expectation_and_status = (*m_infer_reqs_to_expectations_ptr)[m_num_created_infer_requests];
+            auto& expectation = expectation_and_status.first;
+            auto& status = expectation_and_status.second;
             expectation(*mock_sync_infer_request);
             status = true;  // Expectation will be checked
         }
+
+        ++m_num_created_infer_requests;
+
         return mock_sync_infer_request;
     });
 }
@@ -134,14 +139,14 @@ void MockPluginBase<DeviceType>::create_implementation() {
             .WillByDefault([this](const std::shared_ptr<const ov::Model>& model, const ov::AnyMap& properties) {
                 std::lock_guard<std::mutex> lock(m_mock_creation_mutex);
 
-                std::shared_ptr<std::pair<std::function<void(MockInferRequest&)>, bool>> infer_req_expectations;
-                if (m_infer_reqs_to_expectations.count(m_num_compiled_models)) {
-                    infer_req_expectations = m_infer_reqs_to_expectations[m_num_compiled_models];
+                std::shared_ptr<std::map<int, std::pair<std::function<void(MockInferRequest&)>, bool>>> infer_reqs_to_expectations;
+                if (m_models_to_reqs_to_expectations.count(m_num_compiled_models)) {
+                    infer_reqs_to_expectations = m_models_to_reqs_to_expectations[m_num_compiled_models];
                 }
 
                 auto mock_compiled_model = std::make_shared<MockCompiledModel>(
                     model, shared_from_this(), properties,
-                    infer_req_expectations);
+                    infer_reqs_to_expectations);
 
                 mock_compiled_model->create_implementation();
 
@@ -265,10 +270,14 @@ void MockPluginBase<DeviceType>::set_expectations_to_comp_models(int model_idx,
 }
 
 template <typename DeviceType>
-void MockPluginBase<DeviceType>::set_expectations_to_infer_reqs(int req_idx,
+void MockPluginBase<DeviceType>::set_expectations_to_infer_reqs(int model_idx, int req_idx,
                                                                 std::function<void(MockInferRequest&)> expectations) {
-    m_infer_reqs_to_expectations[req_idx] =
-            std::make_shared<std::pair<std::function<void(MockInferRequest&)>, bool>>(expectations, false);
+    if (!m_models_to_reqs_to_expectations.count(model_idx)) {
+        m_models_to_reqs_to_expectations[model_idx] =
+                std::make_shared<std::map<int, std::pair<std::function<void(MockInferRequest&)>, bool>>>();
+    }
+    m_models_to_reqs_to_expectations[model_idx]->insert(            
+            {req_idx, std::pair<std::function<void(MockInferRequest&)>, bool>(expectations, false)});
 }
 
 template <typename DeviceType>
@@ -277,16 +286,21 @@ MockPluginBase<DeviceType>::~MockPluginBase() {
         auto idx = idx_to_expectation_and_status.first;
         auto expectation_and_status = idx_to_expectation_and_status.second;
         if (!expectation_and_status.second) {
-            ADD_FAILURE() << DeviceType::name  << ": Expectation for model with index " << idx
-                          << " was set, but model with that idx was not compiled";
+            ADD_FAILURE() << DeviceType::name  << ": Expectation for model[" << idx
+                          << "] was set, but that model was not compiled";
         }
     }
-    for (auto const& idx_to_expectation_and_status : m_infer_reqs_to_expectations) {
-        auto idx = idx_to_expectation_and_status.first;
-        auto expectation_and_status = *idx_to_expectation_and_status.second;
-        if (!expectation_and_status.second) {
-            ADD_FAILURE() << DeviceType::name << ": Expectation for request with index " << idx
-                          << " was set, but request with that idx was not created";
+    for (auto const& idx_to_reqs_to_expectation_and_status : m_models_to_reqs_to_expectations) {
+        OPENVINO_ASSERT(idx_to_reqs_to_expectation_and_status.second);
+        for (auto const& req_to_expectation_and_status : *idx_to_reqs_to_expectation_and_status.second) {
+            auto req_idx = req_to_expectation_and_status.first;
+            auto expectation_and_status = req_to_expectation_and_status.second;
+            if (!expectation_and_status.second) {
+                auto model_idx = idx_to_reqs_to_expectation_and_status.first;
+                ADD_FAILURE() << DeviceType::name << ": Expectation for request[" << req_idx
+                            << "] of model[" << model_idx << "] was set, but that request was "
+                            << "not created";
+            }
         }
     }
 }
diff --git a/src/plugins/intel_npu/tests/functional/behavior/npuw/mocks/mock_plugins.hpp b/src/plugins/intel_npu/tests/functional/behavior/npuw/mocks/mock_plugins.hpp
index 5c02f96ebd581e..4d720796c6abbf 100644
--- a/src/plugins/intel_npu/tests/functional/behavior/npuw/mocks/mock_plugins.hpp
+++ b/src/plugins/intel_npu/tests/functional/behavior/npuw/mocks/mock_plugins.hpp
@@ -57,7 +57,7 @@ class MockCompiledModelBase : public ov::ICompiledModel {
     MockCompiledModelBase(
             const std::shared_ptr<const ov::Model>& model, std::shared_ptr<ov::IPlugin> plugin,
             const ov::AnyMap& config,
-            std::shared_ptr<std::pair<std::function<void(MockInferRequest&)>, bool>> infer_req_expectations);
+            std::shared_ptr<std::map<int, std::pair<std::function<void(MockInferRequest&)>, bool>>> infer_reqs_to_expectations);
 
     // Methods from a base class ov::ICompiledModel
     MOCK_METHOD(const std::vector<ov::Output<const ov::Node>>&, outputs, (), (const, override));
@@ -74,7 +74,8 @@ class MockCompiledModelBase : public ov::ICompiledModel {
 
 private:
     std::mutex m_mock_creation_mutex;
-    std::shared_ptr<std::pair<std::function<void(MockInferRequest&)>, bool>> m_infer_req_expectations_ptr;
+    int m_num_created_infer_requests{};
+    std::shared_ptr<std::map<int, std::pair<std::function<void(MockInferRequest&)>, bool>>> m_infer_reqs_to_expectations_ptr;
 
     std::shared_ptr<const ov::Model> m_model;
     ov::AnyMap m_config;
@@ -119,7 +120,7 @@ class MockPluginBase : public ov::IPlugin {
     // This must be called *before* the custom ON_CALL() statements.
     void create_implementation();
     void set_expectations_to_comp_models(int model_idx, std::function<void(MockCompiledModel&)> expectations);
-    void set_expectations_to_infer_reqs(int req_idx, std::function<void(MockInferRequest&)> expectations);
+    void set_expectations_to_infer_reqs(int model_idx, int req_idx, std::function<void(MockInferRequest&)> expectations);
 
     ~MockPluginBase() override;
 
@@ -129,7 +130,7 @@ class MockPluginBase : public ov::IPlugin {
     int m_num_compiled_models{};
     // TODO: Make thread-safe and simplify.
     std::map<int, std::pair<std::function<void(MockCompiledModel&)>, bool>> m_models_to_expectations;
-    std::map<int, std::shared_ptr<std::pair<std::function<void(MockInferRequest&)>, bool>>> m_infer_reqs_to_expectations;
+    std::map<int, std::shared_ptr<std::map<int, std::pair<std::function<void(MockInferRequest&)>, bool>>>> m_models_to_reqs_to_expectations;
 
     // Properties
     int32_t num_streams{0};

From e4dbed21f16ce894be2e5644543874a396814fce Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Hubert=20B=C5=82aszczyk?=
 <56601011+hub-bla@users.noreply.github.com>
Date: Wed, 18 Sep 2024 17:16:34 +0200
Subject: [PATCH 04/49] [GHA] Enable parallel execution of TensorFlow Lite
 Layer python tests (#26662)

### Details:
 - Enabled parallel call
- Fixed tests to be compatible with pytest-xdist plugin (pass function
name instead of reference)
- `os.path.dirname` caused saving model in `/out` directory instead of
`/out/{temp_dir}` beacause it didn't have '/' char at the end and it
treated `temp_dir` like a file

### Tickets:
 - [None](https://github.com/openvinotoolkit/openvino/issues/20920)

Without parallel execution
![Screenshot from 2024-09-18
14-15-16](https://github.com/user-attachments/assets/f1b00954-de59-445a-904f-5b13819c0971)

With parallel execution (8 cpu cores)
![Screenshot from 2024-09-18
14-32-48](https://github.com/user-attachments/assets/2fc144cc-f771-43aa-909b-f41dedc1ccca)
---
 .github/workflows/job_python_unit_tests.yml   |  2 +-
 .github/workflows/windows_vs2019_release.yml  |  2 +-
 .../common/tflite_layer_test_class.py         |  2 +-
 .../layer_tests/common/utils/tflite_utils.py  |  6 +--
 .../test_tfl_ArgValue.py                      |  6 +--
 .../tensorflow_lite_tests/test_tfl_Binary.py  | 30 ++++++------
 .../test_tfl_BinaryWithActivation.py          | 12 ++---
 .../tensorflow_lite_tests/test_tfl_Pool.py    |  6 +--
 .../tensorflow_lite_tests/test_tfl_Reduce.py  | 16 +++----
 .../tensorflow_lite_tests/test_tfl_Resize.py  |  6 +--
 .../tensorflow_lite_tests/test_tfl_Unary.py   | 46 +++++++++----------
 11 files changed, 67 insertions(+), 67 deletions(-)

diff --git a/.github/workflows/job_python_unit_tests.yml b/.github/workflows/job_python_unit_tests.yml
index c4ab208c54e6a8..862ade24810e8c 100644
--- a/.github/workflows/job_python_unit_tests.yml
+++ b/.github/workflows/job_python_unit_tests.yml
@@ -260,7 +260,7 @@ jobs:
 
       - name: TensorFlow Lite Layer Tests - TFL FE
         if: fromJSON(inputs.affected-components).TFL_FE.test
-        run: python3 -m pytest ${LAYER_TESTS_INSTALL_DIR}/tensorflow_lite_tests/ --junitxml=${INSTALL_TEST_DIR}/TEST-tfl_fe.xml
+        run: python3 -m pytest ${LAYER_TESTS_INSTALL_DIR}/tensorflow_lite_tests/ -n logical --junitxml=${INSTALL_TEST_DIR}/TEST-tfl_fe.xml
         env:
           TEST_DEVICE: CPU
           TEST_PRECISION: FP16
diff --git a/.github/workflows/windows_vs2019_release.yml b/.github/workflows/windows_vs2019_release.yml
index a1ff614672cccd..b9d5d1687f773e 100644
--- a/.github/workflows/windows_vs2019_release.yml
+++ b/.github/workflows/windows_vs2019_release.yml
@@ -340,7 +340,7 @@ jobs:
         if: fromJSON(needs.smart_ci.outputs.affected_components).TFL_FE.test
         shell: cmd
         run: |
-          python3 -m pytest ${{ env.LAYER_TESTS_INSTALL_DIR }}/tensorflow_lite_tests/ --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-tfl_fe.xml
+          python3 -m pytest ${{ env.LAYER_TESTS_INSTALL_DIR }}/tensorflow_lite_tests/ -n logical --junitxml=${{ env.INSTALL_TEST_DIR }}/TEST-tfl_fe.xml
         env:
           TEST_DEVICE: CPU
           TEST_PRECISION: FP16
diff --git a/tests/layer_tests/common/tflite_layer_test_class.py b/tests/layer_tests/common/tflite_layer_test_class.py
index 8b1fdd9f794a67..2bc75c76a3ca05 100644
--- a/tests/layer_tests/common/tflite_layer_test_class.py
+++ b/tests/layer_tests/common/tflite_layer_test_class.py
@@ -34,7 +34,7 @@ def produce_tflite_model(self, framework_model, save_path):
                                                          input_tensors=input_tensors,
                                                          output_tensors=output_tensors).convert()
 
-        tflite_model_path = os.path.join(os.path.dirname(save_path), 'model.tflite')
+        tflite_model_path = os.path.join(save_path, 'model.tflite')
         with tf.io.gfile.GFile(tflite_model_path, 'wb') as f:
             f.write(tflite_model)
         return tflite_model_path
diff --git a/tests/layer_tests/common/utils/tflite_utils.py b/tests/layer_tests/common/utils/tflite_utils.py
index 475858c1a7b777..5754e063a7f198 100644
--- a/tests/layer_tests/common/utils/tflite_utils.py
+++ b/tests/layer_tests/common/utils/tflite_utils.py
@@ -54,13 +54,13 @@ def activation_helper(input_node, activation_name, name):
     ],
     [
         {'activation': None},
-        {'activation': tf.nn.relu},
-        {'activation': tf.nn.relu6},
+        {'activation': 'tf.nn.relu'},
+        {'activation': 'tf.nn.relu6'},
         # skip tanh and signbit since tflite doesn't fuse such activations
         # https://github.com/tensorflow/tensorflow/blob/77d8c333405a080c57850c45531dbbf077b2bd0e/tensorflow/compiler/mlir/lite/transforms/optimize_patterns.td#L86:L89
         # {'activation': tf.math.tanh},
         # {'activation': lambda x, name: tf.identity(tf.experimental.numpy.signbit(x), name=name)},
-        {'activation': lambda x, name: tf.math.minimum(tf.math.maximum(-1., x), 1., name=name)}
+        {'activation': 'lambda x, name: tf.math.minimum(tf.math.maximum(-1., x), 1., name=name)'}
     ]
 ]
 
diff --git a/tests/layer_tests/tensorflow_lite_tests/test_tfl_ArgValue.py b/tests/layer_tests/tensorflow_lite_tests/test_tfl_ArgValue.py
index 70dadf58722c0f..87bf7c4c96599b 100644
--- a/tests/layer_tests/tensorflow_lite_tests/test_tfl_ArgValue.py
+++ b/tests/layer_tests/tensorflow_lite_tests/test_tfl_ArgValue.py
@@ -6,8 +6,8 @@
 from common.utils.tflite_utils import parametrize_tests
 
 test_ops = [
-    {'op_name': 'ARG_MAX', 'op_func': tf.math.argmax},
-    {'op_name': 'ARG_MIN', 'op_func': tf.math.argmin},
+    {'op_name': 'ARG_MAX', 'op_func': 'tf.math.argmax'},
+    {'op_name': 'ARG_MIN', 'op_func': 'tf.math.argmin'},
 ]
 
 test_params = [
@@ -31,7 +31,7 @@ def make_model(self, params):
         with tf.compat.v1.Session() as sess:
             place_holder = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
                                                     name=self.inputs[0])
-            params['op_func'](place_holder, axis=params['axis'], name=self.outputs[0])
+            eval(params['op_func'])(place_holder, axis=params['axis'], name=self.outputs[0])
             net = sess.graph_def
         return net
 
diff --git a/tests/layer_tests/tensorflow_lite_tests/test_tfl_Binary.py b/tests/layer_tests/tensorflow_lite_tests/test_tfl_Binary.py
index 71e117c8c6893d..99fe3c0caa2245 100644
--- a/tests/layer_tests/tensorflow_lite_tests/test_tfl_Binary.py
+++ b/tests/layer_tests/tensorflow_lite_tests/test_tfl_Binary.py
@@ -6,20 +6,20 @@
 from common.utils.tflite_utils import parametrize_tests
 
 test_ops = [
-    {'op_name': 'EQUAL', 'op_func': tf.math.equal},
-    {'op_name': 'FLOOR_MOD', 'op_func': tf.math.floormod, 'kwargs_to_prepare_input': 'positive'},
-    {'op_name': 'FLOOR_DIV', 'op_func': tf.math.floordiv, 'kwargs_to_prepare_input': 'positive'},
-    {'op_name': 'GREATER', 'op_func': tf.math.greater},
-    {'op_name': 'GREATER_EQUAL', 'op_func': tf.math.greater_equal},
-    {'op_name': 'LESS', 'op_func': tf.math.less},
-    {'op_name': 'LESS_EQUAL', 'op_func': tf.math.less_equal},
-    {'op_name': 'LOGICAL_AND', 'op_func': tf.math.logical_and, 'kwargs_to_prepare_input': 'boolean', 'dtype': tf.bool},
-    {'op_name': 'LOGICAL_OR', 'op_func': tf.math.logical_or, 'kwargs_to_prepare_input': 'boolean', 'dtype': tf.bool},
-    {'op_name': 'MAXIMUM', 'op_func': tf.math.maximum},
-    {'op_name': 'MINIMUM', 'op_func': tf.math.minimum},
-    {'op_name': 'NOT_EQUAL', 'op_func': tf.math.not_equal},
-    {'op_name': 'POW', 'op_func': tf.math.pow, 'kwargs_to_prepare_input': 'positive'},
-    {'op_name': 'SQUARED_DIFFERENCE', 'op_func': tf.math.squared_difference},
+    {'op_name': 'EQUAL', 'op_func': 'tf.math.equal'},
+    {'op_name': 'FLOOR_MOD', 'op_func': 'tf.math.floormod', 'kwargs_to_prepare_input': 'positive'},
+    {'op_name': 'FLOOR_DIV', 'op_func': 'tf.math.floordiv', 'kwargs_to_prepare_input': 'positive'},
+    {'op_name': 'GREATER', 'op_func': 'tf.math.greater'},
+    {'op_name': 'GREATER_EQUAL', 'op_func': 'tf.math.greater_equal'},
+    {'op_name': 'LESS', 'op_func': 'tf.math.less'},
+    {'op_name': 'LESS_EQUAL', 'op_func': 'tf.math.less_equal'},
+    {'op_name': 'LOGICAL_AND', 'op_func': 'tf.math.logical_and', 'kwargs_to_prepare_input': 'boolean', 'dtype': tf.bool},
+    {'op_name': 'LOGICAL_OR', 'op_func': 'tf.math.logical_or', 'kwargs_to_prepare_input': 'boolean', 'dtype': tf.bool},
+    {'op_name': 'MAXIMUM', 'op_func': 'tf.math.maximum'},
+    {'op_name': 'MINIMUM', 'op_func': 'tf.math.minimum'},
+    {'op_name': 'NOT_EQUAL', 'op_func': 'tf.math.not_equal'},
+    {'op_name': 'POW', 'op_func': 'tf.math.pow', 'kwargs_to_prepare_input': 'positive'},
+    {'op_name': 'SQUARED_DIFFERENCE', 'op_func': 'tf.math.squared_difference'},
 ]
 
 test_params = [
@@ -44,7 +44,7 @@ def make_model(self, params):
                                                      name=self.inputs[0])
             place_holder1 = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
                                                      name=self.inputs[1])
-            params['op_func'](place_holder0, place_holder1, name=self.outputs[0])
+            eval(params['op_func'])(place_holder0, place_holder1, name=self.outputs[0])
             net = sess.graph_def
         return net
 
diff --git a/tests/layer_tests/tensorflow_lite_tests/test_tfl_BinaryWithActivation.py b/tests/layer_tests/tensorflow_lite_tests/test_tfl_BinaryWithActivation.py
index 1e44bfdb6b01f4..6e6ed7625f8f3e 100644
--- a/tests/layer_tests/tensorflow_lite_tests/test_tfl_BinaryWithActivation.py
+++ b/tests/layer_tests/tensorflow_lite_tests/test_tfl_BinaryWithActivation.py
@@ -6,10 +6,10 @@
 from common.utils.tflite_utils import parametrize_tests
 
 test_ops = [
-    {'op_name': 'ADD', 'op_func': tf.math.add},
-    {'op_name': 'DIV', 'op_func': tf.math.divide, 'kwargs_to_prepare_input': 'positive'},
-    {'op_name': 'MUL', 'op_func': tf.math.multiply},
-    {'op_name': 'SUB', 'op_func': tf.math.subtract},
+    {'op_name': 'ADD', 'op_func': 'tf.math.add'},
+    {'op_name': 'DIV', 'op_func': 'tf.math.divide', 'kwargs_to_prepare_input': 'positive'},
+    {'op_name': 'MUL', 'op_func': 'tf.math.multiply'},
+    {'op_name': 'SUB', 'op_func': 'tf.math.subtract'},
 ]
 
 test_params = [
@@ -35,8 +35,8 @@ def make_model(self, params):
             in1 = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'], name=self.inputs[1])
             bin_op_name = self.outputs[0] if not params['activation'] else \
                 self.outputs[0] + "/op"
-            op = params['op_func'](in0, in1, name=bin_op_name)
-            op = activation_helper(op, params['activation'], self.outputs[0])
+            op = eval(params['op_func'])(in0, in1, name=bin_op_name)
+            op = activation_helper(op, eval(params['activation']) if params['activation'] else None, self.outputs[0])
 
             net = sess.graph_def
         return net
diff --git a/tests/layer_tests/tensorflow_lite_tests/test_tfl_Pool.py b/tests/layer_tests/tensorflow_lite_tests/test_tfl_Pool.py
index fd96bcf08cdf3c..89067a0b33fda9 100644
--- a/tests/layer_tests/tensorflow_lite_tests/test_tfl_Pool.py
+++ b/tests/layer_tests/tensorflow_lite_tests/test_tfl_Pool.py
@@ -5,8 +5,8 @@
 from common.utils.tflite_utils import parametrize_tests
 
 test_ops = [
-    {'op_name': 'AVERAGE_POOL_2D', 'op_func': tf.nn.avg_pool2d},
-    {'op_name': 'MAX_POOL_2D', 'op_func': tf.nn.max_pool2d},
+    {'op_name': 'AVERAGE_POOL_2D', 'op_func': 'tf.nn.avg_pool2d'},
+    {'op_name': 'MAX_POOL_2D', 'op_func': 'tf.nn.max_pool2d'},
 ]
 
 test_params = [
@@ -32,7 +32,7 @@ def make_model(self, params):
         with tf.compat.v1.Session() as sess:
             place_holder = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
                                                     name=self.inputs[0])
-            params['op_func'](place_holder, params['ksize'], params['strides'],
+            eval(params['op_func'])(place_holder, params['ksize'], params['strides'],
                               params['padding'], 'NHWC', name=self.outputs[0])
             net = sess.graph_def
         return net
diff --git a/tests/layer_tests/tensorflow_lite_tests/test_tfl_Reduce.py b/tests/layer_tests/tensorflow_lite_tests/test_tfl_Reduce.py
index 6a2c1449f520e7..9cbc56045175ac 100644
--- a/tests/layer_tests/tensorflow_lite_tests/test_tfl_Reduce.py
+++ b/tests/layer_tests/tensorflow_lite_tests/test_tfl_Reduce.py
@@ -6,13 +6,13 @@
 from common.utils.tflite_utils import parametrize_tests
 
 test_ops = [
-    {'op_name': 'MEAN', 'op_func': tf.math.reduce_mean},
-    {'op_name': 'REDUCE_ALL', 'op_func': tf.math.reduce_all, 'kwargs_to_prepare_input': 'boolean', 'dtype': tf.bool},
-    {'op_name': 'REDUCE_ANY', 'op_func': tf.math.reduce_any, 'kwargs_to_prepare_input': 'boolean', 'dtype': tf.bool},
-    {'op_name': 'REDUCE_MAX', 'op_func': tf.math.reduce_max},
-    {'op_name': 'REDUCE_MIN', 'op_func': tf.math.reduce_min},
-    {'op_name': 'REDUCE_PROD', 'op_func': tf.math.reduce_prod, 'kwargs_to_prepare_input': 'short_range'},
-    {'op_name': 'SUM', 'op_func': tf.math.reduce_sum},
+    {'op_name': 'MEAN', 'op_func': 'tf.math.reduce_mean'},
+    {'op_name': 'REDUCE_ALL', 'op_func': 'tf.math.reduce_all', 'kwargs_to_prepare_input': 'boolean', 'dtype': tf.bool},
+    {'op_name': 'REDUCE_ANY', 'op_func': 'tf.math.reduce_any', 'kwargs_to_prepare_input': 'boolean', 'dtype': tf.bool},
+    {'op_name': 'REDUCE_MAX', 'op_func': 'tf.math.reduce_max'},
+    {'op_name': 'REDUCE_MIN', 'op_func': 'tf.math.reduce_min'},
+    {'op_name': 'REDUCE_PROD', 'op_func': 'tf.math.reduce_prod', 'kwargs_to_prepare_input': 'short_range'},
+    {'op_name': 'SUM', 'op_func': 'tf.math.reduce_sum'},
 ]
 
 test_params = [
@@ -36,7 +36,7 @@ def make_model(self, params):
         with tf.compat.v1.Session() as sess:
             place_holder = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
                                                     name=self.inputs[0])
-            params['op_func'](place_holder, axis=params['axis'], name=self.outputs[0])
+            eval(params['op_func'])(place_holder, axis=params['axis'], name=self.outputs[0])
             net = sess.graph_def
         return net
 
diff --git a/tests/layer_tests/tensorflow_lite_tests/test_tfl_Resize.py b/tests/layer_tests/tensorflow_lite_tests/test_tfl_Resize.py
index 5c37153e965856..18c90af71a80ef 100644
--- a/tests/layer_tests/tensorflow_lite_tests/test_tfl_Resize.py
+++ b/tests/layer_tests/tensorflow_lite_tests/test_tfl_Resize.py
@@ -5,8 +5,8 @@
 from common.utils.tflite_utils import parametrize_tests
 
 test_ops = [
-    {'op_name': ['RESIZE_BILINEAR'], 'op_func': tf.compat.v1.image.resize_bilinear},
-    {'op_name': ['RESIZE_NEAREST_NEIGHBOR'], 'op_func': tf.compat.v1.image.resize_nearest_neighbor},
+    {'op_name': ['RESIZE_BILINEAR'], 'op_func': 'tf.compat.v1.image.resize_bilinear'},
+    {'op_name': ['RESIZE_NEAREST_NEIGHBOR'], 'op_func': 'tf.compat.v1.image.resize_nearest_neighbor'},
 ]
 
 test_params = [
@@ -35,7 +35,7 @@ def make_model(self, params):
         with tf.compat.v1.Session() as sess:
             placeholder = tf.compat.v1.placeholder(params.get("dtype", tf.float32), params["shape"],
                                                    name=self.inputs[0])
-            params['op_func'](placeholder, size=params['size'],
+            eval(params['op_func'])(placeholder, size=params['size'],
                               align_corners=params['align_corners'],
                               half_pixel_centers=params['half_pixel_centers'],
                               name=self.outputs[0])
diff --git a/tests/layer_tests/tensorflow_lite_tests/test_tfl_Unary.py b/tests/layer_tests/tensorflow_lite_tests/test_tfl_Unary.py
index 8d348b54aa44d1..212951e6d4d698 100644
--- a/tests/layer_tests/tensorflow_lite_tests/test_tfl_Unary.py
+++ b/tests/layer_tests/tensorflow_lite_tests/test_tfl_Unary.py
@@ -12,28 +12,28 @@
 np.random.seed(42)
 
 test_ops = [
-    {'op_name': 'ABS', 'op_func': tf.math.abs},
-    {'op_name': 'CAST', 'op_func': partial(tf.cast, dtype=tf.int32)},
-    {'op_name': 'CEIL', 'op_func': tf.math.ceil},
-    {'op_name': 'COS', 'op_func': tf.math.cos},
-    {'op_name': 'ELU', 'op_func': tf.nn.elu},
-    {'op_name': 'EXP', 'op_func': tf.math.exp, 'kwargs_to_prepare_input': 'positive'},
-    {'op_name': 'FLOOR', 'op_func': tf.math.floor},
-    {'op_name': 'LEAKY_RELU', 'op_func': partial(tf.nn.leaky_relu, alpha=-0.5)},
-    {'op_name': 'LOG', 'op_func': tf.math.log, 'kwargs_to_prepare_input': 'positive'},
-    {'op_name': 'LOG_SOFTMAX', 'op_func': partial(tf.nn.log_softmax, axis=-1)},
-    {'op_name': 'LOGICAL_NOT', 'op_func': tf.math.logical_not, 'kwargs_to_prepare_input': 'boolean', 'dtype': tf.bool},
-    {'op_name': 'LOGISTIC', 'op_func': tf.math.sigmoid},
-    {'op_name': 'NEG', 'op_func': tf.math.negative},
-    {'op_name': 'RELU6', 'op_func': tf.nn.relu6},
-    {'op_name': 'RELU', 'op_func': tf.nn.relu},
-    {'op_name': 'ROUND', 'op_func': tf.math.round},
-    {'op_name': 'RSQRT', 'op_func': tf.math.rsqrt, 'kwargs_to_prepare_input': 'positive'},
-    {'op_name': 'SIN', 'op_func': tf.math.sin},
-    {'op_name': 'SOFTMAX', 'op_func': partial(tf.nn.softmax, axis=-1)},  # additionally test with alpha
-    {'op_name': 'SQRT', 'op_func': tf.math.sqrt, 'kwargs_to_prepare_input': 'positive'},
-    {'op_name': 'SQUARE', 'op_func': tf.math.square},
-    {'op_name': 'TANH', 'op_func': tf.math.tanh},
+    {'op_name': 'ABS', 'op_func': 'tf.math.abs'},
+    {'op_name': 'CAST', 'op_func': 'partial(tf.cast, dtype=tf.int32)'},
+    {'op_name': 'CEIL', 'op_func': 'tf.math.ceil'},
+    {'op_name': 'COS', 'op_func': 'tf.math.cos'},
+    {'op_name': 'ELU', 'op_func': 'tf.nn.elu'},
+    {'op_name': 'EXP', 'op_func': 'tf.math.exp', 'kwargs_to_prepare_input': 'positive'},
+    {'op_name': 'FLOOR', 'op_func': 'tf.math.floor'},
+    {'op_name': 'LEAKY_RELU', 'op_func': 'partial(tf.nn.leaky_relu, alpha=-0.5)'},
+    {'op_name': 'LOG', 'op_func': 'tf.math.log', 'kwargs_to_prepare_input': 'positive'},
+    {'op_name': 'LOG_SOFTMAX', 'op_func': 'partial(tf.nn.log_softmax, axis=-1)'},
+    {'op_name': 'LOGICAL_NOT', 'op_func': 'tf.math.logical_not', 'kwargs_to_prepare_input': 'boolean', 'dtype': tf.bool},
+    {'op_name': 'LOGISTIC', 'op_func': 'tf.math.sigmoid'},
+    {'op_name': 'NEG', 'op_func': 'tf.math.negative'},
+    {'op_name': 'RELU6', 'op_func': 'tf.nn.relu6'},
+    {'op_name': 'RELU', 'op_func': 'tf.nn.relu'},
+    {'op_name': 'ROUND', 'op_func': 'tf.math.round'},
+    {'op_name': 'RSQRT', 'op_func': 'tf.math.rsqrt', 'kwargs_to_prepare_input': 'positive'},
+    {'op_name': 'SIN', 'op_func': 'tf.math.sin'},
+    {'op_name': 'SOFTMAX', 'op_func': 'partial(tf.nn.softmax, axis=-1)'},  # additionally test with alpha
+    {'op_name': 'SQRT', 'op_func': 'tf.math.sqrt', 'kwargs_to_prepare_input': 'positive'},
+    {'op_name': 'SQUARE', 'op_func': 'tf.math.square'},
+    {'op_name': 'TANH', 'op_func': 'tf.math.tanh'},
 
     # These operations are getting optimized out by tflite aka empty tfl model
     # {'op_name': 'RANK', 'op_func': tf.rank},
@@ -63,7 +63,7 @@ def make_model(self, params):
         with tf.compat.v1.Session() as sess:
             place_holder = tf.compat.v1.placeholder(params.get('dtype', tf.float32), params['shape'],
                                                     name=self.inputs[0])
-            params['op_func'](place_holder, name=self.outputs[0])
+            eval(params['op_func'])(place_holder, name=self.outputs[0])
             net = sess.graph_def
         return net
 

From 1d3a71160b56da7ad631bcac5b9b5e5f21746b9b Mon Sep 17 00:00:00 2001
From: Sebastian Golebiewski <sebastianx.golebiewski@intel.com>
Date: Wed, 18 Sep 2024 17:34:47 +0200
Subject: [PATCH 05/49] [DOCS] Documenting the support for MXFP4 data format.
 (#26554)

Providing info about support for MXFP4 data format in quantization on
CPU. This PR addresses JIRA ticket no. 151042.
---
 .../weight-compression.rst                    |  9 ++++
 .../microscaling-quantization.rst             | 52 +++++++++++++++++++
 .../cpu-device.rst                            | 21 ++++----
 3 files changed, 73 insertions(+), 9 deletions(-)
 create mode 100644 docs/articles_en/openvino-workflow/model-optimization-guide/weight-compression/microscaling-quantization.rst

diff --git a/docs/articles_en/openvino-workflow/model-optimization-guide/weight-compression.rst b/docs/articles_en/openvino-workflow/model-optimization-guide/weight-compression.rst
index 2b67dc0ddf5cbb..6c85473502ff9b 100644
--- a/docs/articles_en/openvino-workflow/model-optimization-guide/weight-compression.rst
+++ b/docs/articles_en/openvino-workflow/model-optimization-guide/weight-compression.rst
@@ -1,6 +1,13 @@
 Weight Compression
 ==================
 
+.. toctree::
+   :maxdepth: 1
+   :hidden:
+
+   weight-compression/microscaling-quantization
+
+
 Weight compression is a technique for enhancing the efficiency of models,
 especially those with large memory requirements. This method reduces the model's
 memory footprint, a crucial factor for Large Language Models (LLMs).
@@ -289,6 +296,7 @@ from Optimum Intel instead of NNCF to compress the model to INT8_ASYM.
       results = pipe(phrase)
       print(results)
 
+
 Exporting and Loading Compressed Models
 ########################################
 
@@ -485,3 +493,4 @@ Additional Resources
 - `NNCF GitHub <https://github.com/openvinotoolkit/nncf>`__
 - :doc:`Post-training Quantization <quantizing-models-post-training>`
 - :doc:`Training-time Optimization <compressing-models-during-training>`
+- `OCP Microscaling Formats (MX) Specification <https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf>`__
diff --git a/docs/articles_en/openvino-workflow/model-optimization-guide/weight-compression/microscaling-quantization.rst b/docs/articles_en/openvino-workflow/model-optimization-guide/weight-compression/microscaling-quantization.rst
new file mode 100644
index 00000000000000..d4dfad2868cb1a
--- /dev/null
+++ b/docs/articles_en/openvino-workflow/model-optimization-guide/weight-compression/microscaling-quantization.rst
@@ -0,0 +1,52 @@
+Microscaling (MX) Quantization
+==============================
+
+Microscaling (MX) Quantization method has been introduced to enable users
+to quantize LLMs with a high compression rate at minimal cost of accuracy.
+The method helps maintain model performance comparable to that of the conventional
+FP32. It increases compute and storage efficiency by using low bit-width
+floating point and integer-based data formats:
+
++---------------+-----------------+----------------------------+
+|  Data format  |  Data type      | Description                |
++===============+=================+============================+
+| MXFP8         | | FP8 (E5M2)    | | Floating point, 8-bit    |
+|               | | FP8 (E4M3)    | | Floating point, 8-bit    |
++---------------+-----------------+----------------------------+
+| MXFP6         | | FP6 (E3M2)    | | Floating point, 6-bit    |
+|               | | FP6 (E2M3)    | | Floating point, 6-bit    |
++---------------+-----------------+----------------------------+
+| **MXFP4**     | **FP4 (E2M1)**  | **Floating point, 4-bit**  |
++---------------+-----------------+----------------------------+
+| MXINT8        | INT8            | Integer, 8-bit             |
++---------------+-----------------+----------------------------+
+
+
+.. _mxfp4_support:
+
+**Currently, only the**
+`MXFP4 (E2M1) <https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf>`__
+**data format is supported in NNCF and for quantization on CPU.**
+E2M1 may be considered for improving accuracy, however, quantized models will
+not be faster than the ones compressed to INT8_ASYM.
+
+Quantization to the E2M1 data type will compress weights to 4-bit without a zero
+point and with 8-bit E8M0 scales. To quantize a model to E2M1, set
+``mode=CompressWeightsMode.E2M1`` in ``nncf.compress_weights()``. It is
+recommended to use ``group size = 32``. See the example below:
+
+.. code-block:: py
+
+   from nncf import compress_weights, CompressWeightsMode
+   compressed_model = compress_weights(model, mode=CompressWeightsMode.E2M1, group_size=32, all_layers=True)
+
+.. note::
+
+   Different values for ``group_size`` and ``ratio`` are also supported.
+
+
+Additional Resources
+####################
+
+* `OCP Microscaling Formats (MX) Specification <https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf>`__
+* `Intel® Neural Compressor Documentation <https://intel.github.io/neural-compressor/latest/docs/source/3x/PT_MXQuant.html>`__
\ No newline at end of file
diff --git a/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/cpu-device.rst b/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/cpu-device.rst
index d4ee9af90c5a23..f1a914e6b9dac3 100644
--- a/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/cpu-device.rst
+++ b/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/cpu-device.rst
@@ -53,24 +53,27 @@ the ``ov::Core::compile_model()`` method:
          :fragment: [compile_model_default]
 
 
-Supported Inference Data Types
-###########################################################
+Supported Model Precision
+#########################
 
 CPU plugin supports the following data types as inference precision of internal primitives:
 
 - Floating-point data types:
 
-  - ``f32`` (Intel® x86-64, Arm®)
-  - ``bf16`` (Intel® x86-64)
-  - ``f16`` (Intel® x86-64, Arm®)
+  - ``FP32`` (Intel® x86-64, Arm®)
+  - ``BF16`` (Intel® x86-64)
+  - ``FP16`` (Intel® x86-64, Arm®)
+  - ``:ref:`MXFP4 <mxfp4_support>``` (Intel® x86-64)
+
 - Integer data types:
 
-  - ``i32`` (Intel® x86-64, Arm®)
+  - ``INT32`` (Intel® x86-64, Arm®)
+
 - Quantized data types:
 
-  - ``u8`` (Intel® x86-64)
-  - ``i8`` (Intel® x86-64)
-  - ``u1`` (Intel® x86-64)
+  - ``uINT8`` (Intel® x86-64)
+  - ``INT8`` (Intel® x86-64)
+  - ``uINT1`` (Intel® x86-64)
 
 :doc:`Hello Query Device C++ Sample <../../../learn-openvino/openvino-samples/hello-query-device>` can be used to print out supported data types for all detected devices.
 

From 311e2b36668e4e2a8e29799f3c1ca9c7bb62b8d1 Mon Sep 17 00:00:00 2001
From: Karol Blaszczak <karol.blaszczak@intel.com>
Date: Wed, 18 Sep 2024 18:30:56 +0200
Subject: [PATCH 06/49] Docs release notes 24.4 mstr (#26665)

port:
https://github.com/openvinotoolkit/openvino/pull/26529
https://github.com/openvinotoolkit/openvino/pull/26659
---
 .../additional-resources/terms-of-use.rst     |   4 +-
 .../about-openvino/release-notes-openvino.rst | 634 +++++++++++++-----
 .../system-requirements.rst                   |  77 +--
 docs/sphinx_setup/api/api_reference.rst       |   3 +-
 .../api/nodejs_api/nodejs_api.rst             |  12 +
 5 files changed, 482 insertions(+), 248 deletions(-)

diff --git a/docs/articles_en/about-openvino/additional-resources/terms-of-use.rst b/docs/articles_en/about-openvino/additional-resources/terms-of-use.rst
index afdf10aef06c5c..06a5c6dbf035b1 100644
--- a/docs/articles_en/about-openvino/additional-resources/terms-of-use.rst
+++ b/docs/articles_en/about-openvino/additional-resources/terms-of-use.rst
@@ -58,6 +58,4 @@ Your costs and results may vary.
 
 No product or component can be absolutely secure.
 
-Intel technologies may require enabled hardware, software or service activation.
-
-OpenCL and the OpenCL logo are trademarks of Apple Inc. used by permission by Khronos.
\ No newline at end of file
+Intel technologies may require enabled hardware, software or service activation.
\ No newline at end of file
diff --git a/docs/articles_en/about-openvino/release-notes-openvino.rst b/docs/articles_en/about-openvino/release-notes-openvino.rst
index 2f7cffccd5076b..c60371119de69b 100644
--- a/docs/articles_en/about-openvino/release-notes-openvino.rst
+++ b/docs/articles_en/about-openvino/release-notes-openvino.rst
@@ -15,282 +15,540 @@ OpenVINO Release Notes
 
 
 
-2024.3 - 31 July 2024
+2024.4 - 18 September 2024
 #############################
 
 :doc:`System Requirements <./release-notes-openvino/system-requirements>` | :doc:`Release policy <./release-notes-openvino/release-policy>` | :doc:`Installation Guides <./../get-started/install-openvino>`
 
 
+
 What's new
-+++++++++++++++++++++++++++++
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
 * More Gen AI coverage and framework integrations to minimize code changes.
 
-  * OpenVINO pre-optimized models are now available in Hugging Face making it easier for developers
-    to get started with these models.
+  * Support for GLM-4-9B Chat, MiniCPM-1B, Llama 3 and 3.1, Phi-3-Mini, Phi-3-Medium and
+    YOLOX-s models.
+  * Noteworthy notebooks added: Florence-2, NuExtract-tiny Structure Extraction, Flux.1 Image
+    Generation, PixArt-α: Photorealistic Text-to-Image Synthesis, and Phi-3-Vision Visual
+    Language Assistant.
 
 * Broader Large Language Model (LLM) support and more model compression techniques.
 
-  * Significant improvement in LLM performance on Intel discrete GPUs with the addition of
-    Multi-Head Attention (MHA) and OneDNN enhancements.
+  * OpenVINO™ runtime optimized for Intel® Xe Matrix Extensions (Intel® XMX) systolic arrays on
+    built-in GPUs for efficient matrix multiplication resulting in significant LLM performance
+    boost with improved 1st and 2nd token latency, as well as a smaller memory footprint on
+    Intel® Core™ Ultra Processors (Series 2).
+  * Memory sharing enabled for NPUs on Intel® Core™ Ultra Processors (Series 2) for efficient
+    pipeline integration without memory copy overhead.
+  * Addition of the PagedAttention feature for discrete GPUs* enables a significant boost in
+    throughput for parallel inferencing when serving LLMs on Intel® Arc™ Graphics or Intel®
+    Data Center GPU Flex Series.
 
 * More portability and performance to run AI at the edge, in the cloud, or locally.
 
-  * Improved CPU performance when serving LLMs with the inclusion of vLLM and continuous batching
-    in the OpenVINO Model Server (OVMS). vLLM is an easy-to-use open-source library that supports
-    efficient LLM inferencing and model serving.
-  * Ubuntu 24.04 is now officially supported.
+  * Support for Intel® Core Ultra Processors Series 2 (formerly codenamed Lunar Lake) on Windows.
+  * OpenVINO™ Model Server now comes with production-quality support for OpenAI-compatible API
+    which enables significantly higher throughput for parallel inferencing on Intel® Xeon®
+    processors when serving LLMs to many concurrent users.
+  * Improved performance and memory consumption with prefix caching, KV cache compression, and
+    other optimizations for serving LLMs using OpenVINO™ Model Server.
+  * Support for Python 3.12.
+  * Support for Red Hat Enterprise Linux (RHEL) version 9.3 - 9.4.
 
+Now deprecated
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
+* The following will not be available beyond the 2024.4 OpenVINO version:
 
-OpenVINO™ Runtime
-+++++++++++++++++++++++++++++
+  * The macOS x86_64 debug bins
+  * Python 3.8
+  * dKMB support
 
-Common
------------------------------
+* Intel® Streaming SIMD Extensions (Intel® SSE) will be supported in source code form, but not
+  enabled in the binary package by default, starting with OpenVINO 2025.0.
 
-* OpenVINO may now be used as a backend for vLLM, offering better CPU performance due to
-  fully-connected layer optimization, fusing multiple fully-connected layers (MLP), U8 KV cache,
-  and dynamic split fuse.
-* Ubuntu 24.04 is now officially supported, which means OpenVINO is now validated on this
-  system (preview support).
-* The following have been improved:
+|    Check the `deprecation section <#deprecation-and-support>`__ for more information.
 
-  * Increasing support for models like YoloV10 or PixArt-XL-2, thanks to enabling Squeeze and
-    Concat layers.
-  * Performance of precision conversion FP16/BF16 -> FP32.
 
 
+Common
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
-AUTO Inference Mode
------------------------------
+* Encryption and decryption of topology in model cache is now supported with callback functions
+  provided by the user (CPU only for now; ov::cache_encryption_callbacks).
+* The Ubuntu20 and Ubuntu22 Docker images now include the tokenizers and GenAI CPP modules,
+  including pre-installed Python modules, in development versions of these images.
+* Python 3.12 is now supported.
 
-* Model cache is now disabled for CPU acceleration even when cache_dir is set, because CPU
-  acceleration is skipped when the cached model is ready for the target device in the 2nd run.
+CPU Device Plugin
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
+* The following is now supported:
 
-Heterogeneous Inference Mode
------------------------------
-* PIPELINE_PARALLEL policy is now available, to inference large models on multiple devices per
-  available memory size, being especially useful for large language models that don't fit into
-  one discrete GPU (a preview feature).
+  * Tensor parallel feature for multi-socket CPU inference, with performance improvement for
+    LLMs with 6B+ parameters (enabled through model_distribution_policy hint configurations).
+  * RMSNorm operator, optimized with JIT kernel to improve both the 1st and 2nd token
+    performance of LLMs.
 
+* The following has been improved:
 
-CPU Device Plugin
------------------------------
+  * vLLM support, with PagedAttention exposing attention score as the second output. It can now
+    be used in the cache eviction algorithm to improve LLM serving performance.
+  * 1st token performance with Llama series of models, with additional CPU operator optimization
+    (such as MLP, SDPA) on BF16 precision.
+  * Default oneTBB version on Linux is now 2021.13.0, improving overall performance on latest
+    Intel XEON platforms.
+  * MXFP4 weight compression models (compressing weights to 4-bit with the e2m1 data type
+    without a zero point and with 8-bit e8m0 scales) have been optimized for Xeon platforms
+    thanks to fullyconnected compressed weight LLM support.
 
-* Fully Connected layers have been optimized together with RoPE optimization with JIT kernel to
-  improve performance for LLM serving workloads on Intel AMX platforms.
-* Dynamic quantization of Fully Connected layers is now enabled by default on Intel AVX2 and
-  AVX512 platforms, improving out-of-the-box performance for 8bit/4bit weight-compressed LLMs.
-* Performance has been improved for:
+* The following has been fixed:
 
-  * ARM server configuration, due to migration to Intel® oneAPI Threading Building Blocks 2021.13.
-  * ARM for FP32 and FP16.
+  * Memory leak when ov::num_streams value is 0.
+  * CPU affinity mask is changed after OpenVINO execution when OpenVINO is compiled
+    with -DTHREADING=SEQ.
 
 
 GPU Device Plugin
------------------------------
-
-* Performance has been improved for:
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
-  * LLMs and Stable Diffusion on discrete GPUs, due to latency decrease, through optimizations
-    such as Multi-Head Attention (MHA) and oneDNN improvements.
-  * Whisper models on discrete GPU.
+* Dynamic quantization for LLMs is now supported on discrete GPU platforms.
+* Stable Diffusion 3 is now supported with good accuracy on Intel GPU platforms.
+* Both first and second token latency for LLMs have been improved on Intel GPU platforms.
+* The issue of model cache not regenerating with the value changes of
+  ``ov::hint::performance_mode`` or ``ov::hint::dynamic_quantization_group_size`` has been
+  fixed.
 
 
 NPU Device Plugin
------------------------------
-
-* NPU inference of LLMs is now supported with GenAI API (preview feature). To support LLMs on
-  NPU (requires the most recent version of the NPU driver), additional relevant features are
-  also part of the NPU plugin now.
-* Models bigger than 2GB are now supported on both NPU driver
-  (Intel® NPU Driver - Windows* 32.0.100.2540) and NPU plugin side (both Linux and Windows).
-* Memory optimizations have been implemented:
-
-  * Weights are no longer copied from NPU compiler adapter.
-  * Improved memory and first-ever inference latency for inference on NPU.
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+* `Remote Tensor API <https://docs.openvino.ai/nightly/openvino-workflow/running-inference/inference-devices-and-modes/npu-device/remote-tensor-api-npu-plugin.html>`__
+  is now supported.
+* You can now query the available number of tiles (ov::intel_npu::max_tiles) and force a
+  specific number of tiles to be used by the model, per inference request
+  (ov::intel_npu::tiles). **Note:** ov::intel_npu::tiles overrides the default number of tiles
+  selected by the compiler based on performance hints (ov::hint::performance_mode). Any tile
+  number other than 1 may be a problem for cross platform compatibility, if not tested
+  explicitly versus the max_tiles value.
+* You can now bypass the model caching mechanism in the driver
+  (ov::intel_npu::bypass_umd_caching). Read more about driver and OpenVINO caching.
+* Memory footprint at model execution has been reduced by one blob (compiled model) size.
+  For execution, the plugin no longer retrieves the compiled model from the driver, it uses the
+  level zero graph handle directly, instead. The compiled model is now retrieved from the driver
+  only during the export method.
 
 
 OpenVINO Python API
------------------------------
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
-* visit_attributes is now available for custom operation implemented in Python, enabling
-  serialization of operation attributes.
-* Python API is now extended with new methods for Model class, e.g. Model.get_sink_index, new
-  overloads for Model.get_result_index.
+* Openvino.Tensor, when created in the shared memory mode, now prevents “garbage collection” of
+  numpy memory.
+* The ``openvino.experimental`` submodule is now available, providing access to experimental
+  functionalities under development.
+* New python-exclusive openvino.Model constructors have been added.
+* Image padding in PreProcessor is now available.
+* OpenVINO Runtime is now compatible with numpy 2.0.
 
 
 OpenVINO Node.js API
------------------------------
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
-* Tokenizers and StringTensor are now supported for LLM inference.
-* Compatibility with electron.js is now restored for desktop application developers.
-* Async version of Core.import_model and enhancements for Core.read_model methods are now
-  available, for more efficient model reading, especially for LLMs.
+* The following has been improved
 
-TensorFlow Framework Support
-----------------------------------
+  * Unit tests for increased efficiency and stability
+  * Security updates applied to dependencies
 
-* Models with keras.LSTM operations are now more performant in CPU inference.
-* The tensor list initialized with an undefined element shape value is now supported.
+* `Electron <https://www.electronjs.org/>`__
+  compatibility is now confirmed with new end-to-end tests.
+* `New API methods <https://docs.openvino.ai/2024/api/nodejs_api/nodejs_api.html>`__ added.
 
-TensorFlow Lite Framework Support
-----------------------------------
 
-* Constants containing spare tensors are now supported.
+TensorFlow Framework Support
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
-PyTorch Framework Support
------------------------------
+* TensorFlow 2.17.0 is now supported.
+* JAX 0.4.31 is now supported via a path of jax2tf with native_serialization=False
+* `8 NEW* operations <https://github.com/openvinotoolkit/openvino/blob/releases/2024/2/src/frontends/tensorflow/docs/supported_ops.md>`__
+  have been added.
+* Tensor lists with multiple undefined dimensions in element_shape are now supported, enabling
+  support for TF Hub lite0-detection/versions/1 model.
 
-* Setting types/shapes for nested structures (e.g., dictionaries and tuples) is now supported.
-* The aten::layer_norm has been updated to support dynamic shape normalization.
-* Dynamic shapes support in the FX graph has been improved, benefiting torch.compile and
-  torch.export based applications, improving performance for gemma and chatglm model
-  families.
 
-ONNX Framework Support
------------------------------
+PyTorch Framework Support
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
-* More models are now supported:
+* Torch 2.4 is now supported.
+* Inplace ops are now supported automatically if the regular version is supported.
+* Symmetric GPTQ model from Hugging Face will now be automatically converted to the signed type
+  (INT4) and zero-points will be removed.
 
-  * Models using the new version of the ReduceMean operation (introduced in ONNX opset 18).
-  * Models using the Multinomial operation (introduced in ONNX opset 7).
 
+ONNX Framework Support
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
-OpenVINO Model Server
-+++++++++++++++++++++++++++++
+* ONNX 1.16.0 is now supported
+* models with constants/inputs of uint4/int4 types are now supported.
+* 4 NEW operations have been added.
 
-* The following has been improved in OpenAI API text generation:
 
-  * Performance results, due to OpenVINO Runtime and sampling algorithms.
-  * Reporting generation engine metrics in the logs.
-  * Extra sampling parameters added.
-  * Request parameters affecting memory consumption now have value restrictions, within a
-    configurable range.
+OpenVINO Model Server
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
-* The following has been fixed in OpenAI API text generation:
+* OpenAI API for text generation is now officially supported and recommended for production
+  usage. It comes with the following new features:
 
-  * Generating streamer responses impacting incomplete utf-8 sequences.
-  * A sporadic generation hang.
-  * Incompatibility of the last response from the ``completions`` endpoint stream with the vLLM
-    benchmarking script.
+  * Prefix caching feature, caching the prompt evaluation to speed up text generation.
+  * Ability to compress the KV Cache to a lower precision, reducing memory consumption without
+    a significant loss of accuracy.
+  * ``stop`` sampling parameters, to define a sequence that stops text generation.
+  * ``logprobs`` sampling parameter, returning the probabilities to returned tokens, which can
+    be used to calculate the model perplexity metric, among other things.
+  * Generic metrics related to execution of the MediaPipe graph that can be used for autoscaling
+    based on the current load and the level of concurrency.
+  * `Demo of text generation horizontal scalability <https://github.com/openvinotoolkit/model_server/tree/main/demos/continuous_batching/scaling>`__
+    using basic docker containers and Kubernetes.
+  * Automatic cancelling of text generation for disconnected clients.
+  * Non-UTF-8 responses from the model can be now automatically changed to Unicode replacement
+    characters, due to their configurable handling.
+  * Support for Llama3.1 models.
 
+* The following has been improved:
 
+  * Handling of model templates without bos_token is now fixed.
+  * Performance of the multinomial sampling algorithm.
+  * ``finish_reason`` in the response correctly determines reaching max_tokens (length) and
+    completing the sequence (stop).
+  * Security and stability.
 
 
 
 Neural Network Compression Framework
-+++++++++++++++++++++++++++++++++++++
-
-* The `MXFP4 <https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf>`__
-  data format is now supported in the Weight Compression method, compressing weights to 4-bit
-  with the e2m1 data type without a zero point and with 8-bit e8m0 scales. This feature
-  is enabled by setting ``mode=CompressWeightsMode.E2M1`` in nncf.compress_weights().
-* The AWQ algorithm in the Weight Compression method has been extended for patterns:
-  Act->MatMul and Act->MUltiply->MatMul to cover the Phi family models.
-* The representation of symmetrically quantized weights has been updated to a signed data type
-  with no zero point. This allows NPU to support compressed LLMs with the symmetric mode.
-* BF16 models in Post-Training Quantization are now supported; nncf.quantize().
-* `Activation Sparsity <https://arxiv.org/abs/2310.17157>`__ (Contextual Sparsity) algorithm in
-  the Weight Compression method is now supported (preview), speeding up LLM inference.
-  The algorithm is enabled by setting the ``target_sparsity_by_scope`` option in
-  nncf.compress_weights() and supports Torch models only.
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+* The LoRA Correction algorithm is now included in the Weight Compression method, improving the
+  accuracy of INT4-compressed models on top of other data-aware algorithms, such as AWQ and
+  Scale Estimation. To enable it, set the lora_correction option to True in
+  nncf.compress_weights().
+* The GPTQ compression algorithm can now be combined with the Scale Estimation algorithm,
+  making it possible to run GPTQ, AWQ, and Scale Estimation together, for the optimum-accuracy
+  INT4-compressed models.
+* INT8 quantization of LSTMSequence and Convolution operations for constant inputs is now
+  enabled, resulting in better performance and reduced model size.
 
 
 OpenVINO Tokenizers
-+++++++++++++++++++++++++++++++++++++
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
-* The following is now supported:
+* Split and BPE tokenization operations have been reimplemented, resulting in improved
+  tokenization accuracy and performance.
+* New building options are now available, offering up to a 12x reduction in binary size.
+* An operation is now available to validate and skip/replace model-generated non-Unicode
+  bytecode sequences during detokenization.
 
-  * Full Regex syntax with the PCRE2 library for text normalization and splitting.
-  * Left padding side for all tokenizer types.
+OpenVINO.GenAI
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
-* GLM-4 tokenizer support, as well as detokenization support for Phi-3 and Gemma have been
-  improved.
+* New samples and pipelines are now available:
 
+  * An example IterableStreamer implementation in
+    `multinomial_causal_lm/python sample <https://github.com/openvinotoolkit/openvino.genai/tree/master/samples/python/multinomial_causal_lm>`__
 
+* GenAI compilation is now available as part of OpenVINO via the –DOPENVINO_EXTRA_MODULES CMake
+  option.
 
 
 
 Other Changes and Known Issues
-+++++++++++++++++++++++++++++++++++++
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
 Jupyter Notebooks
 -----------------------------
 
-* `Stable Diffusion V3 <https://openvinotoolkit.github.io/openvino_notebooks/?search=Image+generation+with+Stable+Diffusion+v3+and+OpenVINO>`__
-* `Depth Anything V2 <https://openvinotoolkit.github.io/openvino_notebooks/?search=Depth+estimation+with+DepthAnythingV2+and+OpenVINO>`__
-* `RAG System with LLamaIndex <https://openvinotoolkit.github.io/openvino_notebooks/?search=Create+a+RAG+system+using+OpenVINO+and+LlamaIndex>`__
-* `Image Synthesis with Pixart <https://openvinotoolkit.github.io/openvino_notebooks/?search=PixArt-%CE%B1%3A+Fast+Training+of+Diffusion+Transformer+for+Photorealistic+Text-to-Image+Synthesis+with+OpenVINO>`__
-* `Function calling LLM agent with Qwen-Agent <https://openvinotoolkit.github.io/openvino_notebooks/?search=Create+Function-calling+Agent+using+OpenVINO+and+Qwen-Agent>`__
-* `Jina-CLIP <https://openvinotoolkit.github.io/openvino_notebooks/?search=CLIP+model+with+Jina+CLIP+and+OpenVINO>`__
-* `MiniCPM -V2 Visual Language Assistant <https://openvinotoolkit.github.io/openvino_notebooks/?search=Visual-language+assistant+with+MiniCPM-V2+and+OpenVINO>`__
-* `OpenVINO XAI: first steps <https://openvinotoolkit.github.io/openvino_notebooks/?search=eXplainable+AI+%28XAI%29+for+OpenVINO%E2%84%A2+IR+Models>`__
-* `OpenVINO XAI: deep dive <https://openvinotoolkit.github.io/openvino_notebooks/?search=OpenVINO%E2%84%A2+Explainable+AI+Toolkit%3A+Deep+Dive+notebook>`__
-* `LLM Agent with LLamaIndex <https://openvinotoolkit.github.io/openvino_notebooks/?search=Create+an+Agentic+RAG+using+OpenVINO+and+LlamaIndex>`__
-* `Stable Audio <https://openvinotoolkit.github.io/openvino_notebooks/?search=stable+audio>`__
-* `Phi-3-vision <https://openvinotoolkit.github.io/openvino_notebooks/?search=Phi3-Vision>`__
+* `Florence-2 <https://openvinotoolkit.github.io/openvino_notebooks/?search=Florence-2%3A+Open+Source+Vision+Foundation+Model>`__
+* `NuExtract: Structure Extraction <https://openvinotoolkit.github.io/openvino_notebooks/?search=Structure+Extraction+with+NuExtract+and+OpenVINO>`__
+* `Flux.1 Image Generation <https://openvinotoolkit.github.io/openvino_notebooks/?search=Image+generation+with+Flux.1+and+OpenVINO>`__
+* `PixArt-α: Photorealistic Text-to-Image Synthesis <https://openvinotoolkit.github.io/openvino_notebooks/?search=PixArt-%CE%B1%3A+Fast+Training+of+Diffusion+Transformer+for+Photorealistic+Text-to-Image+Synthesis+with+OpenVINO>`__
+* `Phi-3-Vision Visual Language Assistant <https://openvinotoolkit.github.io/openvino_notebooks/?search=Visual-language+assistant+with+Phi3-Vision+and+OpenVINO>`__
+* `MiniCPMV2.6 <https://openvinotoolkit.github.io/openvino_notebooks/?search=Visual-language+assistant+with+MiniCPM-V2+and+OpenVINO>`__
+* `InternVL2 <https://openvinotoolkit.github.io/openvino_notebooks/?search=Visual-language+assistant+with+InternVL2+and+OpenVINO>`__
+* The list of supported models in
+  `LLM chatbot <https://openvinotoolkit.github.io/openvino_notebooks/?search=Create+an+LLM-powered+Chatbot+using+OpenVINO+Generate+API>`__
+  now includes Phi3.5, Gemma2 support
 
-OpenVINO.GenAI
+Known Issues
 -----------------------------
 
-* Performance counters have been added.
-* Preview support for NPU is now available.
+| **Component: CPU**
+| ID: CVS-150542, CVS-145996
+| Description:
+|   The upgrade of default oneTBB on Linux platforms to 2021.13.0 improves overall
+    performance on latest Intel XEON platform but causes regression in some cases. Limit the
+    threads usage of postprocessing done by Torch can mitigate the regression (For example:
+    torch.set_num_threads(n), n can be 1, beam search number, prompt batch size or other
+    numbers).
+
+| **Component: OpenVINO.Genai**
+| ID: 149694
+| Description:
+|   Passing openvino.Tensor instance to LLMPipleine triggers incompatible arguments error if
+    OpenVINO and GenAI are installed from PyPI on Windows.
 
-Hugging Face
------------------------------
+| **Component: OpenVINO.Genai**
+| ID: 148308
+| Description:
+|   OpenVINO.GenAI archive doesn't have debug libraries for OpenVINO Tokenizers and
+    OpenVINO.GenAI.
 
-OpenVINO pre-optimized models are now available on Hugging Face:
 
-* Phi-3-mini-128k-instruct (
-  `INT4 <https://huggingface.co/OpenVINO/Phi-3-mini-128k-instruct-int4-ov>`__,
-  `INT8 <https://huggingface.co/OpenVINO/Phi-3-mini-128k-instruct-int8-ov>`__,
-  `FP16 <https://huggingface.co/OpenVINO/Phi-3-mini-128k-instruct-fp16-ov>`__)
-* Mistral-7B-Instruct-v0.2 (
-  `INT4 <https://huggingface.co/OpenVINO/Mistral-7B-Instruct-v0.2-int4-ov>`__,
-  `INT8 <https://huggingface.co/OpenVINO/Mistral-7B-Instruct-v0.2-int8-ov>`__,
-  `FP16 <https://huggingface.co/OpenVINO/Mistral-7B-Instruct-v0.2-fp16-ov>`__)
-* Mixtral-8x7b-Instruct-v0.1 (
-  `INT4 <https://huggingface.co/OpenVINO/mixtral-8x7b-instruct-v0.1-int4-ov>`__,
-  `INT8 <https://huggingface.co/OpenVINO/Mixtral-8x7B-Instruct-v0.1-int8-ov>`__)
-* LCM_Dreamshaper_v7 (
-  `INT8 <https://huggingface.co/OpenVINO/LCM_Dreamshaper_v7-int8-ov>`__,
-  `FP16 <https://huggingface.co/OpenVINO/LCM_Dreamshaper_v7-fp16-ov>`__)
-* starcoder2-7b (
-  `INT4 <https://huggingface.co/OpenVINO/starcoder2-7b-int4-ov>`__,
-  `INT8 <https://huggingface.co/OpenVINO/starcoder2-7b-int8-ov>`__,
-  `FP16 <https://huggingface.co/OpenVINO/starcoder2-7b-fp16-ov>`__)
-* For all the models see `HuggingFace <https://huggingface.co/OpenVINO>`__
 
 
+Previous 2024 releases
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
+.. ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+.. ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
-Known Issues
------------------------------
+.. dropdown:: 2024.3 - 31 July 2024
+   :animate: fade-in-slide-down
+   :color: secondary
 
-| **Component: OpenVINO.GenAI**
-| ID: 148308
-| Description:
-|   The OpenVINO.GenAI archive distribution doesn't include debug libraries for OpenVINO
-    Tokenizers and OpenVINO.GenAI.
+   **What's new**
 
-| **Component: GPU**
-| ID: 146283
-| Description:
-|   For some LLM models, longer prompts, such as several thousand tokens, may result in
-    decreased accuracy on the GPU plugin.
-| Workaround:
-|   It is recommended to run the model in the FP32 precision to avoid the issue.
+   * More Gen AI coverage and framework integrations to minimize code changes.
 
+     * OpenVINO pre-optimized models are now available in Hugging Face making it easier for developers
+       to get started with these models.
 
+   * Broader Large Language Model (LLM) support and more model compression techniques.
 
+     * Significant improvement in LLM performance on Intel discrete GPUs with the addition of
+       Multi-Head Attention (MHA) and OneDNN enhancements.
 
+   * More portability and performance to run AI at the edge, in the cloud, or locally.
 
+     * Improved CPU performance when serving LLMs with the inclusion of vLLM and continuous batching
+       in the OpenVINO Model Server (OVMS). vLLM is an easy-to-use open-source library that supports
+       efficient LLM inferencing and model serving.
+     * Ubuntu 24.04 is now officially supported.
 
-Previous 2024 releases
-+++++++++++++++++++++++++++++
+   **OpenVINO™ Runtime**
+
+   *Common*
+
+   * OpenVINO may now be used as a backend for vLLM, offering better CPU performance due to
+     fully-connected layer optimization, fusing multiple fully-connected layers (MLP), U8 KV cache,
+     and dynamic split fuse.
+   * Ubuntu 24.04 is now officially supported, which means OpenVINO is now validated on this
+     system (preview support).
+   * The following have been improved:
+
+     * Increasing support for models like YoloV10 or PixArt-XL-2, thanks to enabling Squeeze and
+       Concat layers.
+     * Performance of precision conversion FP16/BF16 -> FP32.
+
+   *AUTO Inference Mode*
+
+   * Model cache is now disabled for CPU acceleration even when cache_dir is set, because CPU
+     acceleration is skipped when the cached model is ready for the target device in the 2nd run.
+
+   *Heterogeneous Inference Mode*
+
+   * PIPELINE_PARALLEL policy is now available, to inference large models on multiple devices per
+     available memory size, being especially useful for large language models that don't fit into
+     one discrete GPU (a preview feature).
+
+   *CPU Device Plugin*
+
+   * Fully Connected layers have been optimized together with RoPE optimization with JIT kernel to
+     improve performance for LLM serving workloads on Intel AMX platforms.
+   * Dynamic quantization of Fully Connected layers is now enabled by default on Intel AVX2 and
+     AVX512 platforms, improving out-of-the-box performance for 8bit/4bit weight-compressed LLMs.
+   * Performance has been improved for:
+
+     * ARM server configuration, due to migration to Intel® oneAPI Threading Building Blocks 2021.13.
+     * ARM for FP32 and FP16.
+
+   *GPU Device Plugin*
+
+   * Performance has been improved for:
+
+     * LLMs and Stable Diffusion on discrete GPUs, due to latency decrease, through optimizations
+       such as Multi-Head Attention (MHA) and oneDNN improvements.
+     * Whisper models on discrete GPU.
+
+
+   *NPU Device Plugin*
+
+   * NPU inference of LLMs is now supported with GenAI API (preview feature). To support LLMs on
+     NPU (requires the most recent version of the NPU driver), additional relevant features are
+     also part of the NPU plugin now.
+   * Models bigger than 2GB are now supported on both NPU driver
+     (Intel® NPU Driver - Windows* 32.0.100.2540) and NPU plugin side (both Linux and Windows).
+   * Memory optimizations have been implemented:
+
+     * Weights are no longer copied from NPU compiler adapter.
+     * Improved memory and first-ever inference latency for inference on NPU.
+
+   *OpenVINO Python API*
+
+   * visit_attributes is now available for custom operation implemented in Python, enabling
+     serialization of operation attributes.
+   * Python API is now extended with new methods for Model class, e.g. Model.get_sink_index, new
+     overloads for Model.get_result_index.
+
+   *OpenVINO Node.js API*
+
+   * Tokenizers and StringTensor are now supported for LLM inference.
+   * Compatibility with electron.js is now restored for desktop application developers.
+   * Async version of Core.import_model and enhancements for Core.read_model methods are now
+     available, for more efficient model reading, especially for LLMs.
+
+   *TensorFlow Framework Support*
+
+   * Models with keras.LSTM operations are now more performant in CPU inference.
+   * The tensor list initialized with an undefined element shape value is now supported.
+
+   *TensorFlow Lite Framework Support*
+
+   * Constants containing spare tensors are now supported.
+
+   *PyTorch Framework Support*
+
+   * Setting types/shapes for nested structures (e.g., dictionaries and tuples) is now supported.
+   * The aten::layer_norm has been updated to support dynamic shape normalization.
+   * Dynamic shapes support in the FX graph has been improved, benefiting torch.compile and
+     torch.export based applications, improving performance for gemma and chatglm model
+     families.
+
+   *ONNX Framework Support*
+
+   * More models are now supported:
+
+     * Models using the new version of the ReduceMean operation (introduced in ONNX opset 18).
+     * Models using the Multinomial operation (introduced in ONNX opset 7).
+
+
+   **OpenVINO Model Server**
+
+   * The following has been improved in OpenAI API text generation:
+
+     * Performance results, due to OpenVINO Runtime and sampling algorithms.
+     * Reporting generation engine metrics in the logs.
+     * Extra sampling parameters added.
+     * Request parameters affecting memory consumption now have value restrictions, within a
+       configurable range.
+
+   * The following has been fixed in OpenAI API text generation:
+
+     * Generating streamer responses impacting incomplete utf-8 sequences.
+     * A sporadic generation hang.
+     * Incompatibility of the last response from the ``completions`` endpoint stream with the vLLM
+       benchmarking script.
+
+   **Neural Network Compression Framework**
+
+   * The `MXFP4 <https://www.opencompute.org/documents/ocp-microscaling-formats-mx-v1-0-spec-final-pdf>`__
+     data format is now supported in the Weight Compression method, compressing weights to 4-bit
+     with the e2m1 data type without a zero point and with 8-bit e8m0 scales. This feature
+     is enabled by setting ``mode=CompressWeightsMode.E2M1`` in nncf.compress_weights().
+   * The AWQ algorithm in the Weight Compression method has been extended for patterns:
+     Act->MatMul and Act->MUltiply->MatMul to cover the Phi family models.
+   * The representation of symmetrically quantized weights has been updated to a signed data type
+     with no zero point. This allows NPU to support compressed LLMs with the symmetric mode.
+   * BF16 models in Post-Training Quantization are now supported; nncf.quantize().
+   * `Activation Sparsity <https://arxiv.org/abs/2310.17157>`__ (Contextual Sparsity) algorithm in
+     the Weight Compression method is now supported (preview), speeding up LLM inference.
+     The algorithm is enabled by setting the ``target_sparsity_by_scope`` option in
+     nncf.compress_weights() and supports Torch models only.
+
+
+   **OpenVINO Tokenizers**
+
+   * The following is now supported:
+
+     * Full Regex syntax with the PCRE2 library for text normalization and splitting.
+     * Left padding side for all tokenizer types.
+
+   * GLM-4 tokenizer support, as well as detokenization support for Phi-3 and Gemma have been
+     improved.
+
+
+
+
+
+   **Other Changes and Known Issues**
+
+   *Jupyter Notebooks*
+
+   * `Stable Diffusion V3 <https://openvinotoolkit.github.io/openvino_notebooks/?search=Image+generation+with+Stable+Diffusion+v3+and+OpenVINO>`__
+   * `Depth Anything V2 <https://openvinotoolkit.github.io/openvino_notebooks/?search=Depth+estimation+with+DepthAnythingV2+and+OpenVINO>`__
+   * `RAG System with LLamaIndex <https://openvinotoolkit.github.io/openvino_notebooks/?search=Create+a+RAG+system+using+OpenVINO+and+LlamaIndex>`__
+   * `Image Synthesis with Pixart <https://openvinotoolkit.github.io/openvino_notebooks/?search=PixArt-%CE%B1%3A+Fast+Training+of+Diffusion+Transformer+for+Photorealistic+Text-to-Image+Synthesis+with+OpenVINO>`__
+   * `Function calling LLM agent with Qwen-Agent <https://openvinotoolkit.github.io/openvino_notebooks/?search=Create+Function-calling+Agent+using+OpenVINO+and+Qwen-Agent>`__
+   * `Jina-CLIP <https://openvinotoolkit.github.io/openvino_notebooks/?search=CLIP+model+with+Jina+CLIP+and+OpenVINO>`__
+   * `MiniCPM -V2 Visual Language Assistant <https://openvinotoolkit.github.io/openvino_notebooks/?search=Visual-language+assistant+with+MiniCPM-V2+and+OpenVINO>`__
+   * `OpenVINO XAI: first steps <https://openvinotoolkit.github.io/openvino_notebooks/?search=eXplainable+AI+%28XAI%29+for+OpenVINO%E2%84%A2+IR+Models>`__
+   * `OpenVINO XAI: deep dive <https://openvinotoolkit.github.io/openvino_notebooks/?search=OpenVINO%E2%84%A2+Explainable+AI+Toolkit%3A+Deep+Dive+notebook>`__
+   * `LLM Agent with LLamaIndex <https://openvinotoolkit.github.io/openvino_notebooks/?search=Create+an+Agentic+RAG+using+OpenVINO+and+LlamaIndex>`__
+   * `Stable Audio <https://openvinotoolkit.github.io/openvino_notebooks/?search=stable+audio>`__
+   * `Phi-3-vision <https://openvinotoolkit.github.io/openvino_notebooks/?search=Phi3-Vision>`__
+
+   *OpenVINO.GenAI*
+
+   * Performance counters have been added.
+   * Preview support for NPU is now available.
+
+   *Hugging Face*
+
+   OpenVINO pre-optimized models are now available on Hugging Face:
+
+   * Phi-3-mini-128k-instruct (
+     `INT4 <https://huggingface.co/OpenVINO/Phi-3-mini-128k-instruct-int4-ov>`__,
+     `INT8 <https://huggingface.co/OpenVINO/Phi-3-mini-128k-instruct-int8-ov>`__,
+     `FP16 <https://huggingface.co/OpenVINO/Phi-3-mini-128k-instruct-fp16-ov>`__)
+   * Mistral-7B-Instruct-v0.2 (
+     `INT4 <https://huggingface.co/OpenVINO/Mistral-7B-Instruct-v0.2-int4-ov>`__,
+     `INT8 <https://huggingface.co/OpenVINO/Mistral-7B-Instruct-v0.2-int8-ov>`__,
+     `FP16 <https://huggingface.co/OpenVINO/Mistral-7B-Instruct-v0.2-fp16-ov>`__)
+   * Mixtral-8x7b-Instruct-v0.1 (
+     `INT4 <https://huggingface.co/OpenVINO/mixtral-8x7b-instruct-v0.1-int4-ov>`__,
+     `INT8 <https://huggingface.co/OpenVINO/Mixtral-8x7B-Instruct-v0.1-int8-ov>`__)
+   * LCM_Dreamshaper_v7 (
+     `INT8 <https://huggingface.co/OpenVINO/LCM_Dreamshaper_v7-int8-ov>`__,
+     `FP16 <https://huggingface.co/OpenVINO/LCM_Dreamshaper_v7-fp16-ov>`__)
+   * starcoder2-7b (
+     `INT4 <https://huggingface.co/OpenVINO/starcoder2-7b-int4-ov>`__,
+     `INT8 <https://huggingface.co/OpenVINO/starcoder2-7b-int8-ov>`__,
+     `FP16 <https://huggingface.co/OpenVINO/starcoder2-7b-fp16-ov>`__)
+   * For all the models see `HuggingFace <https://huggingface.co/OpenVINO>`__
+
+
+
+
+   *Known Issues*
+
+   | **Component: OpenVINO.GenAI**
+   | ID: 148308
+   | Description:
+   |   The OpenVINO.GenAI archive distribution doesn't include debug libraries for OpenVINO
+       Tokenizers and OpenVINO.GenAI.
+
+   | **Component: GPU**
+   | ID: 146283
+   | Description:
+   |   For some LLM models, longer prompts, such as several thousand tokens, may result in
+       decreased accuracy on the GPU plugin.
+   | Workaround:
+   |   It is recommended to run the model in the FP32 precision to avoid the issue.
+
+
+
+
+
+.. ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+.. ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
 .. dropdown:: 2024.2 - 17 June 2024
    :animate: fade-in-slide-down
@@ -522,6 +780,8 @@ Previous 2024 releases
 
 
 
+.. ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+.. ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 
 .. dropdown:: 2024.1 - 24 April 2024
    :animate: fade-in-slide-down
@@ -1041,8 +1301,18 @@ Discontinued in 2024
 Deprecated and to be removed in the future
 --------------------------------------------
 
+* The macOS x86_64 debug bins will no longer be provided with the OpenVINO toolkit, starting
+  with OpenVINO 2024.5.
+* Python 3.8 is now considered deprecated, and it will not be available beyond the 2024.4
+  OpenVINO version.
+* dKMB support is now considered deprecated and will be fully removed with OpenVINO 2024.5
+* Intel® Streaming SIMD Extensions (Intel® SSE) will be supported in source code form, but not
+  enabled in the binary package by default, starting with OpenVINO 2025.0
+* The openvino-nightly PyPI module will soon be discontinued. End-users should proceed with the
+  Simple PyPI nightly repo instead. More information in
+  `Release Policy <https://docs.openvino.ai/2024/about-openvino/release-notes-openvino/release-policy.html#nightly-releases>`__.
 * The OpenVINO™ Development Tools package (pip install openvino-dev) will be removed from
-  installation options and distribution channels beginning with OpenVINO 2025.
+  installation options and distribution channels beginning with OpenVINO 2025.0.
 * Model Optimizer will be discontinued with OpenVINO 2025.0. Consider using the
   :doc:`new conversion methods <../openvino-workflow/model-preparation/convert-model-to-ir>`
   instead. For more details, see the
@@ -1165,26 +1435,20 @@ are available on request.
 
 Intel technologies' features and benefits depend on system configuration and may require
 enabled hardware, software or service activation. Learn more at
-`http://www.intel.com/ <http://www.intel.com/>`__
+`www.intel.com <http://www.intel.com/>`__
 or from the OEM or retailer.
 
 No computer system can be absolutely secure.
 
-Intel, Atom, Arria, Core, Movidius, Xeon, OpenVINO, and the Intel logo are trademarks
+Intel, Atom, Core, Xeon, OpenVINO, and the Intel logo are trademarks
 of Intel Corporation in the U.S. and/or other countries.
 
-OpenCL and the OpenCL logo are trademarks of Apple Inc. used by permission by Khronos
-
 Other names and brands may be claimed as the property of others.
 
 Copyright © 2024, Intel Corporation. All rights reserved.
 
 For more complete information about compiler optimizations, see our Optimization Notice.
 
-Performance varies by use, configuration and other factors. Learn more at
-`www.Intel.com/PerformanceIndex <www.Intel.com/PerformanceIndex>`__.
-
-
-
+Performance varies by use, configuration and other factors.
 
 
diff --git a/docs/articles_en/about-openvino/release-notes-openvino/system-requirements.rst b/docs/articles_en/about-openvino/release-notes-openvino/system-requirements.rst
index a224bf39427fab..d44e57bf7215c2 100644
--- a/docs/articles_en/about-openvino/release-notes-openvino/system-requirements.rst
+++ b/docs/articles_en/about-openvino/release-notes-openvino/system-requirements.rst
@@ -24,7 +24,7 @@ CPU
       * Intel Atom® processor with Intel® SSE4.2 support
       * Intel® Pentium® processor N4200/5, N3350/5, N3450/5 with Intel® HD Graphics
       * 6th - 14th generation Intel® Core™ processors
-      * Intel® Core™ Ultra (codename Meteor Lake)
+      * Intel® Core™ Ultra Series 1 and Series 2 (Windows only)
       * 1st - 5th generation Intel® Xeon® Scalable Processors
       * ARM CPUs with armv7a and higher, ARM64 CPUs with arm64-v8a and higher, Apple® Mac with Apple silicon
 
@@ -33,12 +33,11 @@ CPU
       * Ubuntu 24.04 long-term support (LTS), 64-bit (Kernel 6.8+) (preview support)
       * Ubuntu 22.04 long-term support (LTS), 64-bit (Kernel 5.15+)
       * Ubuntu 20.04 long-term support (LTS), 64-bit (Kernel 5.15+)
-      * Ubuntu 18.04 long-term support (LTS) with limitations, 64-bit (Kernel 5.4+)
       * Windows 10, 64-bit
       * Windows 11, 64-bit
       * macOS 12.6 and above, 64-bit and ARM64
       * CentOS 7
-      * Red Hat Enterprise Linux 8, 64-bit
+      * Red Hat Enterprise Linux 9.3-9.4, 64-bit
       * openSUSE Tumbleweed, 64-bit and ARM64
       * Ubuntu 20.04 ARM64
 
@@ -60,13 +59,13 @@ GPU
 
    .. tab-item::  Supported Operating Systems
 
-      * Ubuntu 24.04 long-term support (LTS), 64-bit (preview support)
+      * Ubuntu 24.04 long-term support (LTS), 64-bit
       * Ubuntu 22.04 long-term support (LTS), 64-bit
       * Ubuntu 20.04 long-term support (LTS), 64-bit
       * Windows 10, 64-bit
       * Windows 11, 64-bit
       * CentOS 7
-      * Red Hat Enterprise Linux 8, 64-bit
+      * Red Hat Enterprise Linux 9.3-9.4, 64-bit
 
    .. tab-item:: Additional considerations
 
@@ -78,10 +77,8 @@ GPU
       * While this release of OpenVINO supports Ubuntu 20.04, the driver stack
         for Intel discrete graphic cards does not fully support Ubuntu 20.04.
         We recommend using Ubuntu 22.04 and later when executing on discrete graphics.
-      * The following minimum (i.e., used for old hardware) OpenCL™ driver's versions
-        were used during OpenVINO internal validation: 22.43 for Ubuntu 22.04, 21.48
-        for Ubuntu 20.04 and 21.49 for Red Hat Enterprise Linux 8 (some hardware may require
-        higher versions of drivers).
+      * OpenCL™ driver versions required may vary, depending on hardware and operating Systems
+        used. Consult driver documentation to select the best version for your setup.
 
 Intel® Neural Processing Unit
 ################################
@@ -110,14 +107,14 @@ Operating systems and developer environment
 
    .. tab-item:: Linux OS
 
-      * Ubuntu 24.04 with Linux kernel 6.8+ (preview support)
+      * Ubuntu 24.04 with Linux kernel 6.8+
       * Ubuntu 22.04 with Linux kernel 5.15+
       * Ubuntu 20.04 with Linux kernel 5.15+
-      * Red Hat Enterprise Linux 8 with Linux kernel 5.4
+      * Red Hat Enterprise Linux 9.3-9.4 with Linux kernel 5.4
 
       Build environment components:
 
-      * Python* 3.8-3.11
+      * Python 3.8-3.12
       * `Intel® HD Graphics Driver <https://downloadcenter.intel.com/product/80939/Graphics-Drivers>`__
         required for inference on GPU
       * GNU Compiler Collection and CMake are needed for building from source:
@@ -135,7 +132,7 @@ Operating systems and developer environment
 
       * `Microsoft Visual Studio 2019 <https://visualstudio.microsoft.com/vs/older-downloads/>`__
       * `CMake <https://cmake.org/download/>`__ 3.16 or higher
-      * `Python <http://www.python.org/downloads/>`__ 3.8-3.11
+      * `Python <http://www.python.org/downloads/>`__ 3.8-3.12
       * `Intel® HD Graphics Driver <https://downloadcenter.intel.com/product/80939/Graphics-Drivers>`__
         required for inference on GPU
 
@@ -147,13 +144,15 @@ Operating systems and developer environment
 
       * `Xcode <https://developer.apple.com/xcode/>`__ 10.3
       * `CMake <https://cmake.org/download/>`__ 3.13 or higher
-      * `Python <http://www.python.org/downloads/>`__ 3.8-3.11
+      * `Python <http://www.python.org/downloads/>`__ 3.8-3.12
 
-   .. tab-item:: DL frameworks versions:
+   .. tab-item:: DL framework versions:
 
-      * TensorFlow 1.15.5, 2.16.1
-      * ONNX 1.15
+      * TensorFlow 1.15.5 - 2.17
+      * PyTorch 2.4
+      * ONNX 1.16
       * PaddlePaddle 2.6
+      * JAX 0.4.31 (via a path of jax2tf with native_serialization=False)
 
       This package can be installed on other versions of DL Frameworks
       but only the versions specified here are fully validated.
@@ -165,45 +164,5 @@ Operating systems and developer environment
 
 
 
-
-Legal Information
-+++++++++++++++++++++++++++++++++++++++++++++
-
-You may not use or facilitate the use of this document in connection with any infringement
-or other legal analysis concerning Intel products described herein.
-
-You agree to grant Intel a non-exclusive, royalty-free license to any patent claim
-thereafter drafted which includes subject matter disclosed herein.
-
-No license (express or implied, by estoppel or otherwise) to any intellectual property
-rights is granted by this document.
-
-All information provided here is subject to change without notice. Contact your Intel
-representative to obtain the latest Intel product specifications and roadmaps.
-
-The products described may contain design defects or errors known as errata which may
-cause the product to deviate from published specifications. Current characterized errata
-are available on request.
-
-Intel technologies' features and benefits depend on system configuration and may require
-enabled hardware, software or service activation. Learn more at
-`http://www.intel.com/ <http://www.intel.com/>`__
-or from the OEM or retailer.
-
-No computer system can be absolutely secure.
-
-Intel, Atom, Arria, Core, Movidius, Xeon, OpenVINO, and the Intel logo are trademarks
-of Intel Corporation in the U.S. and/or other countries.
-
-OpenCL and the OpenCL logo are trademarks of Apple Inc. used by permission by Khronos
-
-Other names and brands may be claimed as the property of others.
-
-Copyright © 2024, Intel Corporation. All rights reserved.
-
-For more complete information about compiler optimizations, see our Optimization Notice.
-
-Performance varies by use, configuration and other factors. Learn more at
-`www.Intel.com/PerformanceIndex <www.Intel.com/PerformanceIndex>`__.
-
-
+The claims stated here may not apply to all use cases and setups. See
+:doc:`Legal notices and terms of use <../additional-resources/terms-of-use>` for more information.
\ No newline at end of file
diff --git a/docs/sphinx_setup/api/api_reference.rst b/docs/sphinx_setup/api/api_reference.rst
index ac59abca93db7b..acf816364ca0fc 100644
--- a/docs/sphinx_setup/api/api_reference.rst
+++ b/docs/sphinx_setup/api/api_reference.rst
@@ -4,7 +4,8 @@ API Reference
 -------------
 
 .. meta::
-   :description: Explore the features of Python, C, C++ APIs in Intel® Distribution of OpenVINO™ Toolkit.
+   :description: Explore the features of Python, C, C++, and Node.js APIs in the Intel®
+                 Distribution of OpenVINO™ Toolkit.
 
 .. toctree::
    :maxdepth: 2
diff --git a/docs/sphinx_setup/api/nodejs_api/nodejs_api.rst b/docs/sphinx_setup/api/nodejs_api/nodejs_api.rst
index f71424d05294dd..995ceb3d29c003 100644
--- a/docs/sphinx_setup/api/nodejs_api/nodejs_api.rst
+++ b/docs/sphinx_setup/api/nodejs_api/nodejs_api.rst
@@ -37,6 +37,18 @@ For more details, refer to the
 <https://github.com/openvinotoolkit/openvino/blob/master/src/bindings/js/docs/README.md#openvino-node-package-developer-documentation>`__
 
 
+
+API Development
+#####################
+
+OpenVINO 2024.4 has introduced the following methods:
+
+-	Model.clone()
+-	Model.getoutputelementtype()
+-	CompiledModel getProperty()
+-	CompiledModel.setProperty()
+
+
 Additional Resources
 #####################
 

From 6c9d7962aa79d6ade904934f07affd51e12fc800 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Hubert=20B=C5=82aszczyk?=
 <56601011+hub-bla@users.noreply.github.com>
Date: Wed, 18 Sep 2024 20:12:48 +0200
Subject: [PATCH 07/49] [JAX FE]:Support jax.lax.ge and jax.lax.gt operation
 for JAX (#26640)

### Details:
 - support jax.lax.ge and jax.lax.gt operation
 - create unit tests

### Tickets:
 - [None](https://github.com/openvinotoolkit/openvino/issues/26572)

---------

Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com>
---
 src/frontends/jax/src/op/binary_op.cpp        | 34 ++++++++++++
 src/frontends/jax/src/op_table.cpp            |  5 ++
 .../jax_tests/test_binary_comparison.py       | 55 +++++++++++++++++++
 3 files changed, 94 insertions(+)
 create mode 100644 src/frontends/jax/src/op/binary_op.cpp
 create mode 100644 tests/layer_tests/jax_tests/test_binary_comparison.py

diff --git a/src/frontends/jax/src/op/binary_op.cpp b/src/frontends/jax/src/op/binary_op.cpp
new file mode 100644
index 00000000000000..9fd5cc12525b89
--- /dev/null
+++ b/src/frontends/jax/src/op/binary_op.cpp
@@ -0,0 +1,34 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "openvino/frontend/jax/node_context.hpp"
+#include "openvino/op/greater.hpp"
+#include "openvino/op/greater_eq.hpp"
+#include "utils.hpp"
+
+using namespace std;
+using namespace ov;
+using namespace ov::op;
+
+namespace ov {
+namespace frontend {
+namespace jax {
+namespace op {
+
+template <typename T>
+OutputVector translate_binary_op(const NodeContext& context) {
+    num_inputs_check(context, 2, 2);
+    auto lhs = context.get_input(0);
+    auto rhs = context.get_input(1);
+    Output<Node> binary_op = make_shared<T>(lhs, rhs);
+    return {binary_op};
+}
+
+template OutputVector translate_binary_op<v1::GreaterEqual>(const NodeContext& context);
+template OutputVector translate_binary_op<v1::Greater>(const NodeContext& context);
+
+}  // namespace op
+}  // namespace jax
+}  // namespace frontend
+}  // namespace ov
diff --git a/src/frontends/jax/src/op_table.cpp b/src/frontends/jax/src/op_table.cpp
index f7db07afe67f36..c0bcb85c724dbb 100644
--- a/src/frontends/jax/src/op_table.cpp
+++ b/src/frontends/jax/src/op_table.cpp
@@ -8,6 +8,8 @@
 #include "openvino/op/divide.hpp"
 #include "openvino/op/erf.hpp"
 #include "openvino/op/exp.hpp"
+#include "openvino/op/greater.hpp"
+#include "openvino/op/greater_eq.hpp"
 #include "openvino/op/maximum.hpp"
 #include "openvino/op/multiply.hpp"
 #include "openvino/op/reduce_max.hpp"
@@ -29,6 +31,7 @@ namespace op {
     template <class T>     \
     OutputVector op(const ov::frontend::jax::NodeContext& node)
 
+OP_T_CONVERTER(translate_binary_op);
 OP_CONVERTER(translate_broadcast_in_dim);
 OP_CONVERTER(translate_concatenate);
 OP_CONVERTER(translate_constant);
@@ -62,6 +65,8 @@ const std::map<std::string, CreatorFunction> get_supported_ops_jaxpr() {
             {"dot_general", op::translate_dot_general},
             {"erf", op::translate_1to1_match_1_input<v0::Erf>},
             {"exp", op::translate_1to1_match_1_input<v0::Exp>},
+            {"ge", op::translate_binary_op<v1::GreaterEqual>},
+            {"gt", op::translate_binary_op<v1::Greater>},
             {"integer_pow", op::translate_integer_pow},
             {"max", op::translate_1to1_match_2_inputs<v1::Maximum>},
             {"mul", op::translate_1to1_match_2_inputs<v1::Multiply>},
diff --git a/tests/layer_tests/jax_tests/test_binary_comparison.py b/tests/layer_tests/jax_tests/test_binary_comparison.py
new file mode 100644
index 00000000000000..0a85b1e4a551c2
--- /dev/null
+++ b/tests/layer_tests/jax_tests/test_binary_comparison.py
@@ -0,0 +1,55 @@
+# Copyright (C) 2018-2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import numpy as np
+import pytest
+from jax import lax
+from jax import numpy as jnp
+
+from jax_layer_test_class import JaxLayerTest
+
+rng = np.random.default_rng(56342)
+
+class TestBinaryComparison(JaxLayerTest):
+    def _prepare_input(self):
+        if np.issubdtype(self.input_type, np.floating):
+            x = rng.uniform(-5.0, 5.0, self.input_shapes[0]).astype(self.input_type)
+            y = rng.uniform(-5.0, 5.0, self.input_shapes[1]).astype(self.input_type)
+        elif np.issubdtype(self.input_type, np.signedinteger):
+            x = rng.integers(-8, 8, self.input_shapes[0]).astype(self.input_type)
+            y = rng.integers(-8, 8, self.input_shapes[1]).astype(self.input_type)
+        else:
+            x = rng.integers(0, 8, self.input_shapes[0]).astype(self.input_type)
+            y = rng.integers(0, 8, self.input_shapes[1]).astype(self.input_type)
+        x = jnp.array(x)
+        y = jnp.array(y)
+        return [x, y]
+
+    def create_model(self, input_shapes, binary_op, input_type):
+        reduce_map = {
+            'ge': lax.ge,
+            'gt': lax.gt
+        }
+
+        self.input_shapes = input_shapes
+        self.input_type = input_type
+        
+        def jax_binary(x, y):
+            return reduce_map[binary_op](x, y)
+
+        return jax_binary, None, binary_op
+
+    @pytest.mark.parametrize('input_shapes', [[[5], [1]], [[1], [5]], [[2, 2, 4], [1, 1, 4]],
+                                              [[5, 10], [5, 10]], [[2, 4, 6], [1, 4, 6]],
+                                              [[5, 8, 10, 128], [5, 1, 10, 128]]])
+    @pytest.mark.parametrize('binary_op', ['ge', 'gt'])
+    @pytest.mark.parametrize('input_type', [np.int8, np.uint8, np.int16, np.uint16,
+                                            np.int32, np.uint32, np.int64, np.uint64,
+                                            np.float16, np.float32, np.float64])
+    @pytest.mark.nightly
+    @pytest.mark.precommit
+    @pytest.mark.precommit_jax_fe
+    def test_binary(self, input_shapes, binary_op, input_type,
+                    ie_device, precision, ir_version):
+        self._test(*self.create_model(input_shapes,  binary_op, input_type),
+                   ie_device, precision, ir_version)

From 4ed5ed09c0c49b581cf5e8ee8d3b34d416d5c15b Mon Sep 17 00:00:00 2001
From: Roman Kazantsev <roman.kazantsev@intel.com>
Date: Wed, 18 Sep 2024 22:13:00 +0400
Subject: [PATCH 08/49] [TF FE][SDL] Fix performance inefficiencies (#26644)

**Details:** Fix performance inefficiencies

**Ticket:** 123298

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>
---
 src/frontends/tensorflow/src/decoder_proto.cpp               | 4 ++--
 src/frontends/tensorflow_common/include/helper_ops/merge.hpp | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/frontends/tensorflow/src/decoder_proto.cpp b/src/frontends/tensorflow/src/decoder_proto.cpp
index 157dca36f42202..b28161faa1af83 100644
--- a/src/frontends/tensorflow/src/decoder_proto.cpp
+++ b/src/frontends/tensorflow/src/decoder_proto.cpp
@@ -150,7 +150,7 @@ void parse_producer_name(const std::string& producer_port_name,
                                 "Port id is not specified or not a number. Value: ",
                                 port_id);
         producer_output_port_index = std::stoi(port_id);
-        producer_output_port_name = port_name;
+        producer_output_port_name = std::move(port_name);
         return;
     } else if (first_colon != std::string::npos) {
         // just one colon case
@@ -186,7 +186,7 @@ std::vector<::tensorflow::AttrValue> DecoderProto::decode_attribute_helper(const
     auto attr_map = m_node_def->attr();
     if (attr_map.contains(name)) {
         auto value = m_node_def->attr().at(name);
-        return {value};
+        return {std::move(value)};
     } else {
         return {};
     }
diff --git a/src/frontends/tensorflow_common/include/helper_ops/merge.hpp b/src/frontends/tensorflow_common/include/helper_ops/merge.hpp
index 045c651a8714ce..2a04d12ba4190e 100644
--- a/src/frontends/tensorflow_common/include/helper_ops/merge.hpp
+++ b/src/frontends/tensorflow_common/include/helper_ops/merge.hpp
@@ -84,7 +84,7 @@ class Merge : public InternalOperation {
             cf_marker_exists(this_node),
             "[TensorFlow Frontend] internal error: Switch node has not set conditional flow marker");
         auto cf_marker = get_cf_marker(this_node);
-        for (auto eliminated_marker : cf_marker.merge_eliminated_markers) {
+        for (const auto& eliminated_marker : cf_marker.merge_eliminated_markers) {
             resulted_indices.push_back(eliminated_marker.first);
         }
         return resulted_indices;

From 327f8e224d97559f9392c4adc7621cb139e201f9 Mon Sep 17 00:00:00 2001
From: David Nam <david.nam@intel.com>
Date: Thu, 19 Sep 2024 11:54:20 +0800
Subject: [PATCH 09/49] [GPU] Support different input and output data type in
 convolution ref (#26501)

### Details:
- For fp model, some convolutions may not be compressed to fp16
depending on the transformation policy and those convolutions may have
the fused node which is of fp16. Then convolution node input data type
will be fp32 while output data type fp16. Convolution needs to support
this case.

### Tickets:
 - 147689
---
 .../convolution/convolution_kernel_ref.cpp    |  3 +-
 .../unit/test_cases/convolution_gpu_test.cpp  | 42 +++++++++++++++++++
 2 files changed, 44 insertions(+), 1 deletion(-)

diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/convolution/convolution_kernel_ref.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/convolution/convolution_kernel_ref.cpp
index 7ddc42b6c152b0..776ef796ab92b5 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/convolution/convolution_kernel_ref.cpp
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/convolution/convolution_kernel_ref.cpp
@@ -123,7 +123,8 @@ bool ConvolutionKernel_Ref::Validate(const Params& params) const {
 
     // int8/uint8 inputs (quantization case) require additional checks
     // require some additional checks.
-    if (input_type == output_type && input_type != Datatype::UINT8 && input_type != Datatype::INT8)
+    if (input_type != Datatype::UINT8 && input_type != Datatype::INT8 &&
+        output_type != Datatype::UINT8 && output_type != Datatype::INT8)
         return true;
 
     // (u)int8 input + fp weights
diff --git a/src/plugins/intel_gpu/tests/unit/test_cases/convolution_gpu_test.cpp b/src/plugins/intel_gpu/tests/unit/test_cases/convolution_gpu_test.cpp
index ce07bffe3666f3..5939e8066b9537 100644
--- a/src/plugins/intel_gpu/tests/unit/test_cases/convolution_gpu_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/test_cases/convolution_gpu_test.cpp
@@ -12,6 +12,7 @@
 #include <intel_gpu/primitives/crop.hpp>
 #include <intel_gpu/primitives/reorder.hpp>
 #include <intel_gpu/primitives/reshape.hpp>
+#include <intel_gpu/primitives/permute.hpp>
 
 #include <algorithm>
 #include <array>
@@ -1639,6 +1640,47 @@ TEST(convolution_f32_fw_gpu, basic_convolution) {
     }
 }
 
+TEST(convolution_f32_fw_gpu, input_f32_output_f16_dynamic_ref_kernel) {
+    auto& engine = get_test_engine();
+
+    auto in_layout = layout{ov::PartialShape::dynamic(4), data_types::f32, format::bfyx};
+    auto input = engine.allocate_memory({ data_types::f32, format::bfyx, { 1, 1, 5, 4 } });
+    auto weights = engine.allocate_memory({ data_types::i8, format::bfyx, { 1, 1, 3, 2 } });
+    auto biases = engine.allocate_memory({ data_types::f32, format::bfyx, { 1, 1, 1, 1 } });
+    auto eltwise_data = engine.allocate_memory({ data_types::f32, format::bfyx, { 1, 1, 1, 1 } });
+
+    set_values(input, {
+        1.0f, 2.0f, 3.0f, 4.0f,
+        5.0f, 2.0f, 2.0f, 3.0f,
+        4.0f, 6.0f, 3.0f, 3.0f,
+        3.0f, 5.0f, 1.0f, 1.0f,
+        1.0f, 1.0f, 1.0f, 1.0f }
+    );
+
+    topology topology(
+        input_layout("input", in_layout),
+        data("weights", weights),
+        data("biases", biases),
+        data("eltwise_data", eltwise_data),
+        convolution( "conv", input_info("input"), "weights", "biases", 1, {2, 1}, {1, 1}, {0, 0}, {0, 0}, false),
+        eltwise("eltwise", { input_info("conv"), input_info("eltwise_data") }, eltwise_mode::prod, data_types::f16),
+        permute("permute", input_info("eltwise"), {0, 1, 2, 3}));
+
+    ExecutionConfig config = get_test_default_config(engine);
+    ov::intel_gpu::ImplementationDesc conv_impl_ref = { format::bfyx, "convolution_gpu_ref", impl_types::ocl };
+    config.set_property(ov::intel_gpu::force_implementations(ov::intel_gpu::ImplForcingMap{ { "conv", conv_impl_ref } }));
+    config.set_property(ov::intel_gpu::allow_new_shape_infer(true));
+
+    network network(engine, topology, config);
+    network.set_input_data("input", input);
+
+    auto outputs = network.execute();
+
+    ASSERT_EQ(outputs.size(), size_t(1));
+    ASSERT_FALSE(has_node(*network.get_program(), "eltwise"));
+    ASSERT_EQ(outputs.at("permute").get_layout().data_type, data_types::f16);
+}
+
 TEST(convolution_f32_fw_gpu, convolution_big_size_weights) {
     auto& engine = get_test_engine();
 

From bdc01107d989310c6efa7dd61c7fa5743c971f1d Mon Sep 17 00:00:00 2001
From: Taylor Yeonbok Lee <taylor.lee@intel.com>
Date: Thu, 19 Sep 2024 15:59:57 +0900
Subject: [PATCH 10/49] [GPU] Fuse more eltwises for horizontally fused FC
 (#26599)

### Details:
- Target pattern: FCs to be fused by horizontal fusing pass and they
have Add users which can be regarded as bias add. Here if we fuse the
FCs as is, the fused pattern will be fused_fc -> VariadicSplit -> Add so
the Adds cannot be fused to the FCs.
- This PR sets such Add users as the FC's bias inputs so that the fused
FC can handle them as fused bias.

### Tickets:
 - CVS-151841
---
 .../transformations/fc_horizontal_fusion.cpp  | 140 ++++++++++++++----
 .../horizontal_fc_fusion_test.cpp             |  80 ++++++++++
 2 files changed, 192 insertions(+), 28 deletions(-)

diff --git a/src/plugins/intel_gpu/src/plugin/transformations/fc_horizontal_fusion.cpp b/src/plugins/intel_gpu/src/plugin/transformations/fc_horizontal_fusion.cpp
index a5ec6c0060ef81..a0651b484e2431 100644
--- a/src/plugins/intel_gpu/src/plugin/transformations/fc_horizontal_fusion.cpp
+++ b/src/plugins/intel_gpu/src/plugin/transformations/fc_horizontal_fusion.cpp
@@ -13,6 +13,7 @@
 #include "openvino/pass/pattern/op/wrap_type.hpp"
 #include "transformations/utils/utils.hpp"
 #include "intel_gpu/op/placeholder.hpp"
+#include "intel_gpu/runtime/debug_configuration.hpp"
 
 namespace ov {
 namespace intel_gpu {
@@ -40,13 +41,12 @@ FullyConnectedHorizontalFusion::FullyConnectedHorizontalFusion() {
             return std::dynamic_pointer_cast<op::Placeholder>(node);
         };
         // Three FCs connected to the same input
-        const int num_fcs_to_fuse = 3;
+        const int min_num_fcs_to_fuse = 3;
+        const int max_num_fcs_to_fuse = 3;
         const auto& fc = std::dynamic_pointer_cast<op::FullyConnectedCompressed>(output.get_node_shared_ptr());
         const auto& input = fc->get_input_node_shared_ptr(0);
         if (!fc->get_input_partial_shape(0).is_dynamic())
             return false;
-        if (input->get_users().size() < num_fcs_to_fuse)
-            return false;
         size_t user_fc_count = 0;
         int32_t nodes_with_bias = 0;
         int32_t nodes_with_zp = 0;
@@ -67,8 +67,9 @@ FullyConnectedHorizontalFusion::FullyConnectedHorizontalFusion() {
             }
             user_fc_count++;
         }
-        return (user_fc_count == num_fcs_to_fuse) && (nodes_with_bias == num_fcs_to_fuse || nodes_with_bias == 0) &&
-               (nodes_with_zp == num_fcs_to_fuse || nodes_with_zp == 0);
+        return (user_fc_count >= min_num_fcs_to_fuse) && (user_fc_count <= max_num_fcs_to_fuse) &&
+               (nodes_with_bias == static_cast<int32_t>(user_fc_count) || nodes_with_bias == 0) &&
+               (nodes_with_zp == static_cast<int32_t>(user_fc_count) || nodes_with_zp == 0);
     };
 
     auto target_fc = wrap_type<op::FullyConnectedCompressed>(is_target_pattern);
@@ -78,6 +79,7 @@ FullyConnectedHorizontalFusion::FullyConnectedHorizontalFusion() {
         auto m_fc = pattern_map.at(target_fc).get_node_shared_ptr();
         auto input_node = m_fc->get_input_node_shared_ptr(0);
         std::vector<std::shared_ptr<op::FullyConnectedCompressed>> fc_nodes;
+        ov::NodeVector fc_nodes_vec;
         ov::NodeVector weight_nodes;
         ov::NodeVector scale_nodes;
         ov::NodeVector bias_nodes;
@@ -87,6 +89,7 @@ FullyConnectedHorizontalFusion::FullyConnectedHorizontalFusion() {
             if (fc_user) {
                 OPENVINO_ASSERT(fc_user->inputs().size() >= 4, "Compressed FC should have at least 4 inputs");
                 fc_nodes.push_back(fc_user);
+                fc_nodes_vec.push_back(fc_user);
                 weight_nodes.push_back(fc_user->get_input_node_shared_ptr(1));
                 if (!std::dynamic_pointer_cast<op::Placeholder>(fc_user->get_input_node_shared_ptr(2)))
                     bias_nodes.push_back(fc_user->get_input_node_shared_ptr(2));
@@ -95,33 +98,109 @@ FullyConnectedHorizontalFusion::FullyConnectedHorizontalFusion() {
                     zp_nodes.push_back(fc_user->get_input_node_shared_ptr(4));
             }
         }
-        auto weight_dtype = fc_nodes[0]->get_input_element_type(1);
-        auto k_size = fc_nodes[0]->get_input_shape(1)[fc_nodes[0]->get_input_shape(1).size() - 1];
+        // fc weight is already transposed to [N, K]
+        const size_t weight_idx = 1;
+        if (fc_nodes[0]->get_input_shape(weight_idx).size() != 2)
+            return false;
+        const size_t n_axis = 0;
+        const size_t k_axis = 1;
+        auto weight_dtype = fc_nodes[0]->get_input_element_type(weight_idx);
+        auto k_size = fc_nodes[0]->get_input_shape(weight_idx)[k_axis];
         std::vector<int64_t> orig_n_sizes;
         // merge weights, scale, zp
         for (auto fc : fc_nodes) {
-            if (k_size != fc->get_input_shape(1)[fc->get_input_shape(1).size() - 1])
+            if (k_size != fc->get_input_shape(weight_idx)[k_axis])
                 return false;
-            if (weight_dtype != fc->get_input_element_type(1))
+            if (weight_dtype != fc->get_input_element_type(weight_idx))
                 return false;
-            orig_n_sizes.push_back(fc->get_input_shape(1)[fc->get_input_shape(1).size() - 2]);
+            orig_n_sizes.push_back(fc->get_input_shape(weight_idx)[n_axis]);
+        }
+        ov::OutputVector weight_nodes_as_output_vector;
+        for (size_t i = 0; i < weight_nodes.size(); ++i) {
+            weight_nodes_as_output_vector.push_back(weight_nodes[i]);
         }
-        auto weight_nodes_as_output_vector = ov::OutputVector{weight_nodes[0], weight_nodes[1], weight_nodes[2]};
         auto fused_weight = std::make_shared<ov::op::v0::Concat>(weight_nodes_as_output_vector, 0);
-        fused_weight->set_friendly_name(weight_nodes[0]->get_friendly_name() + "_fused");
-        ov::copy_runtime_info({weight_nodes[0], weight_nodes[1], weight_nodes[2]}, fused_weight);
+        fused_weight->set_friendly_name(weight_nodes[0]->get_friendly_name() + "_fused_weight");
+        ov::copy_runtime_info(weight_nodes, fused_weight);
+
+        ov::OutputVector scales_as_output_vector;
+        for (size_t i = 0; i < scale_nodes.size(); ++i) {
+            scales_as_output_vector.push_back(scale_nodes[i]);
+        }
 
-        auto scale_nodes_as_output_vector = ov::OutputVector{scale_nodes[0], scale_nodes[1], scale_nodes[2]};
-        auto fused_scale = std::make_shared<ov::op::v0::Concat>(scale_nodes_as_output_vector, 0);
-        fused_scale->set_friendly_name(scale_nodes[0]->get_friendly_name() + "_fused");
-        ov::copy_runtime_info({scale_nodes[0], scale_nodes[1], scale_nodes[2]}, fused_scale);
+        auto fused_scale = std::make_shared<ov::op::v0::Concat>(scales_as_output_vector, 0);
+        fused_scale->set_friendly_name(scale_nodes[0]->get_friendly_name() + "_fused_scale");
+        ov::copy_runtime_info(scale_nodes, fused_scale);
+        // check if all of the fc has a bias user, set it as bias input
+        size_t n_bias_users = 0;
+        for (auto fc : fc_nodes) {
+            if (fc->get_users().size() == 1
+                && fc->get_users()[0]->get_type_info() == ov::opset1::Add::get_type_info_static()
+                && ov::is_type<ov::op::v0::Constant>(fc->get_users()[0]->inputs()[1].get_source_output().get_node())) {
+                    n_bias_users++;
+            }
+        }
 
+        size_t bias_concat_axis = 0;
+        if (bias_nodes.empty() && n_bias_users == fc_nodes.size()) {
+            // Set Add user as bias input to FC
+            for (size_t i = 0; i < fc_nodes.size(); ++i) {
+                auto orig_fc = fc_nodes[i];
+                auto bias_node = orig_fc->get_users()[0];
+                auto bias_const_ptr = orig_fc->get_users()[0]->get_input_node_shared_ptr(1);
+                bias_nodes.push_back(bias_const_ptr);
+            }
+            // Check shape and find axis
+            const auto bias_rank = bias_nodes[0]->get_output_partial_shape(0).size();
+            size_t non_zero_diffs = 0;
+            for (size_t i = 0; i < bias_rank; ++i) {
+                std::unordered_set<size_t> dims;
+                for (size_t j = 0; j < bias_nodes.size(); ++j) {
+                    dims.insert(bias_nodes[j]->get_output_partial_shape(0)[i].get_length());
+                }
+                if (dims.size() > 1) {
+                    bias_concat_axis = i;
+                    non_zero_diffs++;
+                }
+            }
+            if (non_zero_diffs <= 1) {
+                for (size_t i = 0; i < fc_nodes.size(); ++i) {
+                    auto orig_fc = fc_nodes[i];
+                    auto bias_node = orig_fc->get_users()[0];
+                    GPU_DEBUG_TRACE_DETAIL << "Set Add op user " << bias_node->get_friendly_name() << " as the FC "
+                                           << orig_fc->get_friendly_name() << "'s bias input" << std::endl;
+                    auto bias_const = orig_fc->get_users()[0]->input_value(1);
+                    auto orig_users_of_bias_user = bias_node->get_users();
+                    ov::OutputVector fc_inputs = orig_fc->input_values();
+                    fc_inputs[2] = bias_const;
+                    auto new_fc = orig_fc->clone_with_new_inputs(fc_inputs);
+                    new_fc->set_friendly_name(orig_fc->get_friendly_name() + "_with_bias");
+                    ov::copy_runtime_info(orig_fc, new_fc);
+                    for (auto u : orig_users_of_bias_user) {
+                        for (size_t idx = 0; idx < u->inputs().size(); ++idx) {
+                            if (u->get_input_node_shared_ptr(idx) == bias_node) {
+                                u->input(idx).replace_source_output(new_fc->output(0));
+                            }
+                        }
+                    }
+                    fc_nodes[i] = std::dynamic_pointer_cast<op::FullyConnectedCompressed>(new_fc);
+                    bias_node->clear_control_dependencies();
+                    orig_fc->clear_control_dependencies();
+                }
+            } else {
+                // biases cannot be fusable. Not to set users as bias input
+                bias_nodes.clear();
+            }
+        }
         std::shared_ptr<ov::Node> fused_bias;
-        if (bias_nodes.size() == 3) {
-            auto bias_nodes_as_output_vector = ov::OutputVector{bias_nodes[0], bias_nodes[1], bias_nodes[2]};
-            fused_bias = std::make_shared<ov::op::v0::Concat>(bias_nodes_as_output_vector, 0);
-            fused_bias->set_friendly_name(bias_nodes[0]->get_friendly_name() + "_fused");
-            ov::copy_runtime_info({bias_nodes[0], bias_nodes[1], bias_nodes[2]}, fused_bias);
+        if (bias_nodes.size() == fc_nodes.size()) {
+            ov::OutputVector bias_nodes_as_output_vector;
+            for (size_t i = 0; i < bias_nodes.size(); ++i) {
+                bias_nodes_as_output_vector.push_back(bias_nodes[i]);
+            }
+            fused_bias = std::make_shared<ov::op::v0::Concat>(bias_nodes_as_output_vector, bias_concat_axis);
+            fused_bias->set_friendly_name(bias_nodes[0]->get_friendly_name() + "_fused_bias");
+            ov::copy_runtime_info(bias_nodes, fused_bias);
         } else {
             fused_bias = std::make_shared<op::Placeholder>();
         }
@@ -161,9 +240,12 @@ FullyConnectedHorizontalFusion::FullyConnectedHorizontalFusion() {
                         return false;
                 }
             } else {
-                auto zp_nodes_as_output_vector = ov::OutputVector{zp_nodes[0], zp_nodes[1], zp_nodes[2]};
+                ov::OutputVector zp_nodes_as_output_vector;
+                for (size_t i = 0; i < zp_nodes.size(); ++i) {
+                    zp_nodes_as_output_vector.push_back(zp_nodes[i]);
+                }
                 fused_zps = std::make_shared<ov::op::v0::Concat>(zp_nodes_as_output_vector, 0);
-                fused_zps->set_friendly_name(zp_nodes[0]->get_friendly_name() + "_fused");
+                fused_zps->set_friendly_name(zp_nodes[0]->get_friendly_name() + "_fused_zps");
             }
         }
         // Create new fc with merged weights, bias, scale, zp
@@ -182,16 +264,17 @@ FullyConnectedHorizontalFusion::FullyConnectedHorizontalFusion() {
                                                                     fused_scale,
                                                                     fc_nodes[0]->get_output_type());
 
-        auto new_fc_name = fc_nodes[0]->get_friendly_name() + "_fused";
+        auto new_fc_name = fc_nodes[0]->get_friendly_name() + "_fused_" + std::to_string(fc_nodes.size()) + "FCs";
         new_fc->set_friendly_name(new_fc_name);
-        copy_runtime_info({fc_nodes[0], fc_nodes[1], fc_nodes[2]}, new_fc);
+        copy_runtime_info(fc_nodes_vec, new_fc);
 
         // Split output and connect to the orig users
         auto split_name = fc_nodes[0]->get_friendly_name() + "_split";
         auto axis_const = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{1}, {new_fc->get_output_partial_shape(0).size() - 1});
-        auto split_const = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{3}, orig_n_sizes);
+        auto split_size = fc_nodes.size();
+        auto split_const = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{split_size}, orig_n_sizes);
         auto output_split = std::make_shared<ov::op::v1::VariadicSplit>(new_fc, axis_const, split_const);
-        copy_runtime_info({fc_nodes[0], fc_nodes[1], fc_nodes[2]}, output_split);
+        copy_runtime_info(fc_nodes_vec, output_split);
         output_split->set_friendly_name(split_name);
         for (size_t i = 0; i < fc_nodes.size(); ++i) {
             auto org_fc = fc_nodes[i];
@@ -204,6 +287,7 @@ FullyConnectedHorizontalFusion::FullyConnectedHorizontalFusion() {
             }
             org_fc->clear_control_dependencies();
         }
+        GPU_DEBUG_TRACE_DETAIL << "Created a new fused FC " << new_fc_name << std::endl;
         return true;
     };
 
diff --git a/src/plugins/intel_gpu/tests/unit/transformations/horizontal_fc_fusion_test.cpp b/src/plugins/intel_gpu/tests/unit/transformations/horizontal_fc_fusion_test.cpp
index fababa0c20df38..b94ba03a7565e8 100644
--- a/src/plugins/intel_gpu/tests/unit/transformations/horizontal_fc_fusion_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/transformations/horizontal_fc_fusion_test.cpp
@@ -17,6 +17,7 @@
 #include "openvino/op/concat.hpp"
 #include "openvino/op/variadic_split.hpp"
 #include "openvino/op/reshape.hpp"
+#include "openvino/op/add.hpp"
 #include "openvino/pass/manager.hpp"
 
 #include <transformations/utils/utils.hpp>
@@ -162,6 +163,85 @@ TEST_F(TransformationTestsF, FullyConnectedHorizontalFusion_bias_zp) {
         comparator.enable(FunctionsComparator::ATTRIBUTES);
     }
 }
+
+TEST_F(TransformationTestsF, FullyConnectedHorizontalFusion_eltwise_bias_zp) {
+    std::vector<int64_t> pattern = {7, -1};
+    {
+        auto input = std::make_shared<ov::op::v0::Parameter>(ov::element::f16, ov::PartialShape{-1, 7, 4096});
+        auto weight1 = std::make_shared<ov::op::v0::Constant>(ov::element::u4, ov::Shape{1024, 4096});
+        weight1->set_friendly_name("weight1_1");
+        auto weight2 = std::make_shared<ov::op::v0::Constant>(ov::element::u4, ov::Shape{512, 4096});
+        weight2->set_friendly_name("weight1_2");
+        auto weight3 = std::make_shared<ov::op::v0::Constant>(ov::element::u4, ov::Shape{128, 4096});
+        weight3->set_friendly_name("weight1_3");
+
+        auto bias1 = std::make_shared<ov::intel_gpu::op::Placeholder>();
+        auto bias2 = std::make_shared<ov::intel_gpu::op::Placeholder>();
+        auto bias3 = std::make_shared<ov::intel_gpu::op::Placeholder>();
+ 
+        auto scale1 = std::make_shared<ov::op::v0::Constant>(ov::element::f16, ov::Shape{1024, 32});
+        auto scale2 = std::make_shared<ov::op::v0::Constant>(ov::element::f16, ov::Shape{512, 32});
+        auto scale3 = std::make_shared<ov::op::v0::Constant>(ov::element::f16, ov::Shape{128, 32});
+        auto fc1 = std::make_shared<ov::intel_gpu::op::FullyConnectedCompressed>(input, weight1, bias1, scale1);
+        fc1->set_friendly_name("fc1");
+        auto fc2 = std::make_shared<ov::intel_gpu::op::FullyConnectedCompressed>(input, weight2, bias2, scale2);
+        auto fc3 = std::make_shared<ov::intel_gpu::op::FullyConnectedCompressed>(input, weight3, bias3, scale3);
+
+        auto add_input1 = std::make_shared<ov::op::v0::Constant>(ov::element::f16, ov::Shape{1, 1024});
+        auto add1 = std::make_shared<ov::op::v1::Add>(fc1, add_input1);
+
+        auto add_input2 = std::make_shared<ov::op::v0::Constant>(ov::element::f16, ov::Shape{1, 512});
+        auto add2 = std::make_shared<ov::op::v1::Add>(fc2, add_input2);
+
+        auto add_input3 = std::make_shared<ov::op::v0::Constant>(ov::element::f16, ov::Shape{1, 128});
+        auto add3 = std::make_shared<ov::op::v1::Add>(fc3, add_input3);
+
+        auto reshape_pattern = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{2}, pattern);
+        auto reshape1 = std::make_shared<ov::op::v1::Reshape>(add1, reshape_pattern, true);
+        auto reshape2 = std::make_shared<ov::op::v1::Reshape>(add2, reshape_pattern, true);
+        auto reshape3 = std::make_shared<ov::op::v1::Reshape>(add3, reshape_pattern, true);
+        auto result1 = std::make_shared<ov::op::v0::Result>(reshape1);
+        auto result2 = std::make_shared<ov::op::v0::Result>(reshape2);
+        auto result3 = std::make_shared<ov::op::v0::Result>(reshape3);
+        model = std::make_shared<ov::Model>(ov::ResultVector{result1, result2, result3}, ov::ParameterVector{input});
+        manager.register_pass<FullyConnectedHorizontalFusion>();
+    }
+    {
+        auto input = std::make_shared<ov::op::v0::Parameter>(ov::element::f16, ov::PartialShape{-1, 7, 4096});
+        auto weight1 = std::make_shared<ov::op::v0::Constant>(ov::element::u4, ov::Shape{1024, 4096});
+        weight1->set_friendly_name("weight2_1");
+        auto weight2 = std::make_shared<ov::op::v0::Constant>(ov::element::u4, ov::Shape{512, 4096});
+        weight2->set_friendly_name("weight2_2");
+        auto weight3 = std::make_shared<ov::op::v0::Constant>(ov::element::u4, ov::Shape{128, 4096});
+        weight3->set_friendly_name("weight2_3");
+        auto weights = ov::OutputVector{weight1, weight2, weight3};
+        auto weight_fused = std::make_shared<ov::op::v0::Concat>(weights, 0);
+        auto bias1 = std::make_shared<ov::op::v0::Constant>(ov::element::f16, ov::Shape{1, 1024});
+        auto bias2 = std::make_shared<ov::op::v0::Constant>(ov::element::f16, ov::Shape{1, 512});
+        auto bias3 = std::make_shared<ov::op::v0::Constant>(ov::element::f16, ov::Shape{1, 128});
+        auto biases = ov::OutputVector{bias1, bias2, bias3};
+        auto bias_fused = std::make_shared<ov::op::v0::Concat>(biases, 1);
+        auto scale1 = std::make_shared<ov::op::v0::Constant>(ov::element::f16, ov::Shape{1024, 32});
+        auto scale2 = std::make_shared<ov::op::v0::Constant>(ov::element::f16, ov::Shape{512, 32});
+        auto scale3 = std::make_shared<ov::op::v0::Constant>(ov::element::f16, ov::Shape{128, 32});
+        auto scales = ov::OutputVector{scale1, scale2, scale3};
+        auto scale_fused = std::make_shared<ov::op::v0::Concat>(scales, 0);
+        auto fc_fused = std::make_shared<ov::intel_gpu::op::FullyConnectedCompressed>(input, weight_fused, bias_fused, scale_fused);
+        auto axis_const = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{1}, {fc_fused->get_output_partial_shape(0).size() - 1});
+        std::vector<int64_t> orig_n_sizes = {1024, 512, 128};
+        auto split_const = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{3}, orig_n_sizes);
+        auto split = std::make_shared<ov::op::v1::VariadicSplit>(fc_fused, axis_const, split_const);
+        auto reshape_pattern = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{2}, pattern);
+        auto reshape1 = std::make_shared<ov::op::v1::Reshape>(split->output(0), reshape_pattern, true);
+        auto reshape2 = std::make_shared<ov::op::v1::Reshape>(split->output(1), reshape_pattern, true);
+        auto reshape3 = std::make_shared<ov::op::v1::Reshape>(split->output(2), reshape_pattern, true);
+        auto result1 = std::make_shared<ov::op::v0::Result>(reshape1);
+        auto result2 = std::make_shared<ov::op::v0::Result>(reshape2);
+        auto result3 = std::make_shared<ov::op::v0::Result>(reshape3);
+        model_ref = std::make_shared<ov::Model>(ov::ResultVector{result1, result2, result3}, ov::ParameterVector{input});
+        comparator.enable(FunctionsComparator::ATTRIBUTES);
+    }
+}
 }  // namespace intel_gpu
 }  // namespace test
 }  // namespace ov
\ No newline at end of file

From f0c5d2f434605da626243f641aecd6e43c5c4ec9 Mon Sep 17 00:00:00 2001
From: Vladislav Golubev <vladislav.golubev@intel.com>
Date: Thu, 19 Sep 2024 13:05:27 +0200
Subject: [PATCH 11/49] [Snippets] Fix sporadic issue in MHAParallelWAOptimizer
 (#26660)

### Details:
- *Currently, `loops_to_split` in `MHAParallelWAOptimizer` are stored in
unordered_map, so elements order is not determined. This sporadically
leads to the situation when loop last iteration has work_amount more
than main body's increment. This might lead to failures*
- *In this PR, 'loops_to_split' are stored in vector, so loop
information updates are always called for expanded loop infos in
determined order: FIRST_ITER->MAIN_BODY->LAST_ITER*
- *Also, the corresponding assert is added to
`InsertSpecificIterations::get_decomposed_loop_work_amount` in order to
throw an exception on early stage in case of incorrect configuration.
This assert also allows to cover the changes by the existing tests (some
of them fail if assert is added but the fix is not applied)*

### Tickets:
 - *N/A*
---
 .../include/snippets/runtime_configurator.hpp     |  4 ++--
 .../lowered/pass/insert_specific_iterations.cpp   |  6 +++++-
 src/common/snippets/src/runtime_configurator.cpp  | 15 ++++++++++-----
 3 files changed, 17 insertions(+), 8 deletions(-)

diff --git a/src/common/snippets/include/snippets/runtime_configurator.hpp b/src/common/snippets/include/snippets/runtime_configurator.hpp
index a2c605fd8b5356..10c15a4621a72a 100644
--- a/src/common/snippets/include/snippets/runtime_configurator.hpp
+++ b/src/common/snippets/include/snippets/runtime_configurator.hpp
@@ -179,13 +179,13 @@ class RuntimeConfigurator {
         static std::unordered_set<size_t> find_unsqueezed_params(
             const ov::snippets::lowered::LinearIRCPtr& linear_ir,
             const std::unordered_set<snippets::lowered::ExpressionPtr>& brgemms);
-        static std::unordered_set<ov::snippets::lowered::ExpandedLoopInfoPtr> find_loops_to_split(
+        static std::vector<ov::snippets::lowered::ExpandedLoopInfoPtr> find_loops_to_split(
             const ov::snippets::lowered::LinearIRCPtr& linear_ir,
             const std::unordered_set<size_t>& unsqueezed_params);
 
         RuntimeConfigurator* configurator = nullptr;
 
-        std::unordered_set<ov::snippets::lowered::ExpandedLoopInfoPtr> loops_to_split{};
+        std::vector<ov::snippets::lowered::ExpandedLoopInfoPtr> loops_to_split{};
         std::unordered_set<size_t> unsqueezed_params{};
         std::vector<std::vector<size_t>> optimized_layouts{};
         std::vector<size_t> m_dim_idces{};
diff --git a/src/common/snippets/src/lowered/pass/insert_specific_iterations.cpp b/src/common/snippets/src/lowered/pass/insert_specific_iterations.cpp
index 1e99f8c845161f..9ca5181883af36 100644
--- a/src/common/snippets/src/lowered/pass/insert_specific_iterations.cpp
+++ b/src/common/snippets/src/lowered/pass/insert_specific_iterations.cpp
@@ -85,8 +85,12 @@ size_t InsertSpecificIterations::get_decomposed_loop_work_amount(const UnifiedLo
             return is_dynamic ? remaining_work_amount : increment;
         case (SpecificLoopIterType::MAIN_BODY):
             return is_dynamic ? remaining_work_amount : (remaining_work_amount / increment) * increment;
-        case (SpecificLoopIterType::LAST_ITER):
+        case (SpecificLoopIterType::LAST_ITER): {
+            OPENVINO_ASSERT(is_dynamic || remaining_work_amount < unified_loop_info->get_increment(),
+                            "Last iter work amount (", remaining_work_amount,
+                            ") must be less than the UnifiedLoopInfo's increment: ", unified_loop_info->get_increment());
             return remaining_work_amount;
+        }
         default:
             OPENVINO_THROW("Unknown SpecificLoopIterType!");
     }
diff --git a/src/common/snippets/src/runtime_configurator.cpp b/src/common/snippets/src/runtime_configurator.cpp
index 609005a9b86d80..9174d93eea3f98 100644
--- a/src/common/snippets/src/runtime_configurator.cpp
+++ b/src/common/snippets/src/runtime_configurator.cpp
@@ -477,21 +477,21 @@ std::unordered_set<size_t> RuntimeConfigurator::MHAParallelWAOptimizer::find_uns
     return unsqueezed_params;
 }
 
-std::unordered_set<ExpandedLoopInfoPtr> RuntimeConfigurator::MHAParallelWAOptimizer::find_loops_to_split(
+std::vector<ExpandedLoopInfoPtr> RuntimeConfigurator::MHAParallelWAOptimizer::find_loops_to_split(
     const lowered::LinearIRCPtr& linear_ir,
     const std::unordered_set<size_t>& unsqueezed_params) {
     const auto loop_manager = linear_ir->get_loop_manager();
-    std::unordered_set<ExpandedLoopInfoPtr> loops_to_split;
+    std::set<size_t> loop_idces_to_split;
     std::vector<size_t> prev_loop_idces;
 
-    auto add_loops_to_split = [&](const ExpressionPtr& expr) {
+    auto add_loop_idx_to_split = [&](const ExpressionPtr& expr) {
         const auto& loop_idces = expr->get_loop_ids();
         if (loop_idces != prev_loop_idces) {
             prev_loop_idces = loop_idces;
             for (const auto& loop_id : loop_idces) {
                 const auto expanded_loop_info = loop_manager->get_loop_info<ExpandedLoopInfo>(loop_id);
                 if (expanded_loop_info->get_dim_idx() == m_dim_idx) {
-                    loops_to_split.insert(expanded_loop_info);
+                    loop_idces_to_split.insert(loop_id);
                 }
             }
         }
@@ -505,8 +505,13 @@ std::unordered_set<ExpandedLoopInfoPtr> RuntimeConfigurator::MHAParallelWAOptimi
         // Ops after non related params mustn't be traversed
         if (unsqueezed_params.count(i++))
             continue;
-        utils::visit_path(param, visited, add_loops_to_split, false);
+        utils::visit_path(param, visited, add_loop_idx_to_split, false);
     }
+
+    const auto& loops_map = linear_ir->get_loop_manager()->get_map();
+    std::vector<ExpandedLoopInfoPtr> loops_to_split;
+    for (const auto& id : loop_idces_to_split)
+        loops_to_split.push_back(ov::as_type_ptr<ExpandedLoopInfo>(loops_map.at(id)));
     return loops_to_split;
 }
 

From 94fe66cf6adac68afe84da236ad3f0fb62ee9816 Mon Sep 17 00:00:00 2001
From: Nikolay Shchegolev <nikolay.shchegolev@intel.com>
Date: Thu, 19 Sep 2024 16:37:45 +0400
Subject: [PATCH 12/49] [CPU] Could not deserialize the RMS node (#26658)

### Details:
 - *Could not deserialize RMS node during reading model from cache*
 - *...*

### Tickets:
 - *152740*
---
 src/plugins/intel_cpu/src/extension.cpp | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/src/plugins/intel_cpu/src/extension.cpp b/src/plugins/intel_cpu/src/extension.cpp
index 5e43da6e2bfb86..c55012d67c2d9f 100644
--- a/src/plugins/intel_cpu/src/extension.cpp
+++ b/src/plugins/intel_cpu/src/extension.cpp
@@ -11,6 +11,7 @@
 #include "ov_ops/multiclass_nms_ie_internal.hpp"
 #include "ov_ops/nms_ie_internal.hpp"
 #include "ov_ops/nms_static_shape_ie.hpp"
+#include "ov_ops/rms.hpp"
 #include "ov_ops/rotary_positional_embeddings.hpp"
 #include "ov_ops/type_relaxed.hpp"
 #include "snippets/op/subgraph.hpp"
@@ -81,6 +82,7 @@ class TypeRelaxedExtension : public ov::OpExtension<ov::op::TypeRelaxed<Op>> {
     OP_EXTENSION(ov::op::internal::AUGRUCell)                               \
     OP_EXTENSION(ov::op::internal::AUGRUSequence)                           \
     OP_EXTENSION(ov::op::internal::NmsStaticShapeIE<ov::op::v8::MatrixNms>) \
+    OP_EXTENSION(ov::op::internal::RMS)                                     \
     OP_EXTENSION(ov::op::internal::RoPE)                                    \
     OP_EXTENSION_X64(ov::intel_cpu::MHANode)                                \
     OP_EXTENSION_X64(ov::intel_cpu::InteractionNode)                        \

From 67a4c180754922bad45da5925f2f33c29d410307 Mon Sep 17 00:00:00 2001
From: Pawel Raasz <pawel.raasz@intel.com>
Date: Thu, 19 Sep 2024 14:44:35 +0200
Subject: [PATCH 13/49] [core] ShapeOf preserve lower bound when upper is
 infinite (#21414)

### Details:
 - `ShapeOf` preserve lower bound when upper is infinite

### Tickets:
 - [CVS-126430](https://jira.devtools.intel.com/browse/CVS-126430)
---
 src/core/src/op/shape_of.cpp                     |  9 ++++-----
 src/core/tests/type_prop/prior_box.cpp           |  4 ++--
 src/core/tests/type_prop/prior_box_clustered.cpp |  4 ++--
 src/core/tests/type_prop/shape_of.cpp            |  2 +-
 src/core/tests/type_prop/split.cpp               |  2 +-
 src/core/tests/type_prop/squeeze.cpp             |  2 +-
 src/core/tests/type_prop/tile.cpp                |  2 +-
 src/core/tests/type_prop/top_k.cpp               | 16 ++++++++--------
 src/core/tests/type_prop/unsqueeze.cpp           |  2 +-
 9 files changed, 21 insertions(+), 22 deletions(-)

diff --git a/src/core/src/op/shape_of.cpp b/src/core/src/op/shape_of.cpp
index 1c12b54b1fdbe9..293c1b5fc5a59c 100644
--- a/src/core/src/op/shape_of.cpp
+++ b/src/core/src/op/shape_of.cpp
@@ -68,17 +68,16 @@ bool evaluate_bound(const Node* const node, ov::TensorVector& outputs, const boo
         // use node output type as it can be different than output tensor type
         // e.g. when v3::ShapeOf is converted to v0::ShapeOf then the output tensor will have always i64
         // but node output type is transferred from v3 and can be i32 (dimension inf bound is i32 max)
-        const auto is_out_type_i32 = node->get_output_element_type(0) == element::i32;
-        if (is_out_type_i32 || !is_upper) {
+        if (node->get_output_element_type(0) == element::i32) {
             const auto in_shape_rank = in_shape.size();
-            const auto max_et_val = is_out_type_i32 ? static_cast<int64_t>(std::numeric_limits<int32_t>::max())
-                                                    : std::numeric_limits<int64_t>::max();
+            constexpr auto max_et_val = static_cast<int64_t>(std::numeric_limits<int32_t>::max());
 
+            const auto get_val = is_upper ? &Interval::get_max_val : &Interval::get_min_val;
             auto limit_val = is_upper ? max_et_val : static_cast<decltype(max_et_val)>(0);
 
             auto dynamic_mask = std::vector<char>(in_shape_rank);
             std::transform(in_shape.begin(), in_shape.end(), dynamic_mask.begin(), [&](const Dimension& d) {
-                return static_cast<char>(d.get_interval().get_max_val() >= max_et_val);
+                return static_cast<char>((d.get_interval().*get_val)() >= max_et_val);
             });
 
             const auto limit = Tensor(out_et, Shape{}, &limit_val);
diff --git a/src/core/tests/type_prop/prior_box.cpp b/src/core/tests/type_prop/prior_box.cpp
index 67595a3d980e9f..0e75ef576c9664 100644
--- a/src/core/tests/type_prop/prior_box.cpp
+++ b/src/core/tests/type_prop/prior_box.cpp
@@ -212,7 +212,7 @@ TYPED_TEST_P(PriorBoxTest, preseve_partial_values_and_symbols_on_inputs) {
 }
 
 TYPED_TEST_P(PriorBoxTest, preseve_partial_values_inf_bound) {
-    auto out_size_shape = PartialShape{{1, 4}, {5, -1}};  // ShapeOf make 2nd Dim {0, -1}
+    auto out_size_shape = PartialShape{{1, 4}, {5, -1}};
     set_shape_symbols(out_size_shape);
 
     const auto output_size = std::make_shared<Parameter>(element::u64, out_size_shape);
@@ -223,7 +223,7 @@ TYPED_TEST_P(PriorBoxTest, preseve_partial_values_inf_bound) {
 
     EXPECT_EQ(op->get_output_size(), 1);
     EXPECT_EQ(op->get_output_element_type(0), element::f32);
-    EXPECT_EQ(op->get_output_partial_shape(0), PartialShape({2, {0, -1}}));
+    EXPECT_EQ(op->get_output_partial_shape(0), PartialShape({2, {100, -1}}));
     EXPECT_THAT(get_shape_symbols(op->get_output_partial_shape(0)), Each(nullptr));
 }
 
diff --git a/src/core/tests/type_prop/prior_box_clustered.cpp b/src/core/tests/type_prop/prior_box_clustered.cpp
index 3470d798df6eec..ba5a3603aff6e7 100644
--- a/src/core/tests/type_prop/prior_box_clustered.cpp
+++ b/src/core/tests/type_prop/prior_box_clustered.cpp
@@ -186,7 +186,7 @@ TYPED_TEST_P(PriorBoxClusteredTest, preseve_partial_values_and_symbols_on_inputs
 }
 
 TYPED_TEST_P(PriorBoxClusteredTest, preseve_partial_values_inf_bound) {
-    auto out_size_shape = PartialShape{{1, 4}, {5, -1}};  // ShapeOf make 2nd Dim {0, -1}
+    auto out_size_shape = PartialShape{{1, 4}, {5, -1}};
     set_shape_symbols(out_size_shape);
 
     const auto output_size = std::make_shared<Parameter>(element::u64, out_size_shape);
@@ -197,7 +197,7 @@ TYPED_TEST_P(PriorBoxClusteredTest, preseve_partial_values_inf_bound) {
 
     EXPECT_EQ(op->get_output_size(), 1);
     EXPECT_EQ(op->get_output_element_type(0), element::f32);
-    EXPECT_EQ(op->get_output_partial_shape(0), PartialShape({2, {0, -1}}));
+    EXPECT_EQ(op->get_output_partial_shape(0), PartialShape({2, {60, -1}}));
     EXPECT_THAT(get_shape_symbols(op->get_output_partial_shape(0)), Each(nullptr));
 }
 
diff --git a/src/core/tests/type_prop/shape_of.cpp b/src/core/tests/type_prop/shape_of.cpp
index 3556bf654d64fc..e383ddbaaaeecf 100644
--- a/src/core/tests/type_prop/shape_of.cpp
+++ b/src/core/tests/type_prop/shape_of.cpp
@@ -151,5 +151,5 @@ TEST(type_prop, shape_of_3_dynamic_value_propagation_out_i32) {
     auto bc_param = std::make_shared<ov::op::v0::Parameter>(element::f32, Shape{1});
     auto bc = std::make_shared<op::v1::Broadcast>(bc_param, op);
 
-    EXPECT_EQ(bc->get_output_partial_shape(0), PartialShape({-1, -1, -1, -1, {1, 1021}}));
+    EXPECT_EQ(bc->get_output_partial_shape(0), PartialShape({{2, -1}, {3, -1}, -1, -1, {1, 1021}}));
 }
diff --git a/src/core/tests/type_prop/split.cpp b/src/core/tests/type_prop/split.cpp
index 7485b9487fdaa9..1f46200b5f3f5d 100644
--- a/src/core/tests/type_prop/split.cpp
+++ b/src/core/tests/type_prop/split.cpp
@@ -356,7 +356,7 @@ INSTANTIATE_TEST_SUITE_P(
            std::make_tuple(PartialShape{Dimension(2, 6), Dimension::dynamic(), Dimension(-1, 6), Dimension(7, -1)},
                            2,
                            std::vector<PartialShape>{{Dimension(2, 6), Dimension::dynamic()},
-                                                     {Dimension(-1, 6), Dimension::dynamic()}})),
+                                                     {Dimension(-1, 6), Dimension{7, -1}}})),
     PrintToStringParamName());
 
 TEST_P(SplitBoundTest, propagate_symbol_and_dynamic_value) {
diff --git a/src/core/tests/type_prop/squeeze.cpp b/src/core/tests/type_prop/squeeze.cpp
index 3e60a654f4bccd..c7d81fd97c2786 100644
--- a/src/core/tests/type_prop/squeeze.cpp
+++ b/src/core/tests/type_prop/squeeze.cpp
@@ -391,7 +391,7 @@ INSTANTIATE_TEST_SUITE_P(
            std::make_tuple(PartialShape{Dimension(-1)}, PartialShape{Dimension(-1)}),
            std::make_tuple(PartialShape{Dimension::dynamic(), 8}, PartialShape{Dimension::dynamic()}),
            std::make_tuple(PartialShape{Dimension(4, 8), Dimension::dynamic()}, PartialShape{Dimension(4, 8)}),
-           std::make_tuple(PartialShape{Dimension(20, -1), Dimension::dynamic()}, PartialShape::dynamic(1)),
+           std::make_tuple(PartialShape{Dimension(20, -1), Dimension::dynamic()}, PartialShape{{20, -1}}),
            std::make_tuple(PartialShape{Dimension(-1, 5), Dimension::dynamic()}, PartialShape{Dimension(-1, 5)}),
            std::make_tuple(PartialShape{15}, PartialShape{15}),
            std::make_tuple(PartialShape{2, 6}, PartialShape{2})),
diff --git a/src/core/tests/type_prop/tile.cpp b/src/core/tests/type_prop/tile.cpp
index d3a9e5377ea9d4..09768df294fbea 100644
--- a/src/core/tests/type_prop/tile.cpp
+++ b/src/core/tests/type_prop/tile.cpp
@@ -136,7 +136,7 @@ TEST_F(TypePropTileTest, preserve_partial_values_and_symbols) {
 
     const auto op = make_op(data, shape_of_repeats);
 
-    EXPECT_EQ(op->get_output_partial_shape(0), PartialShape({2, {2, 4}, {-1, 6}, -1, -1}));
+    EXPECT_EQ(op->get_output_partial_shape(0), PartialShape({2, {2, 4}, {-1, 6}, {2, -1}, -1}));
     EXPECT_THAT(get_shape_symbols(op->get_output_partial_shape(0)),
                 ElementsAre(nullptr, nullptr, nullptr, symbols[3], symbols[4]));
 }
diff --git a/src/core/tests/type_prop/top_k.cpp b/src/core/tests/type_prop/top_k.cpp
index d34c8f17fc1ba4..6c674efffd855d 100644
--- a/src/core/tests/type_prop/top_k.cpp
+++ b/src/core/tests/type_prop/top_k.cpp
@@ -309,12 +309,12 @@ TYPED_TEST_P(topk_type_prop, preserve_partial_values_and_symbols_k_is_interval)
 }
 
 TYPED_TEST_P(topk_type_prop, preserve_partial_values_and_symbols_k_is_interval_with_no_upper_bound) {
-    auto shape = PartialShape{{10, -1}};
+    auto shape = PartialShape{{4, -1}};
     auto k_symbols = set_shape_symbols(shape);
 
     const auto p_k = std::make_shared<Parameter>(element::i64, shape);
     const auto shape_of_k = std::make_shared<ShapeOf>(p_k);
-    // Squeeze make scalar but if interval value has no upper bound result will be {0,inf}
+    // Squeeze make scalar of interval value {4,inf}
     const auto k = std::make_shared<Squeeze>(shape_of_k, Constant::create(element::i64, Shape{}, {0}));
 
     auto data_shape = PartialShape{5, {2, 8}, {2, 100}};
@@ -322,24 +322,24 @@ TYPED_TEST_P(topk_type_prop, preserve_partial_values_and_symbols_k_is_interval_w
     const auto data = std::make_shared<Parameter>(element::f32, data_shape);
 
     {
-        // dim{5} k{0,inf} -> {0,5}
+        // dim{5} k{4,inf} -> {4,5}
         const auto op = this->make_op(data, k, 0, "max", "value");
         EXPECT_THAT(op->get_output_partial_shape(0),
-                    AllOf(PartialShape({{0, 5}, {2, 8}, {2, 100}}),
+                    AllOf(PartialShape({{4, 5}, {2, 8}, {2, 100}}),
                           ResultOf(get_shape_symbols, ElementsAre(nullptr, symbols[1], symbols[2]))));
     }
     {
-        // dim{2,8} k{0,inf} -> {0,8}
+        // dim{2,8} k{4,inf} -> {2,8}
         const auto op = this->make_op(data, k, 1, "max", "value");
         EXPECT_THAT(op->get_output_partial_shape(0),
-                    AllOf(PartialShape({5, {0, 8}, {2, 100}}),
+                    AllOf(PartialShape({5, {2, 8}, {2, 100}}),
                           ResultOf(get_shape_symbols, ElementsAre(symbols[0], nullptr, symbols[2]))));
     }
     {
-        // dim{2,100} k{0,inf} -> {0,100}
+        // dim{2,100} k{4,inf} -> {2,100}
         const auto op = this->make_op(data, k, 2, "max", "value");
         EXPECT_THAT(op->get_output_partial_shape(0),
-                    AllOf(PartialShape({5, {2, 8}, {0, 100}}),
+                    AllOf(PartialShape({5, {2, 8}, {2, 100}}),
                           ResultOf(get_shape_symbols, ElementsAre(symbols[0], symbols[1], nullptr))));
     }
 }
diff --git a/src/core/tests/type_prop/unsqueeze.cpp b/src/core/tests/type_prop/unsqueeze.cpp
index 4796dea9f22bd2..b3543632272686 100644
--- a/src/core/tests/type_prop/unsqueeze.cpp
+++ b/src/core/tests/type_prop/unsqueeze.cpp
@@ -264,7 +264,7 @@ INSTANTIATE_TEST_SUITE_P(
            std::make_tuple(PartialShape{Dimension(-1)}, PartialShape{Dimension(-1)}),
            std::make_tuple(PartialShape{Dimension::dynamic(), 8}, PartialShape{Dimension::dynamic()}),
            std::make_tuple(PartialShape{Dimension(2, 5), Dimension::dynamic()}, PartialShape{Dimension(2, 5)}),
-           std::make_tuple(PartialShape{Dimension(2, -1), Dimension::dynamic()}, PartialShape::dynamic(1)),
+           std::make_tuple(PartialShape{Dimension(2, -1), Dimension::dynamic()}, PartialShape{{2, -1}}),
            std::make_tuple(PartialShape{Dimension(-1, 3), Dimension::dynamic()}, PartialShape{Dimension(-1, 3)}),
            std::make_tuple(PartialShape{5}, PartialShape{5}),
            std::make_tuple(PartialShape{2, 6}, PartialShape{2})),

From 60325deab04f877d161bb369cd2af6b44104a219 Mon Sep 17 00:00:00 2001
From: Piotr Kowalczyk <piotr.kowalczyk@intel.com>
Date: Thu, 19 Sep 2024 14:46:15 +0200
Subject: [PATCH 14/49] [GPU]: Fix for failing i16 elementwise functional test.
 (#26383)

### Details:
- Fix for failing GPU functional i16 test. The problem is that i16 input
is wrongly converted to f32 in constant and parameter op.
- Had to disable i16 case for Deformable conv, which won't work with
this fix. THe motivation is that Deformable conv on GPU supports only
f16, f32 and int8 types - does not support i16 case, which was working
only due to some implicit type transformation which this PR changes.
---
 .../src/plugin/transformations_pipeline.cpp     | 17 +++++++++--------
 .../deformable_convolution.cpp                  |  3 +--
 .../single_layer_tests/eltwise.cpp              |  1 +
 3 files changed, 11 insertions(+), 10 deletions(-)

diff --git a/src/plugins/intel_gpu/src/plugin/transformations_pipeline.cpp b/src/plugins/intel_gpu/src/plugin/transformations_pipeline.cpp
index 1812c3721d6793..40c7ab48c486cb 100644
--- a/src/plugins/intel_gpu/src/plugin/transformations_pipeline.cpp
+++ b/src/plugins/intel_gpu/src/plugin/transformations_pipeline.cpp
@@ -421,14 +421,15 @@ void TransformationsPipeline::apply(std::shared_ptr<ov::Model> func) {
         manager.register_pass<ov::pass::ConvertPad12ToPad1, false>();
         manager.register_pass<DecomposeReduceForScalarOutput>();
 
-        precisions_map int_convert_precision_map {
-                {ov::element::i64, ov::element::i32},
-                {ov::element::u64, ov::element::i32},
-                {ov::element::u16, ov::element::i32},
-                {ov::element::u32, ov::element::i32},
-                {ov::element::boolean, ov::element::u8},
-                {ov::element::i4, ov::element::i8},
-                {ov::element::u4, ov::element::u8},
+        precisions_map int_convert_precision_map{
+            {ov::element::i64, ov::element::i32},
+            {ov::element::u64, ov::element::i32},
+            {ov::element::i16, ov::element::i32},
+            {ov::element::u16, ov::element::i32},
+            {ov::element::u32, ov::element::i32},
+            {ov::element::boolean, ov::element::u8},
+            {ov::element::i4, ov::element::i8},
+            {ov::element::u4, ov::element::u8},
         };
 
         manager.register_pass<ov::pass::Validate>();
diff --git a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/deformable_convolution.cpp b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/deformable_convolution.cpp
index 2936f6f50e2981..89f4666329f2eb 100644
--- a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/deformable_convolution.cpp
+++ b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/deformable_convolution.cpp
@@ -9,8 +9,7 @@ namespace {
 using ov::test::DeformableConvolutionLayerTest;
 const std::vector<ov::element::Type> netPrecisions = {
     ov::element::f32,
-    ov::element::f16,
-    ov::element::i16
+    ov::element::f16
 };
 
 /* ============= 2D DeformableConvolution ============= */
diff --git a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/eltwise.cpp b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/eltwise.cpp
index 85ba33753a7407..964f8b5b21c47e 100644
--- a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/eltwise.cpp
+++ b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/eltwise.cpp
@@ -73,6 +73,7 @@ std::vector<EltwiseTypes> smoke_intOnly_eltwiseOpTypes = {
 
 std::vector<ov::test::ElementType> intOnly_netPrecisions = {
         ov::element::i32,
+        ov::element::i16,
         ov::element::u16
 };
 

From b368c3189f769e28074adc358f4fdf93911e683a Mon Sep 17 00:00:00 2001
From: Alicja Miloszewska <alicja.miloszewska@intel.com>
Date: Thu, 19 Sep 2024 15:30:34 +0200
Subject: [PATCH 15/49] [OV JS] Prepare testing and codestyle guidelines
 (#26616)

### Details:
 - Add guidelines how to test new js api functionality
 - Add guide how to extend JS API functionality


### Tickets:
- [CVS-151489](https://jira.devtools.intel.com/browse/CVS-151489)
[CVS-151492](https://jira.devtools.intel.com/browse/CVS-151492)

---------

Co-authored-by: Tatiana Savina <tatiana.savina@intel.com>
---
 src/bindings/js/docs/code_examples.md | 140 ++++++++++++++++++++++++++
 src/bindings/js/docs/test_examples.md |  87 ++++++++++++++++
 2 files changed, 227 insertions(+)
 create mode 100644 src/bindings/js/docs/code_examples.md
 create mode 100644 src/bindings/js/docs/test_examples.md

diff --git a/src/bindings/js/docs/code_examples.md b/src/bindings/js/docs/code_examples.md
new file mode 100644
index 00000000000000..13bfa14812d54b
--- /dev/null
+++ b/src/bindings/js/docs/code_examples.md
@@ -0,0 +1,140 @@
+# How to extend the OpenVINO™ JavaScript API code
+
+## Build the OpenVINO™ JavaScript API 
+For detailed build instructions, refer to the [OpenVINO™ JavaScript API documentation](./README.md).
+
+## Project's naming conventions
+When implementing the C++ sources for the JavaScript API, it is essential to adhere to the OpenVINO naming conventions described in the [OpenVINO Coding Style Guide](../../../../docs/dev/coding_style.md). In summary, the naming style employs `Snake Case` for methods, functions, and variables, while `Camel Case` is used for class names. Additionally, the naming of entities in the C++ sources should closely mirror their equivalents in the C++ API to maintain consistency.
+
+For methods that are exposed to JavaScript, the naming convention transitions to `Camel Case`, aligning with common JavaScript practices. As an example, a method in the C++ API named `get_element_type` would be represented in the JavaScript API as `getElementType()`.
+
+## node-addon-api module
+
+[node addon api](https://github.com/nodejs/node-addon-api) is used to create OpenVINO JavaScript API for Node.js. The quickest way to learn is to follow the official [examples](https://github.com/nodejs/node-addon-examples). It is recommended to check out the tutorial on [how to create a JavaScript object from a C++ object](https://github.com/nodejs/node-addon-examples/tree/main/src/2-js-to-native-conversion/object-wrap-demo/node-addon-api).
+
+
+
+
+
+## Adding a new class and method
+To introduce a new `MyTensor` class that interacts with the `ov::Tensor` class, follow these steps:
+ - The class should facilitate construction from an ov::Tensor instance and allow initialization from a JavaScript element type and shape.
+ - It should also provide a getElementType method that retrieves the ov::Tensor element type.
+
+Begin by creating a header file for the `MyTensor` class in the OpenVINO repository at `<openvino_repo>/src/bindings/js/node/include/my_tensor.hpp`. This file should contain the necessary includes and class definitions:
+```cpp
+class MyTensor : public Napi::ObjectWrap<MyTensor> {
+public:
+    // Constructor for the wrapper class 
+    MyTensor(const Napi::CallbackInfo& info);
+
+    // It returns a JavaScript class definition
+    static Napi::Function get_class(Napi::Env env);
+
+    // It returns the element type of ov::Tensor
+    Napi::Value get_element_type(const Napi::CallbackInfo& info);
+
+private:
+    ov::Tensor _tensor;
+};
+```
+The implementation of the class methods should be placed in a source file at `<openvino_repo>/src/bindings/js/node/src/my_tensor.cpp`
+```cpp
+MyTensor::MyTensor(const Napi::CallbackInfo& info) : Napi::ObjectWrap<MyTensor>(info) {
+    std::vector<std::string> allowed_signatures;
+
+    try {
+        if (ov::js::validate<Napi::String, Napi::Array>(info, allowed_signatures)) {
+            const auto type = js_to_cpp<ov::element::Type_t>(info, 0);
+            const auto& shape = js_to_cpp<ov::Shape>(info, 1);
+            this->_tensor = ov::Tensor(type, shape);
+        } else {
+            OPENVINO_THROW("'MyTensor' constructor", ov::js::get_parameters_error_msg(info, allowed_signatures));
+        }
+    } catch (std::exception& err) {
+        reportError(info.Env(), err.what());
+    }
+}
+
+Napi::Function MyTensor::get_class(Napi::Env env) {
+    return DefineClass(env,
+                       "MyTensor",
+                       {
+                           InstanceMethod("getElementType", &MyTensor::get_element_type),
+                       });
+}
+
+Napi::Value MyTensor::get_element_type(const Napi::CallbackInfo& info) {
+    return Napi::String::New(info.Env(), _tensor.get_element_type().to_string());
+}
+```
+Finally, update the `CMakeLists.txt` file at `<openvino_repo>/src/bindings/js/node/` to include the new source file in the build process:
+```cmake
+add_library(${PROJECT_NAME} SHARED
+    ${CMAKE_CURRENT_SOURCE_DIR}/src/my_tensor.cpp
+)
+```
+
+### Argument validation and conversion
+
+When binding JavaScript arguments with C++ functions, it is crucial to validate and convert the arguments appropriately. The template `ov::js::validate` function is a utility that facilitates this process. It is particularly useful for handling different overloads of functions and ensuring standardized error messages when arguments do not match expected signatures.
+Before implementing a new conversion function, such as `js_to_cpp<ov::Shape>`, review the existing [helper methods](../../node/include/helper.hpp) to see if one already meets your requirements.
+
+### New class initialization
+When a new class is introduced to the `openvino-node` module, it must be initialized upon module loading. This is done in the [addon.cpp](../../src/addon.cpp) file. The initialization process registers the class with the Node.js environment so that it can be used within JavaScript code.
+```cpp
+Napi::Object init_module(Napi::Env env, Napi::Object exports) {
+    auto addon_data = new AddonData();
+    env.SetInstanceData<AddonData>(addon_data);
+    init_class(env, exports, "MyTensor", &MyTensor::get_class, addon_data->my_tensor);
+
+    return exports;
+}
+```
+To keep track of the JavaScript class definitions, they are kept in `<openvino_repo>/src/bindings/js/node/include/addon.hpp`.
+```cpp
+struct AddonData {
+    Napi::FunctionReference my_tensor;
+     // and other class references
+};
+```
+
+### Document the new functionality
+The last step is to add the TypeScript type definitions and describe the new functionality.
+```typescript
+/**
+ * The {@link MyTensor} class and its documentation.
+ */
+interface MyTensor {
+  /**
+   * It gets the tensor element type.
+   */
+  getElementType(): element;
+
+}
+interface MyTensorConstructor {
+  /**
+   * It constructs a tensor using the element type and shape. The new tensor
+   * data will be allocated by default.
+   * @param type The element type of the new tensor.
+   * @param shape The shape of the new tensor.
+   */
+  new(type: element | elementTypeString, shape: number[]): MyTensor;
+}
+
+export interface NodeAddon {
+  MyTensor: MyTensorConstructor,
+}
+```
+
+
+## Testing the new code
+
+Now that coding is finished, remember to rebuild the project and test it out.
+
+To learn how to test your code, refer to the guide on [how to test OpenVINO™ JavaScript API.](./test_examples.md) 
+
+## See also
+ * [OpenVINO™ README](../../../../README.md)
+ * [OpenVINO™ bindings README](../../README.md)
+ * [Developer documentation](../../../../docs/dev/index.md)
\ No newline at end of file
diff --git a/src/bindings/js/docs/test_examples.md b/src/bindings/js/docs/test_examples.md
new file mode 100644
index 00000000000000..b8ff0c8ff7c9d0
--- /dev/null
+++ b/src/bindings/js/docs/test_examples.md
@@ -0,0 +1,87 @@
+# How to test the OpenVINO™ JavaScript API
+
+## Build the OpenVINO™ JavaScript API 
+For detailed build instructions, refer to the [OpenVINO™ JavaScript API documentation](./README.md).
+
+
+## Run OpenVINO™ JavaScript API tests
+*For simplicity, begin by navigating to the [main JavaScript API folder](./../node):*
+```shell
+cd <openvino_repo>/src/bindings/js/node
+```
+
+Use this command to run OpenVINO JavaScript API tests:
+```shell
+npm run test
+```
+
+To run specific test files, you can pass one or more glob patterns:
+```shell
+node --test "tests/unit/core.test.js" "tests/unit/*model.test.js" 
+```
+
+Before executing individual test files, a one-time setup is required. If you have not previously executed `npm run test`, initiate the setup by running the following command:
+
+```shell
+npm run test_setup
+``` 
+
+More information on running tests from the command line can be found in the [Node.js documentation]( https://nodejs.org/docs/latest/api/test.html#running-tests-from-the-command-line).
+
+
+## Check the code style of the JavaScript API
+*ESLint* is a tool to enforce a consistent coding style and to identify and fix potential issues in JavaScript code.
+
+To check the code style of the JavaScript API, run the following commands:
+```shell
+npm run lint
+```
+*ESLint* can automatically fix many of the issues it detects. Use following command to run *ESLint* with the fix option on a single file:
+```shell
+npx eslint --fix "tests/unit/core.test.js"
+```
+
+It is recommended to run the code style check each time new tests are added.
+
+
+## Writing OpenVINO™ JavaScript API tests
+### Before start
+Follow and complete [Examples of OpenVINO™ JavaScript API code](./code_examples.md).
+
+
+
+### Adding new test-case in the correct place
+Each new test should verify the correct behavior of the new functionality (e.g. class, method).
+
+Unit test files are located in the `<openvino_repo>/src/bindings/js/node/tests/unit/` directory and their names correspond to the class/module to be tested.
+
+Always add tests to the correct locations and create new files only when necessary. *Remember to include the license on top of each new file*.
+
+### Test writing guidelines 
+Each test file starts with a `describe` block to group all tests related to a specific class or module. The name of the `describe` block should match the name of the class or module being tested, for example *ov.Core tests*.
+
+Within the `describe` block, individual tests are defined using `test` or `it` blocks, with the name of the test reflecting what is being tested. If multiple tests relate to the same method, they can be grouped within a nested `describe` block.
+
+ ```js
+ const { describe, it, beforeEach } = require('node:test');
+describe('ov.Tensor tests', () => {
+  test('MyTensor.getElementType()', () => {
+    const tensor = new ov.MyTensor(ov.element.f32, [1, 3]);
+    assert.strictEqual(tensor.getElementType(), ov.element.f32);
+  });
+
+});
+ ```
+When writing tests, keep the following best practices in mind:
+
+ * Focus on testing general usage and edge cases, but avoid excessive testing that leads to redundant test cases and can slow down validation pipelines.
+  * Avoid initializing shared variables outside of `test` or `beforeEach` blocks to prevent tests from affecting each other.
+ * Do not test built-in language features; instead, focus on the functionality provided by your code.
+ * Use hardcoded expected results to verify the correctness of your code. Alternatively, generate reference values at runtime using reliable libraries or methods.
+ * Extract and reuse common code snippets, such as helper functions or setup code, to improve test readability and maintainability.
+
+
+## See also
+ * [OpenVINO™ README](../../../../README.md)
+ * [OpenVINO™ bindings README](../../README.md)
+ * [Developer documentation](../../../../docs/dev/index.md)

From 76fab4c78d1f685c6b35ef5bab0094bcd69fd264 Mon Sep 17 00:00:00 2001
From: Tatiana Savina <tatiana.savina@intel.com>
Date: Thu, 19 Sep 2024 15:35:07 +0200
Subject: [PATCH 16/49] [DOCS] Port benchmark 24.4  (#26690)

### Details:
 - *item1*
 - *...*

### Tickets:
 - *ticket-id*

Co-authored-by: Karol Blaszczak <karol.blaszczak@intel.com>
---
 .../about-openvino/performance-benchmarks.rst |    52 +-
 .../generative-ai-performance.rst             |    18 +-
 .../model-accuracy-int8-fp32.rst              |   145 +-
 .../performance-benchmarks-faq.rst            |    24 +-
 .../OV-2024.3-Performance-Data.xlsx           |   Bin 305774 -> 0 bytes
 .../OV-2024.3-platform_list.pdf               |   Bin 239931 -> 0 bytes
 .../OV-2024.3-system-info-detailed.xlsx       |   Bin 78345 -> 0 bytes
 .../OV-2024.4-Performance-Data.xlsx           |   Bin 0 -> 329057 bytes
 .../OV-2024.4-platform_list.pdf               |   Bin 0 -> 240967 bytes
 .../OV-2024.4-system-info-detailed.xlsx       |   Bin 0 -> 83914 bytes
 .../benchmarks_files/data/graph-data-ov.json  | 12042 ++++++++++------
 11 files changed, 7404 insertions(+), 4877 deletions(-)
 delete mode 100644 docs/sphinx_setup/_static/benchmarks_files/OV-2024.3-Performance-Data.xlsx
 delete mode 100644 docs/sphinx_setup/_static/benchmarks_files/OV-2024.3-platform_list.pdf
 delete mode 100644 docs/sphinx_setup/_static/benchmarks_files/OV-2024.3-system-info-detailed.xlsx
 create mode 100644 docs/sphinx_setup/_static/benchmarks_files/OV-2024.4-Performance-Data.xlsx
 create mode 100644 docs/sphinx_setup/_static/benchmarks_files/OV-2024.4-platform_list.pdf
 create mode 100644 docs/sphinx_setup/_static/benchmarks_files/OV-2024.4-system-info-detailed.xlsx

diff --git a/docs/articles_en/about-openvino/performance-benchmarks.rst b/docs/articles_en/about-openvino/performance-benchmarks.rst
index 7f838d62b18f1c..aa60c44a2ad5c8 100644
--- a/docs/articles_en/about-openvino/performance-benchmarks.rst
+++ b/docs/articles_en/about-openvino/performance-benchmarks.rst
@@ -11,9 +11,9 @@ Performance Benchmarks
    :hidden:
 
    Efficient LLMs for AI PC <performance-benchmarks/generative-ai-performance>
-   performance-benchmarks/performance-benchmarks-faq
+   Performance Information F.A.Q. <performance-benchmarks/performance-benchmarks-faq>
    OpenVINO Accuracy <performance-benchmarks/model-accuracy-int8-fp32>
-   performance-benchmarks/getting-performance-numbers
+   Getting Performance Numbers <performance-benchmarks/getting-performance-numbers>
 
 
 This page presents benchmark results for
@@ -22,7 +22,7 @@ and :doc:`OpenVINO Model Server <../ovms_what_is_openvino_model_server>`, for a
 selection of public neural networks and Intel® devices. The results may help you decide which
 hardware to use in your applications or plan AI workload for the hardware you have already
 implemented in your solutions. Click the buttons below to see the chosen benchmark data.
-For more detailed view of performance numbers for generative AI models, check the
+For a more detailed view of performance numbers for generative AI models, check the
 :doc:`Generative AI Benchmark Results <./performance-benchmarks/generative-ai-performance>`
 
 .. grid:: 1 1 2 2
@@ -49,7 +49,7 @@ For more detailed view of performance numbers for generative AI models, check th
          :material-regular:`bar_chart;1.4em` OVMS Benchmark Graphs
 
 
-Please visit the tabs below for more information on key performance indicators and workload parameters.
+Key performance indicators and workload parameters.
 
 .. tab-set::
 
@@ -57,32 +57,10 @@ Please visit the tabs below for more information on key performance indicators a
       :sync: throughput
 
       For Vision and NLP Models this measures the number of inferences delivered within a latency threshold
-      (for example, number of Frames Per Second - FPS). 
-      For GenAI (or Large Language Models) this measures the token rate after the first token aka. 2nd token 
-      throughput rate which is presented as tokens/sec. Please click on the "Workload Parameters" tab to 
-      learn more about input/output token lengths, etc. 
-
-   .. tab-item:: Value
-      :sync: value
-
-      While throughput is important, what is more critical in edge AI deployments is
-      the performance efficiency or performance-per-cost. Application performance in
-      throughput per dollar of system cost is the best measure of value. The value KPI is
-      calculated as “Throughput measured as inferences per second / price of inference engine”.
-      This means for a 2 socket system 2x the price of a CPU is used. Prices are as per
-      date of benchmarking and sources can be found as links in the Hardware Platforms (PDF)
-      description below.
-
-   .. tab-item:: Efficiency
-      :sync: efficiency
-
-      System power is a key consideration from the edge to the data center. When selecting
-      deep learning solutions, power efficiency (throughput/watt) is a critical factor to
-      consider. Intel designs provide excellent power efficiency for running deep learning
-      workloads. The efficiency KPI is calculated as “Throughput measured as inferences per
-      second / TDP of inference engine”. This means for a 2 socket system 2x the power
-      dissipation (TDP) of a CPU is used. TDP-values are as per date of benchmarking and sources
-      can be found as links in the Hardware Platforms (PDF) description below.
+      (for example, number of Frames Per Second - FPS).
+      For GenAI (or Large Language Models) this measures the token rate after the first token aka. 2nd token
+      throughput rate which is presented as tokens/sec. Please click on the "Workload Parameters" tab to
+      learn more about input/output token lengths, etc.
 
    .. tab-item:: Latency
       :sync: latency
@@ -96,7 +74,7 @@ Please visit the tabs below for more information on key performance indicators a
       example an industrial robot's response to actions in its environment or obstacle avoidance
       for autonomous vehicles.
       For Transformer models like Stable-Diffusion this measures the time it takes to convert the prompt
-      or input text into a finished image. It is presented in seconds. 
+      or input text into a finished image. It is presented in seconds.
 
    .. tab-item:: Workload Parameters
       :sync: workloadparameters
@@ -130,21 +108,21 @@ For a listing of all platforms and configurations used for testing, refer to the
 
    .. grid-item::
 
-      .. button-link:: ../_static/benchmarks_files/OV-2024.3-platform_list.pdf
+      .. button-link:: ../_static/benchmarks_files/OV-2024.4-platform_list.pdf
          :color: primary
          :outline:
          :expand:
 
          :material-regular:`download;1.5em` Click for Hardware Platforms [PDF]
 
-      .. button-link:: ../_static/benchmarks_files/OV-2024.3-system-info-detailed.xlsx
+      .. button-link:: ../_static/benchmarks_files/OV-2024.4-system-info-detailed.xlsx
          :color: primary
          :outline:
          :expand:
 
          :material-regular:`download;1.5em` Click for Configuration Details [XLSX]
 
-      .. button-link:: ../_static/benchmarks_files/OV-2024.3-Performance-Data.xlsx
+      .. button-link:: ../_static/benchmarks_files/OV-2024.4-Performance-Data.xlsx
          :color: primary
          :outline:
          :expand:
@@ -159,9 +137,9 @@ processing) and then reports on the inferences per second (or Frames Per Second)
 OpenVINO™ Model Server (OVMS) employs the Intel® Distribution of OpenVINO™ toolkit runtime
 libraries and exposes a set of models via a convenient inference API over gRPC or HTTP/REST.
 Its benchmark results are measured with the configuration of multiple-clients-single-server,
-using two hardware platforms connected by ethernet. Network bandwidth depends on both, platforms
-and models under investigation. It is set not to be a bottleneck for workload intensity. The
-connection is dedicated only to measuring performance.
+using two hardware platforms connected by ethernet. Network bandwidth depends on both platforms
+and models used. It is set not to be a bottleneck for workload intensity. The connection is
+dedicated only to measuring performance.
 
 .. dropdown:: See more details about OVMS benchmark setup
 
diff --git a/docs/articles_en/about-openvino/performance-benchmarks/generative-ai-performance.rst b/docs/articles_en/about-openvino/performance-benchmarks/generative-ai-performance.rst
index ab6b96f62ffc2f..35e09f91f72b9c 100644
--- a/docs/articles_en/about-openvino/performance-benchmarks/generative-ai-performance.rst
+++ b/docs/articles_en/about-openvino/performance-benchmarks/generative-ai-performance.rst
@@ -25,20 +25,11 @@ running on an Intel® Core™ Ultra 7-165H based system, on built-in GPUs.
          :header-rows: 1
          :file:  ../../_static/download/llm_models.csv
 
-   .. tab-item:: OpenVINO Model Server
-
-      .. csv-table::
-         :class: modeldata stripe
-         :name: supportedModelsTableOvms
-         :header-rows: 1
-         :file:  ../../_static/download/llm_models_ovms.csv
-
-
 
 For complete information on the system config, see:
-`Hardware Platforms [PDF] <https://docs.openvino.ai/2024/_static/benchmarks_files/OV-2024.3-platform_list.pdf>`__
+`Hardware Platforms [PDF] <https://docs.openvino.ai/2024/_static/benchmarks_files/OV-2024.4-platform_list.pdf>`__
 
-To view the data in an editable form, you can download the .csv files here:
+To view the data in an editable form, you can download the .csv file here:
 
 .. grid:: 1 1 2 2
    :gutter: 4
@@ -52,9 +43,4 @@ To view the data in an editable form, you can download the .csv files here:
 
          :material-regular:`download;1.5em` Click for OpenVINO LLM results [CSV]
 
-      .. button-link:: ../../_static/download/llm_models_ovms.csv
-         :color: primary
-         :outline:
-         :expand:
 
-         :material-regular:`download;1.5em` Click for OpenVINO Model Server results [CSV]
diff --git a/docs/articles_en/about-openvino/performance-benchmarks/model-accuracy-int8-fp32.rst b/docs/articles_en/about-openvino/performance-benchmarks/model-accuracy-int8-fp32.rst
index 9166409c7d5ee0..8b93e6a1aebe7b 100644
--- a/docs/articles_en/about-openvino/performance-benchmarks/model-accuracy-int8-fp32.rst
+++ b/docs/articles_en/about-openvino/performance-benchmarks/model-accuracy-int8-fp32.rst
@@ -29,78 +29,78 @@ the table for more information.
      - spearman@cosine
      - 3.33%
      - 3.22%
-     - 3.69%
-     - 3.28%
+     - 3.05%
+     - 2.88%
    * - bert-large-uncased-whole-word-masking-squad-0001
      - SQUAD_v1_1_bert_msl384_mql64_ds128_lowercase
      - F1
-     - 0.19%
-     - 0.06%
+     - 0.12%
+     - 0.03%
      - 0.03%
-     - 0.11%
+     - 0.28%
    * - efficientdet-d0
      - COCO2017_detection_91cl
      - coco_precision
-     - -0.9%
-     - -0.63%
-     - -0.61%
-     - -0.62%
+     - 0.00%
+     - -0.52%
+     - -0.54%
+     - -0.60%
    * - mask_rcnn_resnet50_atrous_coco
      - COCO2017_detection_91cl_bkgr
      - coco_orig_precision
-     - -5.64%
-     - -0.30%
-     - -0.21%
-     - -0.28%
+     - 0.05%
+     - 0.03%
+     - 0.08%
+     - -0.09%
    * - mobilenet-v2
      - ImageNet2012
      - accuracy @ top1
+     -
      - -0.87%
-     - -0.87%
-     - -0.89%
-     - -0.95%
+     - -0.88%
+     - -0.88%
    * - resnet-50
      - ImageNet2012
      - accuracy @ top1
-     - -0.2%
+     - -0.17%
      - -0.18%
      - -0.18%
-     - -0.13%
+     - -0.16%
    * - ssd-resnet34-1200
      - COCO2017_detection_80cl_bkgr
      - map
      - -0.03%
      - -0.02%
      - -0.03%
-     - -0.0%
+     - 0.02%
    * - ssd-mobilenet-v1-coco
      - COCO2017_detection_80cl_bkgr
      - coco-precision
-     - -2.75%
-     - -0.11%
+     - -2.74%
      - -0.11%
-     - -0.08%
+     - -0.13%
+     - -0.12%
    * - unet-camvid-onnx-0001
      - CamVid_12cl
      - mean_iou @ mean
      - -6.28%
      - 6.45%
      - 6.46%
-     - 6.40%
-   * - yolo_v3_tiny
+     - 6.43%
+   * - yolo_v5m
      - COCO2017_detection_80cl
      - map
-     - -0.30%
-     - -0.43%
-     - -0.43%
-     - -0.87%
+     - -0.40%
+     - -0.32%
+     - -0.32%
+     - -0.31%
    * - yolo_v8n
      - COCO2017_detection_80cl
      - map
      - -0.01%
      - -0.04%
-     - 0.04%
-     - -0.08%
+     - -0.07%
+     - 0.05%
 
 .. list-table:: Model Accuracy for BF16, FP32 and FP16 (FP16: Flex-170 only. BF16: Xeon(R) 8480+ only)
    :header-rows: 1
@@ -119,32 +119,32 @@ the table for more information.
      - 0.00%
      - 0.00%
      - 0.00%
-     - -0.03%
+     - -0.01%
      - 0.01%
    * - bert-large-uncased-whole-word-masking-squad-0001
      - SQUAD_v1_1_bert_msl384_mql64_ds128_lowercase
      - F1
      - 0.04%
      - 0.04%
-     - 0.04%
      - 0.06%
-     - %
+     - 0.06%
+     - 0.04%
    * - efficientdet-d0
      - COCO2017_detection_91cl
      - coco_precision
+     - 0.01%
      - -0.02%
+     - 0.01%
+     - 0.00%
      - -0.02%
-     - -0.02%
-     - -0.02%
-     - 0.04%
    * - mask_rcnn_resnet50_atrous_coco
      - COCO2017_detection_91cl_bkgr
      - coco_orig_precision
+     - -0.01%
+     - -0.01%
+     - -0.01%
+     - -0.05%
      - 0.00%
-     - 0.00%
-     - 0.00%
-     - 0.01%
-     - -0.02%
    * - mobilenet-v2
      - ImageNet2012
      - accuracy @ top1
@@ -164,19 +164,19 @@ the table for more information.
    * - ssd-resnet34-1200
      - COCO2017_detection_80cl_bkgr
      - map
-     - 0.00%
+     - 0.02%
      - 0.00%
      - 0.00%
      - -0.02%
-     - 0.02%
+     - 0.04%
    * - ssd-mobilenet-v1-coco
      - COCO2017_detection_80cl_bkgr
      - coco-precision
+     - -0.08%
      - 0.01%
      - 0.01%
+     - 0.08%
      - 0.01%
-     - 0.04%
-     - -0.02%
    * - unet-camvid-onnx-0001
      - CamVid_12cl
      - mean_iou @ mean
@@ -185,68 +185,75 @@ the table for more information.
      - 0.00%
      - -0.03%
      - -0.03%
-   * - yolo_v3_tiny
+   * - yolo_v5m
      - COCO2017_detection_80cl
      - map
      - 0.00%
-     - 0.00%
-     - 0.00%
-     - 0.25%
-     - -0.01%
+     - 0.05%
+     - 0.05%
+     - 0.07%
+     - 0.07%
    * - yolo_v8n
      - COCO2017_detection_80cl
      - map
      - 0.00%
      - 0.00%
+     - 0.01%
+     - 0.05%
      - 0.00%
-     - 0.04%
-     - -0.02%
 
-.. list-table:: Model Accuracy for VNNI-FP16, VNNI-INT8, VNNI-INT4 and MTL-INT4 (Core Ultra)
+.. list-table:: Model Accuracy for VNNI-FP16, VNNI-INT4, AMX-FP16 and MTL-INT4 (Core Ultra iGPU)
    :header-rows: 1
 
    * - OpenVINO™  Model name
      - dataset
      - Metric Name
      - A, VNNI-FP16
-     - B, VNNI-INT8
-     - C, VNNI-INT4
+     - B, VNNI-INT4
+     - C, FAMX-FP16
      - D, MTL-INT4
    * - chatGLM2-6b
      - Wikiset
      - ppl
-     - 5,24
-     - 5.17
-     - 6.86
-     - 6.87
+     - 5.24
+     - 6.03
+     - 5.24
+     - 6.03
    * - Falcon-7b-instruct
      - Wikitext
      - ppl
      - 1.65
+     - 1.76
      - 1.65
-     - 1.82
-     - 1.82
+     - 1.76
    * - Llama-2-7b-chat
      - Wikiset
      - ppl
-     - 1.54
      - 1.58
      - 1.59
+     - 1.91
      - 1.59
+   * - Llama-3-8b
+     - Wikiset
+     - ppl
+     - 1.54
+     - 1.56
+     - 1.17
+     - 1.57
    * - Mistral-7b
      - Wikitext
      - ppl
      - 1.48
-     - 1.48
      - 1.49
+     - 1.39
      - 1.49
-   * - Stable-Diffusion-V2-1
-     - LIAON-5B
-     - CLIP
-     -
-     -
-     -
-     -
+   * - Phi3-mini-4k-instruct
+     - Wikitext
+     - ppl
+     - 1.52
+     - 1.56
+     - 1.52
+     - 1.56
 
 Notes: For all accuracy metrics a "-", (minus sign), indicates an accuracy drop.
 For perplexity (ppl) the values do not indicate a deviation from a reference but are the actual measured
diff --git a/docs/articles_en/about-openvino/performance-benchmarks/performance-benchmarks-faq.rst b/docs/articles_en/about-openvino/performance-benchmarks/performance-benchmarks-faq.rst
index e8e099c5ae4b1f..c55d3f44451f1c 100644
--- a/docs/articles_en/about-openvino/performance-benchmarks/performance-benchmarks-faq.rst
+++ b/docs/articles_en/about-openvino/performance-benchmarks/performance-benchmarks-faq.rst
@@ -2,8 +2,6 @@ Performance Information F.A.Q.
 ==============================
 
 
-
-
 .. meta::
    :description: Check the F.A.Q. for performance benchmarks in Intel® Distribution of OpenVINO™ toolkit.
 
@@ -64,11 +62,19 @@ Performance Information F.A.Q.
         - Meta AI
         - Auto regressive language
         - 4096
+      * - `Llama-3-8b <https://ai.meta.com/llama/>`__
+        - Meta AI
+        - Auto regressive language
+        - 8192
       * - `Mistral-7b <https://huggingface.co/mistralai/Mistral-7B-v0.1>`__
         - Mistral AI
         - Auto regressive language
         - 4096
-      * - `Stable-Diffusion-V2-1 <https://huggingface.co/stabilityai/stable-diffusion-2-1>`__
+      * - `Phi3-4k-mini <https://huggingface.co/microsoft/Phi-3-mini-4k-instruct>`__
+        - Huggingface
+        - Auto regressive language
+        - 4096
+      * - `Stable-Diffusion-V1-5 <https://https://huggingface.co/stable-diffusion-v1-5/stable-diffusion-v1-5>`__
         - Hugginface
         - Latent Diffusion Model
         - 77
@@ -108,13 +114,13 @@ Performance Information F.A.Q.
         - U-Net
         - semantic segmentation
         - 368x480
-      * - `yolo-v3-tiny <https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolo-v3-tiny-tf>`__
-        - YOLO v3 Tiny
+      * - `yolo-v5m <https://github.com/openvinotoolkit/open_model_zoo/tree/master/models/public/yolo-v3-tiny-tf>`__
+        - YOLO V5 Medium
         - object detection
-        - 416x416
+        - 640x640
       * - `yolov8n <https://https://github.com/ultralytics/ultralytics>`__
         - Yolov8nano
-        -  object detection
+        - object detection
         - 608x608
 
 
@@ -122,8 +128,8 @@ Performance Information F.A.Q.
 
    Intel partners with vendors all over the world. For a list of Hardware Manufacturers, see the
    `Intel® AI: In Production Partners & Solutions Catalog <https://www.intel.com/content/www/us/en/internet-of-things/ai-in-production/partners-solutions-catalog.html>`__.
-   For more details, see the :doc:`Supported Devices <../compatibility-and-support/supported-devices>`.
-   documentation.
+   For more details, see the :doc:`Supported Devices <../compatibility-and-support/supported-devices>` article.
+
 
 .. dropdown:: How can I optimize my models for better performance or accuracy?
 
diff --git a/docs/sphinx_setup/_static/benchmarks_files/OV-2024.3-Performance-Data.xlsx b/docs/sphinx_setup/_static/benchmarks_files/OV-2024.3-Performance-Data.xlsx
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diff --git a/docs/sphinx_setup/_static/benchmarks_files/data/graph-data-ov.json b/docs/sphinx_setup/_static/benchmarks_files/data/graph-data-ov.json
index 68f155fdc1dc6c..0bb38199b997ca 100644
--- a/docs/sphinx_setup/_static/benchmarks_files/data/graph-data-ov.json
+++ b/docs/sphinx_setup/_static/benchmarks_files/data/graph-data-ov.json
@@ -1,17 +1,17 @@
 [
     {
-        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
+        "Platform": "Intel® Celeron® 6305E CPU-only",
         "Model": "bert-base-cased",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 314.12,
-                        "fp16": 337.18,
-                        "fp32": "",
+                        "int8": 11.75,
+                        "fp16": "",
+                        "fp32": 4.32,
                         "bf16": ""
                     }
                 ],
@@ -22,7 +22,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 4.77,
+                        "int8": 87.69,
                         "fp16": "",
                         "fp32": "",
                         "bf16": ""
@@ -34,18 +34,18 @@
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     },
     {
-        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Core™ i3-8100 CPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 102.57,
-                        "fp16": 95.51,
-                        "fp32": "",
+                        "int8": 21.17,
+                        "fp16": "",
+                        "fp32": 15.03,
                         "bf16": ""
                     }
                 ],
@@ -56,7 +56,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 12.27,
+                        "int8": 49.24,
                         "fp16": "",
                         "fp32": "",
                         "bf16": ""
@@ -68,18 +68,18 @@
         }
     },
     {
-        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
-        "Model": "chatglm2-6b",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Core™ i5-10500TE CPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 32.5,
                         "fp16": "",
-                        "fp32": "",
+                        "fp32": 21.9,
                         "bf16": ""
                     }
                 ],
@@ -89,9 +89,9 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": 21.73,
-                        "int8": 26.75,
-                        "fp16": 45.14,
+                        "int4": "",
+                        "int8": 35.99,
+                        "fp16": "",
                         "fp32": "",
                         "bf16": ""
                     }
@@ -102,18 +102,18 @@
         }
     },
     {
-        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
-        "Model": "efficientdet-d0",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Core™ i5-1235U Processor CPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 326.6,
-                        "fp16": 313.96,
-                        "fp32": "",
+                        "int8": 33.02,
+                        "fp16": "",
+                        "fp32": 12.59,
                         "bf16": ""
                     }
                 ],
@@ -124,7 +124,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 5.37,
+                        "int8": 45.4,
                         "fp16": "",
                         "fp32": "",
                         "bf16": ""
@@ -136,18 +136,18 @@
         }
     },
     {
-        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
-        "Model": "falcon-7b-instruct",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Core™ i5-1335U Processor CPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 39.93,
                         "fp16": "",
-                        "fp32": "",
+                        "fp32": 15.99,
                         "bf16": ""
                     }
                 ],
@@ -157,9 +157,9 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": 28.74,
-                        "int8": 38.83,
-                        "fp16": 56.81,
+                        "int4": "",
+                        "int8": 40.15,
+                        "fp16": "",
                         "fp32": "",
                         "bf16": ""
                     }
@@ -170,18 +170,18 @@
         }
     },
     {
-        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
-        "Model": "llama-2-7b-chat-hf",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Core™ i5-13600K CPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 119.95,
                         "fp16": "",
-                        "fp32": "",
+                        "fp32": 47.19,
                         "bf16": ""
                     }
                 ],
@@ -191,9 +191,9 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": 25.97,
-                        "int8": 35.32,
-                        "fp16": 52.34,
+                        "int4": "",
+                        "int8": 13.31,
+                        "fp16": "",
                         "fp32": "",
                         "bf16": ""
                     }
@@ -204,18 +204,18 @@
         }
     },
     {
-        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
-        "Model": "llama-3-8b",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Core™ i5-8500 CPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 33.13,
                         "fp16": "",
-                        "fp32": "",
+                        "fp32": 22.73,
                         "bf16": ""
                     }
                 ],
@@ -225,8 +225,8 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": 28.16,
-                        "int8": 38.34,
+                        "int4": "",
+                        "int8": 31.1,
                         "fp16": "",
                         "fp32": "",
                         "bf16": ""
@@ -238,18 +238,18 @@
         }
     },
     {
-        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Core™ i7-1185G7 CPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 35.25,
-                        "fp16": 19.43,
-                        "fp32": "",
+                        "int8": 50.91,
+                        "fp16": "",
+                        "fp32": 18.37,
                         "bf16": ""
                     }
                 ],
@@ -260,7 +260,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 47.87,
+                        "int8": 22.52,
                         "fp16": "",
                         "fp32": "",
                         "bf16": ""
@@ -272,18 +272,18 @@
         }
     },
     {
-        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
-        "Model": "mistral-7b-v0.1",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Core™ i7-1185GRE CPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 38.31,
                         "fp16": "",
-                        "fp32": "",
+                        "fp32": 13.71,
                         "bf16": ""
                     }
                 ],
@@ -293,9 +293,9 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": 26.02,
-                        "int8": 36.92,
-                        "fp16": 55.23,
+                        "int4": "",
+                        "int8": 28.51,
+                        "fp16": "",
                         "fp32": "",
                         "bf16": ""
                     }
@@ -306,18 +306,18 @@
         }
     },
     {
-        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
-        "Model": "mobilenet-v2",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Core™ i7-12700H CPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 2312.44,
-                        "fp16": 2058.66,
-                        "fp32": "",
+                        "int8": 86.13,
+                        "fp16": "",
+                        "fp32": 33.75,
                         "bf16": ""
                     }
                 ],
@@ -328,7 +328,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1.12,
+                        "int8": 16.59,
                         "fp16": "",
                         "fp32": "",
                         "bf16": ""
@@ -340,18 +340,18 @@
         }
     },
     {
-        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
-        "Model": "phi-3-mini-4k-instruct",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Core™ i7-1355U Processor CPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 44.68,
                         "fp16": "",
-                        "fp32": "",
+                        "fp32": 17.96,
                         "bf16": ""
                     }
                 ],
@@ -361,9 +361,9 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": 23.67,
-                        "int8": 27.58,
-                        "fp16": 38.55,
+                        "int4": "",
+                        "int8": 37.63,
+                        "fp16": "",
                         "fp32": "",
                         "bf16": ""
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@@ -374,18 +374,18 @@
         }
     },
     {
-        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
-        "Model": "resnet-50",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Core™ i7-1360P CPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1398.81,
-                        "fp16": 1041.08,
-                        "fp32": "",
+                        "int8": 60.67,
+                        "fp16": "",
+                        "fp32": 23.99,
                         "bf16": ""
                     }
                 ],
@@ -396,7 +396,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1.42,
+                        "int8": 24.02,
                         "fp16": "",
                         "fp32": "",
                         "bf16": ""
@@ -408,18 +408,18 @@
         }
     },
     {
-        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Core™ i7-8700T CPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 112.15,
-                        "fp16": 72.82,
-                        "fp32": "",
+                        "int8": 27.34,
+                        "fp16": "",
+                        "fp32": 18.04,
                         "bf16": ""
                     }
                 ],
@@ -430,7 +430,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 14.59,
+                        "int8": 30.86,
                         "fp16": "",
                         "fp32": "",
                         "bf16": ""
@@ -442,18 +442,18 @@
         }
     },
     {
-        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Core™ i9-10900TE CPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1315.9,
-                        "fp16": 1197.39,
-                        "fp32": "",
+                        "int8": 32.78,
+                        "fp16": "",
+                        "fp32": 21.32,
                         "bf16": ""
                     }
                 ],
@@ -464,7 +464,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1.45,
+                        "int8": 38.38,
                         "fp16": "",
                         "fp32": "",
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@@ -476,18 +476,18 @@
         }
     },
     {
-        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
-        "Model": "stable-diffusion-v2-1",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Core™ i9-13900K CPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 168.92,
                         "fp16": "",
-                        "fp32": "",
+                        "fp32": 67.23,
                         "bf16": ""
                     }
                 ],
@@ -498,8 +498,8 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 2.29,
-                        "fp16": 2.23,
+                        "int8": 10.73,
+                        "fp16": "",
                         "fp32": "",
                         "bf16": ""
                     }
@@ -510,18 +510,18 @@
         }
     },
     {
-        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
-        "Model": "stable-diffusion-v2-1",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Xeon® W1290P CPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 52.48,
                         "fp16": "",
-                        "fp32": "",
+                        "fp32": 35.73,
                         "bf16": ""
                     }
                 ],
@@ -532,8 +532,8 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 2.31,
-                        "fp16": 2.24,
+                        "int8": 26.63,
+                        "fp16": "",
                         "fp32": "",
                         "bf16": ""
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@@ -544,18 +544,18 @@
         }
     },
     {
-        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Xeon® E-2124G CPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 233.96,
-                        "fp16": 178.57,
-                        "fp32": "",
+                        "int8": 20.68,
+                        "fp16": "",
+                        "fp32": 14.76,
                         "bf16": ""
                     }
                 ],
@@ -566,7 +566,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 6.57,
+                        "int8": 49.95,
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@@ -578,18 +578,18 @@
         }
     },
     {
-        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Xeon® Gold 5218T CPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1401.38,
-                        "fp16": 1368.88,
-                        "fp32": "",
+                        "int8": 218.51,
+                        "fp16": "",
+                        "fp32": 80.07,
                         "bf16": ""
                     }
                 ],
@@ -600,7 +600,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1.26,
+                        "int8": 14.63,
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@@ -612,18 +612,18 @@
         }
     },
     {
-        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
-        "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Xeon® Platinum 8280 CPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 519.61,
-                        "fp16": 560.01,
-                        "fp32": "",
+                        "int8": 590.15,
+                        "fp16": "",
+                        "fp32": 224.94,
                         "bf16": ""
                     }
                 ],
@@ -634,7 +634,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 3.28,
+                        "int8": 9.22,
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                         "fp32": "",
                         "bf16": ""
@@ -646,18 +646,18 @@
         }
     },
     {
-        "Platform": "Intel® Atom® X6425E CPU+iGPU",
-        "Model": "efficientdet-d0",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Xeon® Platinum 8380 CPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 23.13,
+                        "int8": 881.6,
                         "fp16": "",
-                        "fp32": 24.02,
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                         "bf16": ""
                     }
                 ],
@@ -668,7 +668,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
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@@ -680,19 +680,19 @@
         }
     },
     {
-        "Platform": "Intel® Atom® X6425E CPU+iGPU",
-        "Model": "mobilenet-v2",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Xeon® Platinum 8480+ CPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 229.14,
+                        "int8": 3032.01,
                         "fp16": "",
-                        "fp32": 219.47,
-                        "bf16": ""
+                        "fp32": 488.41,
+                        "bf16": 1975.07
                     }
                 ],
                 "Unit": "FPS",
@@ -702,10 +702,10 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 3.74,
                         "fp16": "",
                         "fp32": "",
-                        "bf16": ""
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                     }
                 ],
                 "Unit": "ms",
@@ -714,19 +714,19 @@
         }
     },
     {
-        "Platform": "Intel® Atom® X6425E CPU+iGPU",
-        "Model": "resnet-50",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Xeon® Platinum 8580 CPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 59.42,
+                        "int8": 4693.95,
                         "fp16": "",
-                        "fp32": 54.49,
-                        "bf16": ""
+                        "fp32": 562.05,
+                        "bf16": 3202.49
                     }
                 ],
                 "Unit": "FPS",
@@ -736,10 +736,10 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 3.77,
                         "fp16": "",
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                 ],
                 "Unit": "ms",
@@ -748,18 +748,18 @@
         }
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     {
-        "Platform": "Intel® Atom® X6425E CPU+iGPU",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Xeon® Gold 6238L CPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1.16,
+                        "int8": 427.23,
                         "fp16": "",
-                        "fp32": 1.15,
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                         "bf16": ""
                     }
                 ],
@@ -770,7 +770,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
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@@ -782,18 +782,18 @@
         }
     },
     {
-        "Platform": "Intel® Atom® X6425E CPU+iGPU",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Xeon® Silver 4316 CPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 112.0,
+                        "int8": 429.84,
                         "fp16": "",
-                        "fp32": 98.56,
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                         "bf16": ""
                     }
                 ],
@@ -804,7 +804,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
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@@ -816,18 +816,18 @@
         }
     },
     {
-        "Platform": "Intel® Atom® X6425E CPU+iGPU",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ Ultra 7 processor 155H CPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 77.37,
+                        "int8": 76.06,
                         "fp16": "",
-                        "fp32": 68.49,
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                         "bf16": ""
                     }
                 ],
@@ -838,7 +838,7 @@
                 "Precisions": [
                     {
                         "int4": "",
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@@ -850,18 +850,18 @@
         }
     },
     {
-        "Platform": "Intel® Atom® X6425E CPU+iGPU",
-        "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 36.35,
+                        "int8": 52.79,
                         "fp16": "",
-                        "fp32": 34.1,
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                         "bf16": ""
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                 ],
@@ -872,7 +872,7 @@
                 "Precisions": [
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                         "int4": "",
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@@ -884,18 +884,18 @@
         }
     },
     {
-        "Platform": "Intel® Atom® X6425E CPU-only",
-        "Model": "efficientdet-d0",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ Ultra 9 processor 288V CPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 7.26,
+                        "int8": 73.09,
                         "fp16": "",
-                        "fp32": 4.88,
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                         "bf16": ""
                     }
                 ],
@@ -906,7 +906,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 140.26,
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@@ -918,18 +918,18 @@
         }
     },
     {
-        "Platform": "Intel® Atom® X6425E CPU-only",
-        "Model": "mobilenet-v2",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
-        "Parameters": {
-            "throughput": {
+        "Platform": "Intel® Data Center GPU Flex 170 dGPU",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
+        "Parameters": {
+            "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 133.5,
-                        "fp16": "",
-                        "fp32": 80.49,
+                        "int8": 404.72,
+                        "fp16": 444.29,
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                         "bf16": ""
                     }
                 ],
@@ -940,7 +940,7 @@
                 "Precisions": [
                     {
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-                        "int8": 7.82,
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@@ -952,18 +952,18 @@
         }
     },
     {
-        "Platform": "Intel® Atom® X6425E CPU-only",
-        "Model": "resnet-50",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 19.86,
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-                        "fp32": 8.14,
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                     }
                 ],
@@ -974,7 +974,7 @@
                 "Precisions": [
                     {
                         "int4": "",
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@@ -986,18 +986,18 @@
         }
     },
     {
-        "Platform": "Intel® Atom® X6425E CPU-only",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 45.7,
-                        "fp16": "",
-                        "fp32": 21.58,
+                        "int8": 187.77,
+                        "fp16": 149.65,
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                         "bf16": ""
                     }
                 ],
@@ -1008,7 +1008,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 22.77,
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@@ -1020,18 +1020,18 @@
         }
     },
     {
-        "Platform": "Intel® Atom® X6425E CPU-only",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ Ultra 7 processor 155H NPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 22.81,
-                        "fp16": "",
-                        "fp32": 10.3,
+                        "int8": 89.12,
+                        "fp16": 74.2,
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                         "bf16": ""
                     }
                 ],
@@ -1042,7 +1042,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 45.22,
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@@ -1054,18 +1054,18 @@
         }
     },
     {
-        "Platform": "Intel® Atom® X6425E CPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ Ultra 7 processor 165H NPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 10.28,
-                        "fp16": "",
-                        "fp32": 5.11,
+                        "int8": 89.38,
+                        "fp16": 74.26,
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                         "bf16": ""
                     }
                 ],
@@ -1076,7 +1076,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 100.18,
+                        "int8": 12.3,
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@@ -1088,17 +1088,17 @@
         }
     },
     {
-        "Platform": "Intel® Atom® X6425E iGPU-only",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ Ultra 9 processor 288V NPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
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-                        "fp16": "",
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@@ -1110,7 +1110,7 @@
                 "Precisions": [
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-                        "int8": 720.0,
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@@ -1122,17 +1122,17 @@
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     },
     {
-        "Platform": "Intel® Atom® X6425E iGPU-only",
-        "Model": "efficientdet-d0",
-        "Checked": true,
+        "Platform": "Intel® Celeron® 6305E iGPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
         "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 21.7,
-                        "fp16": 25.08,
+                        "int8": 42.88,
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@@ -1144,7 +1144,7 @@
                 "Precisions": [
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-                        "int8": 60.84,
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@@ -1156,17 +1156,17 @@
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     {
-        "Platform": "Intel® Atom® X6425E iGPU-only",
-        "Model": "mobilenet-v2",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i5-1235U Processor iGPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 188.13,
-                        "fp16": 222.45,
+                        "int8": 47.07,
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@@ -1178,7 +1178,7 @@
                 "Precisions": [
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-                        "int8": 7.58,
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@@ -1190,17 +1190,17 @@
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-        "Platform": "Intel® Atom® X6425E iGPU-only",
-        "Model": "resnet-50",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i5-1335U Processor iGPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 48.1,
-                        "fp16": 51.66,
+                        "int8": 47.0,
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@@ -1212,7 +1212,7 @@
                 "Precisions": [
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-                        "int8": 22.9,
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@@ -1224,17 +1224,17 @@
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-        "Platform": "Intel® Atom® X6425E iGPU-only",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i7-1185G7 iGPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1.16,
-                        "fp16": 1.16,
+                        "int8": 67.79,
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@@ -1246,7 +1246,7 @@
                 "Precisions": [
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-                        "int8": 871.11,
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@@ -1258,17 +1258,17 @@
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-        "Platform": "Intel® Atom® X6425E iGPU-only",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i7-1185GRE iGPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 92.86,
-                        "fp16": 95.65,
+                        "int8": 47.29,
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@@ -1280,7 +1280,7 @@
                 "Precisions": [
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-                        "int8": 13.47,
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@@ -1292,17 +1292,17 @@
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     {
-        "Platform": "Intel® Atom® X6425E iGPU-only",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i7-12700H iGPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 1.98,
-                        "fp16": "",
+                        "int8": 88.92,
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@@ -1314,7 +1314,7 @@
                 "Precisions": [
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-                        "int8": 505.58,
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@@ -1326,17 +1326,17 @@
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-        "Platform": "Intel® Atom® X6425E iGPU-only",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i7-1355U Processor iGPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 66.16,
-                        "fp16": 66.59,
+                        "int8": 66.18,
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@@ -1348,7 +1348,7 @@
                 "Precisions": [
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-                        "int8": 16.65,
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@@ -1360,17 +1360,17 @@
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-        "Platform": "Intel® Atom® X6425E iGPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i7-1360P iGPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 31.72,
-                        "fp16": 33.08,
+                        "int8": "",
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@@ -1382,7 +1382,7 @@
                 "Precisions": [
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-                        "int8": 35.73,
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@@ -1394,18 +1394,18 @@
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     {
-        "Platform": "Intel® Atom® x7425E CPU+iGPU",
-        "Model": "efficientdet-d0",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ Ultra 7 processor 155H iGPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 39.13,
-                        "fp16": "",
-                        "fp32": 30.4,
+                        "int8": 159.37,
+                        "fp16": 100.87,
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@@ -1416,7 +1416,7 @@
                 "Precisions": [
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@@ -1428,18 +1428,18 @@
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     {
-        "Platform": "Intel® Atom® x7425E CPU+iGPU",
-        "Model": "mobilenet-v2",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ Ultra 7 processor 165H iGPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 476.24,
-                        "fp16": "",
-                        "fp32": 304.46,
+                        "int8": 152.5,
+                        "fp16": 108.49,
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@@ -1450,7 +1450,7 @@
                 "Precisions": [
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@@ -1462,18 +1462,18 @@
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-        "Platform": "Intel® Atom® x7425E CPU+iGPU",
-        "Model": "resnet-50",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ Ultra 9 processor 288V iGPU-only",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 131.17,
-                        "fp16": "",
-                        "fp32": 55.39,
+                        "int8": 316.53,
+                        "fp16": 268.1,
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@@ -1484,7 +1484,7 @@
                 "Precisions": [
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@@ -1496,18 +1496,18 @@
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     },
     {
-        "Platform": "Intel® Atom® x7425E CPU+iGPU",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
+        "Platform": "Intel® Celeron® 6305E CPU+iGPU",
+        "Model": "bert-base-cased",
+        "Checked": "true",
         "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 2.18,
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-                        "fp32": 0.87,
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@@ -1530,18 +1530,18 @@
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-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i5-1235U Processor CPU+iGPU",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 235.68,
+                        "int8": "",
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-                        "fp32": 115.73,
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@@ -1564,18 +1564,18 @@
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     {
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-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i5-1335U Processor CPU+iGPU",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 148.86,
+                        "int8": 49.58,
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@@ -1598,18 +1598,18 @@
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     {
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-        "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i7-1185G7 CPU+iGPU",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 68.86,
+                        "int8": 85.59,
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-                        "fp32": 37.06,
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@@ -1632,18 +1632,18 @@
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-        "Model": "efficientdet-d0",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i7-1185GRE CPU+iGPU",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 14.29,
+                        "int8": 48.88,
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@@ -1654,7 +1654,7 @@
                 "Precisions": [
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@@ -1666,18 +1666,18 @@
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-        "Model": "mobilenet-v2",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i7-12700H CPU+iGPU",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 273.72,
+                        "int8": "",
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@@ -1688,7 +1688,7 @@
                 "Precisions": [
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@@ -1700,18 +1700,18 @@
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-        "Model": "resnet-50",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i7-1355U Processor CPU+iGPU",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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                         "int4": "",
-                        "int8": 45.28,
+                        "int8": 61.03,
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                 ],
@@ -1722,7 +1722,7 @@
                 "Precisions": [
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@@ -1734,10 +1734,10 @@
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-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i7-1360P CPU+iGPU",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -1745,7 +1745,7 @@
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-                        "fp32": 0.31,
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@@ -1768,18 +1768,18 @@
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-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ Ultra 7 processor 155H CPU+iGPU",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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                         "int4": "",
-                        "int8": 98.26,
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                 ],
@@ -1790,7 +1790,7 @@
                 "Precisions": [
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@@ -1802,18 +1802,18 @@
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-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU+iGPU",
+        "Model": "bert-base-cased",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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-                        "fp32": 23.25,
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@@ -1824,7 +1824,7 @@
                 "Precisions": [
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@@ -1836,18 +1836,18 @@
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     },
     {
-        "Platform": "Intel® Atom® x7425E CPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
+        "Platform": "Intel® Celeron® 6305E CPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
         "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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@@ -1858,7 +1858,7 @@
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@@ -1870,18 +1870,18 @@
         }
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     {
-        "Platform": "Intel® Atom® x7425E iGPU-only",
+        "Platform": "Intel® Core™ i3-8100 CPU-only",
         "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -1892,7 +1892,7 @@
                 "Precisions": [
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@@ -1904,18 +1904,18 @@
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-        "Model": "efficientdet-d0",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i5-10500TE CPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 40.31,
-                        "fp16": 36.41,
-                        "fp32": "",
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@@ -1926,7 +1926,7 @@
                 "Precisions": [
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@@ -1938,17 +1938,17 @@
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-        "Model": "mobilenet-v2",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i5-1235U Processor CPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 424.02,
-                        "fp16": 324.62,
+                        "int8": "",
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@@ -1960,7 +1960,7 @@
                 "Precisions": [
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@@ -1972,17 +1972,17 @@
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     {
-        "Platform": "Intel® Atom® x7425E iGPU-only",
-        "Model": "resnet-50",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i5-1335U Processor CPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 109.29,
-                        "fp16": 64.53,
+                        "int8": "",
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@@ -1994,7 +1994,7 @@
                 "Precisions": [
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@@ -2006,18 +2006,18 @@
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-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i5-13600K CPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 2.19,
-                        "fp16": 1.34,
-                        "fp32": "",
+                        "int8": "",
+                        "fp16": "",
+                        "fp32": 3.91,
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@@ -2028,7 +2028,7 @@
                 "Precisions": [
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-                        "int8": 464.61,
+                        "int8": 128.89,
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@@ -2040,18 +2040,18 @@
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-        "Platform": "Intel® Atom® x7425E iGPU-only",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i5-8500 CPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 215.69,
-                        "fp16": 135.07,
-                        "fp32": "",
+                        "int8": 3.01,
+                        "fp16": "",
+                        "fp32": 1.89,
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@@ -2062,7 +2062,7 @@
                 "Precisions": [
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-                        "int8": 6.29,
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@@ -2074,18 +2074,18 @@
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-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i7-1185G7 CPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 3.43,
+                        "int8": 5.03,
                         "fp16": "",
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@@ -2096,7 +2096,7 @@
                 "Precisions": [
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-                        "int8": 291.91,
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@@ -2108,18 +2108,18 @@
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-        "Platform": "Intel® Atom® x7425E iGPU-only",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i7-1185GRE CPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 126.79,
-                        "fp16": 81.83,
-                        "fp32": "",
+                        "int8": 3.69,
+                        "fp16": "",
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@@ -2130,7 +2130,7 @@
                 "Precisions": [
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-                        "int8": 8.64,
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@@ -2142,18 +2142,18 @@
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-        "Platform": "Intel® Atom® x7425E iGPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i7-12700H CPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 61.98,
-                        "fp16": 44.91,
-                        "fp32": "",
+                        "int8": 7.52,
+                        "fp16": "",
+                        "fp32": 2.85,
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@@ -2164,7 +2164,7 @@
                 "Precisions": [
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@@ -2176,18 +2176,18 @@
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-        "Model": "bert-base-cased",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i7-1355U Processor CPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 44.92,
+                        "int8": "",
                         "fp16": "",
-                        "fp32": 33.4,
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@@ -2198,7 +2198,7 @@
                 "Precisions": [
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@@ -2210,18 +2210,18 @@
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     },
     {
-        "Platform": "Intel® Celeron® 6305E CPU+iGPU",
+        "Platform": "Intel® Core™ i7-1360P CPU-only",
         "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 5.86,
+                        "int8": 5.26,
                         "fp16": "",
-                        "fp32": 3.72,
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@@ -2244,18 +2244,18 @@
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     {
-        "Platform": "Intel® Celeron® 6305E CPU+iGPU",
-        "Model": "efficientdet-d0",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i7-8700T CPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 57.82,
+                        "int8": "",
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@@ -2266,7 +2266,7 @@
                 "Precisions": [
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@@ -2278,18 +2278,18 @@
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     {
-        "Platform": "Intel® Celeron® 6305E CPU+iGPU",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i9-10900TE CPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 0.55,
+                        "int8": 3.14,
                         "fp16": "",
-                        "fp32": 0.51,
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@@ -2312,18 +2312,18 @@
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     {
-        "Platform": "Intel® Celeron® 6305E CPU+iGPU",
-        "Model": "mobilenet-v2",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i9-13900K CPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 533.91,
+                        "int8": 16.05,
                         "fp16": "",
-                        "fp32": 397.76,
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@@ -2334,7 +2334,7 @@
                 "Precisions": [
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@@ -2346,18 +2346,18 @@
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-        "Platform": "Intel® Celeron® 6305E CPU+iGPU",
-        "Model": "resnet-50",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Xeon® W1290P CPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 198.76,
+                        "int8": 5.17,
                         "fp16": "",
-                        "fp32": 116.53,
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@@ -2380,18 +2380,18 @@
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-        "Platform": "Intel® Celeron® 6305E CPU+iGPU",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Xeon® E-2124G CPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 5.06,
+                        "int8": "",
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-                        "fp32": 2.71,
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@@ -2402,7 +2402,7 @@
                 "Precisions": [
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@@ -2414,18 +2414,18 @@
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-        "Platform": "Intel® Celeron® 6305E CPU+iGPU",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Xeon® Gold 5218T CPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 317.11,
+                        "int8": 21.82,
                         "fp16": "",
-                        "fp32": 194.62,
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@@ -2436,7 +2436,7 @@
                 "Precisions": [
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@@ -2448,18 +2448,18 @@
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-        "Platform": "Intel® Celeron® 6305E CPU+iGPU",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Xeon® Platinum 8280 CPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
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                         "int4": "",
-                        "int8": 8.86,
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-                        "fp32": 4.55,
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@@ -2470,7 +2470,7 @@
                 "Precisions": [
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@@ -2482,18 +2482,18 @@
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-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Xeon® Platinum 8380 CPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 261.36,
+                        "int8": 78.31,
                         "fp16": "",
-                        "fp32": 151.54,
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@@ -2504,7 +2504,7 @@
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@@ -2516,19 +2516,19 @@
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-        "Platform": "Intel® Celeron® 6305E CPU+iGPU",
-        "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Xeon® Platinum 8480+ CPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 115.82,
+                        "int8": 244.06,
                         "fp16": "",
-                        "fp32": 78.39,
-                        "bf16": ""
+                        "fp32": 41.97,
+                        "bf16": 211.62
                     }
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                 "Unit": "FPS",
@@ -2538,10 +2538,10 @@
                 "Precisions": [
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                 "Unit": "ms",
@@ -2550,19 +2550,19 @@
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-        "Platform": "Intel® Celeron® 6305E CPU-only",
-        "Model": "bert-base-cased",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Xeon® Platinum 8580 CPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 11.77,
+                        "int8": 305.38,
                         "fp16": "",
-                        "fp32": 4.32,
-                        "bf16": ""
+                        "fp32": 55.37,
+                        "bf16": 289.16
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                 "Unit": "FPS",
@@ -2572,10 +2572,10 @@
                 "Precisions": [
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-                        "int8": 87.85,
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                 "Unit": "ms",
@@ -2584,18 +2584,18 @@
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     {
-        "Platform": "Intel® Celeron® 6305E CPU-only",
+        "Platform": "Intel® Xeon® Gold 6238L CPU-only",
         "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
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                         "int4": "",
-                        "int8": 1.18,
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-                        "fp32": 0.38,
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@@ -2606,7 +2606,7 @@
                 "Precisions": [
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@@ -2618,18 +2618,18 @@
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-        "Model": "efficientdet-d0",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Xeon® Silver 4316 CPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
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                         "int4": "",
-                        "int8": 19.01,
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                         "fp16": "",
-                        "fp32": 11.49,
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@@ -2640,7 +2640,7 @@
                 "Precisions": [
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@@ -2652,18 +2652,18 @@
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-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ Ultra 7 processor 155H CPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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                         "int4": "",
-                        "int8": 0.17,
+                        "int8": 7.34,
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-                        "fp32": 0.04,
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@@ -2674,7 +2674,7 @@
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-                        "int8": 5757.41,
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@@ -2686,18 +2686,18 @@
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-        "Platform": "Intel® Celeron® 6305E CPU-only",
-        "Model": "mobilenet-v2",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 301.12,
+                        "int8": 5.5,
                         "fp16": "",
-                        "fp32": 134.01,
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@@ -2708,7 +2708,7 @@
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@@ -2720,18 +2720,18 @@
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-        "Model": "resnet-50",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ Ultra 9 processor 288V CPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
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         "Parameters": {
             "throughput": {
                 "Precisions": [
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-                        "int8": 51.67,
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@@ -2742,7 +2742,7 @@
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@@ -2754,18 +2754,18 @@
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-        "Platform": "Intel® Celeron® 6305E CPU-only",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Data Center GPU Flex 170 dGPU",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": "",
-                        "fp16": "",
-                        "fp32": 0.23,
+                        "int8": 121.51,
+                        "fp16": 110.4,
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@@ -2776,7 +2776,7 @@
                 "Precisions": [
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@@ -2788,18 +2788,18 @@
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-        "Platform": "Intel® Celeron® 6305E CPU-only",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 115.07,
+                        "int8": "",
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@@ -2810,7 +2810,7 @@
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@@ -2822,18 +2822,18 @@
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-        "Platform": "Intel® Celeron® 6305E CPU-only",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 1.49,
-                        "fp16": "",
-                        "fp32": 0.37,
+                        "int8": 30.58,
+                        "fp16": 21.73,
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@@ -2844,7 +2844,7 @@
                 "Precisions": [
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@@ -2856,18 +2856,18 @@
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-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ Ultra 7 processor 155H NPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 55.57,
-                        "fp16": "",
-                        "fp32": 18.29,
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@@ -2878,7 +2878,7 @@
                 "Precisions": [
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@@ -2890,18 +2890,18 @@
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-        "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ Ultra 7 processor 165H NPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 26.0,
-                        "fp16": "",
-                        "fp32": 9.66,
+                        "int8": 10.38,
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@@ -2912,7 +2912,7 @@
                 "Precisions": [
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@@ -2924,17 +2924,17 @@
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-        "Platform": "Intel® Celeron® 6305E iGPU-only",
-        "Model": "bert-base-cased",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ Ultra 9 processor 288V NPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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                         "int4": "",
-                        "int8": 42.84,
-                        "fp16": 33.56,
+                        "int8": 32.32,
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@@ -2946,7 +2946,7 @@
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@@ -2958,17 +2958,17 @@
         }
     },
     {
-        "Platform": "Intel® Celeron® 6305E iGPU-only",
+        "Platform": "Intel® Atom® x7425E iGPU-only",
         "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
+        "Checked": "true",
         "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 5.49,
-                        "fp16": 3.64,
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@@ -2980,7 +2980,7 @@
                 "Precisions": [
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-                        "int8": 192.72,
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@@ -2992,17 +2992,17 @@
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     },
     {
-        "Platform": "Intel® Celeron® 6305E iGPU-only",
-        "Model": "efficientdet-d0",
-        "Checked": true,
+        "Platform": "Intel® Atom® X6425E iGPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
         "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 73.41,
-                        "fp16": 58.57,
+                        "int8": 1.4,
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@@ -3014,7 +3014,7 @@
                 "Precisions": [
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@@ -3027,16 +3027,16 @@
     },
     {
         "Platform": "Intel® Celeron® 6305E iGPU-only",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
         "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 0.48,
-                        "fp16": 0.53,
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@@ -3048,7 +3048,7 @@
                 "Precisions": [
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@@ -3060,17 +3060,17 @@
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-        "Model": "mobilenet-v2",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i5-1235U Processor iGPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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                         "int4": "",
-                        "int8": 673.95,
-                        "fp16": 504.33,
+                        "int8": 5.41,
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@@ -3082,7 +3082,7 @@
                 "Precisions": [
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@@ -3094,17 +3094,17 @@
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-        "Model": "resnet-50",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i5-1335U Processor iGPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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                         "int4": "",
-                        "int8": 203.39,
-                        "fp16": 118.57,
+                        "int8": 6.17,
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@@ -3116,7 +3116,7 @@
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@@ -3128,17 +3128,17 @@
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-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i7-1185G7 iGPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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                         "int4": "",
-                        "int8": 5.09,
-                        "fp16": 2.78,
+                        "int8": 8.85,
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@@ -3150,7 +3150,7 @@
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@@ -3162,17 +3162,17 @@
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-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i7-1185GRE iGPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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                     {
                         "int4": "",
-                        "int8": 396.51,
-                        "fp16": 222.39,
+                        "int8": 5.41,
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@@ -3184,7 +3184,7 @@
                 "Precisions": [
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@@ -3196,17 +3196,17 @@
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-        "Platform": "Intel® Celeron® 6305E iGPU-only",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i7-12700H iGPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 8.29,
-                        "fp16": 4.48,
+                        "int8": 11.76,
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@@ -3218,7 +3218,7 @@
                 "Precisions": [
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-                        "int8": 121.5,
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@@ -3230,17 +3230,17 @@
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-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i7-1355U Processor iGPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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                         "int4": "",
-                        "int8": 284.94,
-                        "fp16": 157.32,
+                        "int8": 8.08,
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@@ -3252,7 +3252,7 @@
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@@ -3264,17 +3264,17 @@
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-        "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i7-1360P iGPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 121.72,
-                        "fp16": 81.7,
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@@ -3286,7 +3286,7 @@
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@@ -3298,18 +3298,18 @@
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-        "Model": "bert-base-cased",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Processor N100 iGPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 123.16,
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@@ -3320,7 +3320,7 @@
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@@ -3332,18 +3332,18 @@
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+        "Platform": "Intel® Core™ Ultra 7 processor 155H iGPU-only",
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-        "Checked": true,
+        "Checked": "true",
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         "Parameters": {
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-                        "int8": 12.24,
-                        "fp16": "",
-                        "fp32": 6.12,
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@@ -3354,7 +3354,7 @@
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@@ -3366,18 +3366,18 @@
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-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 165H iGPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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@@ -3400,18 +3400,18 @@
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-        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU+iGPU",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 9 processor 288V iGPU-only",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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-                        "int8": 1.46,
-                        "fp16": "",
-                        "fp32": 0.58,
+                        "int8": 54.9,
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@@ -3422,7 +3422,7 @@
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@@ -3434,18 +3434,18 @@
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-        "Model": "mobilenet-v2",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Celeron® 6305E CPU+iGPU",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 3065.32,
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-                        "fp32": 1695.14,
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@@ -3468,18 +3468,18 @@
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     {
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-        "Model": "resnet-50",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1235U Processor CPU+iGPU",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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-                        "int8": 607.16,
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-                        "fp32": 256.25,
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@@ -3502,18 +3502,18 @@
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-        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU+iGPU",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1335U Processor CPU+iGPU",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
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         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 17.78,
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@@ -3536,18 +3536,18 @@
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     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU+iGPU",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185G7 CPU+iGPU",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 704.49,
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@@ -3570,18 +3570,18 @@
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     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU+iGPU",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185GRE CPU+iGPU",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 17.53,
+                        "int8": 5.05,
                         "fp16": "",
-                        "fp32": 8.01,
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@@ -3604,18 +3604,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU+iGPU",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-12700H CPU+iGPU",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 667.92,
+                        "int8": "",
                         "fp16": "",
-                        "fp32": 247.4,
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@@ -3638,18 +3638,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU+iGPU",
-        "Model": "yolo_v8n",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1355U Processor CPU+iGPU",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 236.25,
+                        "int8": 6.41,
                         "fp16": "",
-                        "fp32": 151.99,
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@@ -3672,18 +3672,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU-only",
-        "Model": "bert-base-cased",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1360P CPU+iGPU",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 52.93,
+                        "int8": 8.01,
                         "fp16": "",
-                        "fp32": 20.85,
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@@ -3694,7 +3694,7 @@
                 "Precisions": [
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-                        "int8": 32.01,
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                         "bf16": ""
@@ -3706,18 +3706,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU-only",
+        "Platform": "Intel® Core™ Ultra 7 processor 155H CPU+iGPU",
         "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 5.5,
+                        "int8": 22.64,
                         "fp16": "",
-                        "fp32": 1.7,
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@@ -3728,7 +3728,7 @@
                 "Precisions": [
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-                        "int8": 245.77,
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@@ -3740,18 +3740,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU-only",
-        "Model": "chatglm2-6b",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU+iGPU",
+        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 12.39,
                         "fp16": "",
-                        "fp32": "",
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@@ -3761,9 +3761,9 @@
             "latency": {
                 "Precisions": [
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-                        "int4": 105.48,
-                        "int8": 157.96,
-                        "fp16": 208.79,
+                        "int4": "",
+                        "int8": "",
+                        "fp16": "",
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@@ -3774,18 +3774,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU-only",
+        "Platform": "Intel® Atom® x7425E CPU-only",
         "Model": "efficientdet-d0",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 72.54,
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                         "fp16": "",
-                        "fp32": 46.64,
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@@ -3796,7 +3796,7 @@
                 "Precisions": [
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-                        "int8": 23.79,
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@@ -3808,18 +3808,18 @@
         }
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     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU-only",
-        "Model": "falcon-7b-instruct",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Atom® X6425E CPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 7.25,
                         "fp16": "",
-                        "fp32": "",
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@@ -3829,9 +3829,9 @@
             "latency": {
                 "Precisions": [
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-                        "int4": 96.87,
-                        "int8": 188.32,
-                        "fp16": 244.7,
+                        "int4": "",
+                        "int8": 139.91,
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@@ -3842,18 +3842,18 @@
         }
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     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU-only",
-        "Model": "llama-2-7b-chat-hf",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Celeron® 6305E CPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 18.99,
                         "fp16": "",
-                        "fp32": "",
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@@ -3863,9 +3863,9 @@
             "latency": {
                 "Precisions": [
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-                        "int4": 115.05,
-                        "int8": 190.79,
-                        "fp16": 246.24,
+                        "int4": "",
+                        "int8": 55.68,
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@@ -3876,18 +3876,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU-only",
-        "Model": "llama-3-8b",
-        "Checked": true,
+        "Platform": "Intel® Core™ i3-8100 CPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 37.48,
                         "fp16": "",
-                        "fp32": "",
+                        "fp32": 27.9,
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@@ -3897,9 +3897,9 @@
             "latency": {
                 "Precisions": [
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-                        "int4": 207.44,
-                        "int8": 143.95,
-                        "fp16": 271.61,
+                        "int4": "",
+                        "int8": 27.49,
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@@ -3910,18 +3910,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU-only",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-10500TE CPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 0.9,
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                         "fp16": "",
-                        "fp32": 0.2,
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@@ -3932,7 +3932,7 @@
                 "Precisions": [
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-                        "int8": 1323.81,
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@@ -3944,18 +3944,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU-only",
-        "Model": "mistral-7b-v0.1",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1235U Processor CPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
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                         "fp16": "",
-                        "fp32": "",
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                 ],
@@ -3965,9 +3965,9 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": 109.4,
-                        "int8": 199.33,
-                        "fp16": 262.37,
+                        "int4": "",
+                        "int8": 32.17,
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@@ -3978,18 +3978,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU-only",
-        "Model": "mobilenet-v2",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1335U Processor CPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1501.29,
+                        "int8": 56.87,
                         "fp16": "",
-                        "fp32": 586.49,
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                         "bf16": ""
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                 ],
@@ -4000,7 +4000,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1.57,
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@@ -4012,18 +4012,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU-only",
-        "Model": "phi-3-mini-4k-instruct",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-13600K CPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 152.17,
                         "fp16": "",
-                        "fp32": "",
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@@ -4033,9 +4033,9 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": 83.15,
-                        "int8": 114.9,
-                        "fp16": 148.56,
+                        "int4": "",
+                        "int8": 9.14,
+                        "fp16": "",
                         "fp32": "",
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@@ -4046,18 +4046,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU-only",
-        "Model": "resnet-50",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-8500 CPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 290.91,
+                        "int8": 59.7,
                         "fp16": "",
-                        "fp32": 71.84,
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                 ],
@@ -4068,7 +4068,7 @@
                 "Precisions": [
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-                        "int8": 7.67,
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@@ -4080,18 +4080,18 @@
         }
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-        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU-only",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Core™ i7-1185G7 CPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 75.02,
                         "fp16": "",
-                        "fp32": 1.25,
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                 ],
@@ -4102,7 +4102,7 @@
                 "Precisions": [
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                         "int4": "",
-                        "int8": "",
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@@ -4114,18 +4114,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU-only",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185GRE CPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 529.13,
+                        "int8": 52.75,
                         "fp16": "",
-                        "fp32": 180.71,
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                         "bf16": ""
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                 ],
@@ -4136,7 +4136,7 @@
                 "Precisions": [
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-                        "int8": 3.31,
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@@ -4148,18 +4148,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU-only",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-12700H CPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 6.79,
+                        "int8": 112.1,
                         "fp16": "",
-                        "fp32": 1.59,
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                 ],
@@ -4170,7 +4170,7 @@
                 "Precisions": [
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-                        "int8": 180.81,
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@@ -4182,18 +4182,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU-only",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1355U Processor CPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 326.51,
+                        "int8": 62.08,
                         "fp16": "",
-                        "fp32": 92.23,
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                 ],
@@ -4204,7 +4204,7 @@
                 "Precisions": [
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                         "int4": "",
-                        "int8": 5.21,
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@@ -4216,18 +4216,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1360P CPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 127.76,
+                        "int8": 83.44,
                         "fp16": "",
-                        "fp32": 47.25,
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                 ],
@@ -4238,7 +4238,7 @@
                 "Precisions": [
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                         "int4": "",
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@@ -4250,18 +4250,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H NPU-only",
-        "Model": "bert-base-cased",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-8700T CPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 89.37,
-                        "fp16": 74.27,
-                        "fp32": "",
+                        "int8": 52.39,
+                        "fp16": "",
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                         "bf16": ""
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@@ -4272,7 +4272,7 @@
                 "Precisions": [
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-                        "int8": 12.29,
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@@ -4284,18 +4284,18 @@
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     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H NPU-only",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ i9-10900TE CPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 10.39,
-                        "fp16": 6.74,
-                        "fp32": "",
+                        "int8": 65.23,
+                        "fp16": "",
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@@ -4306,7 +4306,7 @@
                 "Precisions": [
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@@ -4318,18 +4318,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H NPU-only",
+        "Platform": "Intel® Core™ i9-13900K CPU-only",
         "Model": "efficientdet-d0",
-        "Checked": true,
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 37.99,
-                        "fp16": 34.96,
-                        "fp32": "",
+                        "int8": 220.01,
+                        "fp16": "",
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                 ],
@@ -4340,7 +4340,7 @@
                 "Precisions": [
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@@ -4352,18 +4352,18 @@
         }
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     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H NPU-only",
-        "Model": "mobilenet-v2",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Processor N100 CPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1982.75,
-                        "fp16": 1361.8,
-                        "fp32": "",
+                        "int8": 15.45,
+                        "fp16": "",
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@@ -4374,7 +4374,7 @@
                 "Precisions": [
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-                        "int8": 0.69,
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@@ -4386,18 +4386,18 @@
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-        "Platform": "Intel® Core™ Ultra 7 processor 165H NPU-only",
-        "Model": "resnet-50",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® W1290P CPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
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-                        "int8": 772.85,
-                        "fp16": 383.69,
-                        "fp32": "",
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+                        "fp16": "",
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@@ -4408,7 +4408,7 @@
                 "Precisions": [
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-                        "int8": 1.54,
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@@ -4420,18 +4420,18 @@
         }
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     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H NPU-only",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® E-2124G CPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 705.19,
-                        "fp16": 458.52,
-                        "fp32": "",
+                        "int8": 35.92,
+                        "fp16": "",
+                        "fp32": 30.28,
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@@ -4442,7 +4442,7 @@
                 "Precisions": [
                     {
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-                        "int8": 1.65,
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@@ -4454,18 +4454,18 @@
         }
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     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H NPU-only",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Gold 5218T CPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
-                        "fp16": 9.91,
-                        "fp32": "",
+                        "int8": 271.61,
+                        "fp16": "",
+                        "fp32": 166.53,
                         "bf16": ""
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@@ -4476,7 +4476,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
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@@ -4488,18 +4488,18 @@
         }
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     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H NPU-only",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Platinum 8280 CPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 394.51,
-                        "fp16": 300.62,
-                        "fp32": "",
+                        "int8": 576.89,
+                        "fp16": "",
+                        "fp32": 325.0,
                         "bf16": ""
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@@ -4510,7 +4510,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 2.83,
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@@ -4522,18 +4522,18 @@
         }
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     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H NPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Platinum 8380 CPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 127.59,
-                        "fp16": 131.22,
-                        "fp32": "",
+                        "int8": 1014.6,
+                        "fp16": "",
+                        "fp32": 582.63,
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@@ -4544,7 +4544,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 9.0,
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@@ -4556,19 +4556,19 @@
         }
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     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H iGPU-only",
-        "Model": "bert-base-cased",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Platinum 8480+ CPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 158.5,
-                        "fp16": 108.29,
-                        "fp32": "",
-                        "bf16": ""
+                        "int8": 1454.4,
+                        "fp16": "",
+                        "fp32": 872.52,
+                        "bf16": 1037.91
                     }
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                 "Unit": "FPS",
@@ -4578,10 +4578,10 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 6.87,
+                        "int8": 4.6,
                         "fp16": "",
                         "fp32": "",
-                        "bf16": ""
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                 "Unit": "ms",
@@ -4590,19 +4590,19 @@
         }
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     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H iGPU-only",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Platinum 8580 CPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 19.78,
-                        "fp16": 13.08,
-                        "fp32": "",
-                        "bf16": ""
+                        "int8": 1736.65,
+                        "fp16": "",
+                        "fp32": 1132.21,
+                        "bf16": 1408.76
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                 "Unit": "FPS",
@@ -4612,10 +4612,10 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 50.82,
+                        "int8": 4.6,
                         "fp16": "",
                         "fp32": "",
-                        "bf16": ""
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                 "Unit": "ms",
@@ -4624,18 +4624,18 @@
         }
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     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H iGPU-only",
-        "Model": "chatglm2-6b",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Gold 6238L CPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 413.78,
                         "fp16": "",
-                        "fp32": "",
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@@ -4645,9 +4645,9 @@
             "latency": {
                 "Precisions": [
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-                        "int4": 64.67,
-                        "int8": 87.62,
-                        "fp16": 162.47,
+                        "int4": "",
+                        "int8": 8.54,
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                         "bf16": ""
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@@ -4658,18 +4658,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H iGPU-only",
+        "Platform": "Intel® Xeon® Silver 4316 CPU-only",
         "Model": "efficientdet-d0",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 172.52,
-                        "fp16": 142.15,
-                        "fp32": "",
+                        "int8": 497.16,
+                        "fp16": "",
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@@ -4680,7 +4680,7 @@
                 "Precisions": [
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-                        "int8": 7.96,
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@@ -4692,18 +4692,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H iGPU-only",
-        "Model": "falcon-7b-instruct",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 155H CPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 97.68,
                         "fp16": "",
-                        "fp32": "",
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@@ -4713,9 +4713,9 @@
             "latency": {
                 "Precisions": [
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-                        "int4": 69.8,
-                        "int8": 101.51,
-                        "fp16": 188.13,
+                        "int4": "",
+                        "int8": 21.46,
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@@ -4726,18 +4726,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H iGPU-only",
-        "Model": "llama-2-7b-chat-hf",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
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                         "fp16": "",
-                        "fp32": "",
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@@ -4747,9 +4747,9 @@
             "latency": {
                 "Precisions": [
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-                        "int4": 73.91,
-                        "int8": 102.29,
-                        "fp16": 184.95,
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+                        "int8": 25.46,
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@@ -4760,18 +4760,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H iGPU-only",
-        "Model": "llama-3-8b",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 9 processor 288V CPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
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-                        "fp32": "",
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@@ -4781,9 +4781,9 @@
             "latency": {
                 "Precisions": [
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-                        "int4": 71.5,
-                        "int8": 111.72,
-                        "fp16": 207.38,
+                        "int4": "",
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@@ -4794,17 +4794,17 @@
         }
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     {
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-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Data Center GPU Flex 170 dGPU",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1.82,
-                        "fp16": 1.41,
+                        "int8": 456.95,
+                        "fp16": 402.8,
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                         "bf16": ""
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@@ -4816,7 +4816,7 @@
                 "Precisions": [
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-                        "int8": 528.53,
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@@ -4828,10 +4828,10 @@
         }
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-        "Model": "mistral-7b-v0.1",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
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@@ -4849,9 +4849,9 @@
             "latency": {
                 "Precisions": [
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-                        "int4": 70.17,
-                        "int8": 107.23,
-                        "fp16": 196.47,
+                        "int4": "",
+                        "int8": 4.8,
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@@ -4862,17 +4862,17 @@
         }
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     {
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-        "Model": "mobilenet-v2",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1399.46,
-                        "fp16": 1370.74,
+                        "int8": 147.83,
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                         "bf16": ""
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@@ -4884,7 +4884,7 @@
                 "Precisions": [
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-                        "int8": 1.27,
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@@ -4896,17 +4896,17 @@
         }
     },
     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H iGPU-only",
-        "Model": "phi-3-mini-4k-instruct",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 155H NPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
-                        "fp16": "",
+                        "int8": 37.71,
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@@ -4917,9 +4917,9 @@
             "latency": {
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-                        "int4": 42.38,
-                        "int8": 63.04,
-                        "fp16": 107.68,
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@@ -4930,17 +4930,17 @@
         }
     },
     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H iGPU-only",
-        "Model": "resnet-50",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 165H NPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 577.04,
-                        "fp16": 377.92,
+                        "int8": 37.99,
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@@ -4952,7 +4952,7 @@
                 "Precisions": [
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-                        "int8": 2.23,
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@@ -4964,17 +4964,17 @@
         }
     },
     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H iGPU-only",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 9 processor 288V NPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 17.85,
-                        "fp16": 9.97,
+                        "int8": 13.38,
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@@ -4986,7 +4986,7 @@
                 "Precisions": [
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@@ -4998,17 +4998,17 @@
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-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Atom® x7425E iGPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 957.47,
-                        "fp16": 682.95,
+                        "int8": 40.02,
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                         "bf16": ""
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@@ -5020,7 +5020,7 @@
                 "Precisions": [
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-                        "int8": 1.34,
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@@ -5032,17 +5032,17 @@
         }
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-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Atom® X6425E iGPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 30.78,
-                        "fp16": 18.37,
+                        "int8": 21.7,
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@@ -5054,7 +5054,7 @@
                 "Precisions": [
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-                        "int8": 32.75,
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@@ -5066,17 +5066,17 @@
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-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Celeron® 6305E iGPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 871.13,
-                        "fp16": 499.08,
+                        "int8": 73.6,
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@@ -5088,7 +5088,7 @@
                 "Precisions": [
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-                        "int8": 1.41,
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@@ -5100,17 +5100,17 @@
         }
     },
     {
-        "Platform": "Intel® Core™ Ultra 7 processor 165H iGPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1235U Processor iGPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 379.7,
-                        "fp16": 272.5,
+                        "int8": 68.83,
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@@ -5122,7 +5122,7 @@
                 "Precisions": [
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-                        "int8": 3.1,
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@@ -5134,18 +5134,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ Ultra 9 processor 288V NPU-only",
-        "Model": "bert-base-cased",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1335U Processor iGPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 177.0,
-                        "fp16": 139.71,
-                        "fp32": 139.72,
+                        "int8": 80.63,
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@@ -5156,7 +5156,7 @@
                 "Precisions": [
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@@ -5168,16 +5168,16 @@
         }
     },
     {
-        "Platform": "Intel® Core™ Ultra 9 processor 288V NPU-only",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185G7 iGPU-only",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
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-                        "int8": 32.11,
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@@ -5202,17 +5202,17 @@
         }
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+        "Checked": "true",
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         "Parameters": {
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@@ -5224,7 +5224,7 @@
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@@ -5236,17 +5236,17 @@
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     {
-        "Platform": "Intel® Core™ Ultra 9 processor 288V NPU-only",
-        "Model": "mobilenet-v2",
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+        "Platform": "Intel® Core™ i7-12700H iGPU-only",
+        "Model": "efficientdet-d0",
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         "Parameters": {
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-                        "fp16": 2704.39,
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@@ -5258,7 +5258,7 @@
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@@ -5270,17 +5270,17 @@
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-        "Platform": "Intel® Core™ Ultra 9 processor 288V NPU-only",
-        "Model": "resnet-50",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1355U Processor iGPU-only",
+        "Model": "efficientdet-d0",
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         "Parameters": {
             "throughput": {
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-                        "int8": 1903.03,
-                        "fp16": 939.22,
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@@ -5292,7 +5292,7 @@
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@@ -5304,17 +5304,17 @@
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-        "Platform": "Intel® Core™ Ultra 9 processor 288V NPU-only",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1360P iGPU-only",
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-                        "fp16": 199.46,
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@@ -5326,7 +5326,7 @@
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@@ -5338,17 +5338,17 @@
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-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Processor N100 iGPU-only",
+        "Model": "efficientdet-d0",
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+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
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                         "int4": "",
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-                        "fp16": 45.86,
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@@ -5360,7 +5360,7 @@
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@@ -5372,17 +5372,17 @@
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     {
-        "Platform": "Intel® Core™ Ultra 9 processor 288V NPU-only",
-        "Model": "yolo_v3_tiny",
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+        "Platform": "Intel® Core™ Ultra 7 processor 155H iGPU-only",
+        "Model": "efficientdet-d0",
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         "Parameters": {
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-                        "int8": 1080.78,
-                        "fp16": 849.54,
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@@ -5394,7 +5394,7 @@
                 "Precisions": [
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@@ -5406,17 +5406,17 @@
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     {
-        "Platform": "Intel® Core™ Ultra 9 processor 288V NPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 165H iGPU-only",
+        "Model": "efficientdet-d0",
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         "Parameters": {
             "throughput": {
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-                        "int8": 390.32,
-                        "fp16": 504.91,
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@@ -5428,7 +5428,7 @@
                 "Precisions": [
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@@ -5441,16 +5441,16 @@
     },
     {
         "Platform": "Intel® Core™ Ultra 9 processor 288V iGPU-only",
-        "Model": "bert-base-cased",
-        "Checked": true,
+        "Model": "efficientdet-d0",
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         "Parameters": {
             "throughput": {
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-                        "int8": 283.6,
-                        "fp16": 271.24,
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@@ -5462,7 +5462,7 @@
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@@ -5474,18 +5474,18 @@
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-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Atom® x7425E CPU+iGPU",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 54.76,
-                        "fp16": 51.63,
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@@ -5508,18 +5508,18 @@
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-        "Model": "chatglm2-6b",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Atom® X6425E CPU+iGPU",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
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@@ -5529,7 +5529,7 @@
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@@ -5542,18 +5542,18 @@
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     },
     {
-        "Platform": "Intel® Core™ Ultra 9 processor 288V iGPU-only",
+        "Platform": "Intel® Celeron® 6305E CPU+iGPU",
         "Model": "efficientdet-d0",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 166.55,
-                        "fp16": 183.44,
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@@ -5576,9 +5576,9 @@
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-        "Platform": "Intel® Core™ Ultra 9 processor 288V iGPU-only",
-        "Model": "falcon-7b-instruct",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1235U Processor CPU+iGPU",
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+        "Checked": "true",
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@@ -5587,7 +5587,7 @@
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@@ -5597,7 +5597,7 @@
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@@ -5610,18 +5610,18 @@
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     {
-        "Platform": "Intel® Core™ Ultra 9 processor 288V iGPU-only",
-        "Model": "llama-2-7b-chat-hf",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1335U Processor CPU+iGPU",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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@@ -5631,7 +5631,7 @@
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@@ -5644,18 +5644,18 @@
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     {
-        "Platform": "Intel® Core™ Ultra 9 processor 288V iGPU-only",
-        "Model": "llama-3-8b",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185G7 CPU+iGPU",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -5665,7 +5665,7 @@
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@@ -5678,18 +5678,18 @@
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     },
     {
-        "Platform": "Intel® Core™ Ultra 9 processor 288V iGPU-only",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185GRE CPU+iGPU",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 12.11,
-                        "fp16": 7.1,
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@@ -5712,9 +5712,9 @@
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     {
-        "Platform": "Intel® Core™ Ultra 9 processor 288V iGPU-only",
-        "Model": "mistral-7b-v0.1",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-12700H CPU+iGPU",
+        "Model": "efficientdet-d0",
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@@ -5723,7 +5723,7 @@
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@@ -5733,7 +5733,7 @@
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@@ -5746,18 +5746,18 @@
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     {
-        "Platform": "Intel® Core™ Ultra 9 processor 288V iGPU-only",
-        "Model": "mobilenet-v2",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1355U Processor CPU+iGPU",
+        "Model": "efficientdet-d0",
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         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 922.77,
-                        "fp16": 1450.01,
-                        "fp32": "",
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@@ -5780,9 +5780,9 @@
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     {
-        "Platform": "Intel® Core™ Ultra 9 processor 288V iGPU-only",
-        "Model": "phi-3-mini-4k-instruct",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1360P CPU+iGPU",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
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@@ -5791,7 +5791,7 @@
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@@ -5801,7 +5801,7 @@
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@@ -5814,18 +5814,18 @@
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-        "Model": "resnet-50",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Processor N100 CPU+iGPU",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 1030.48,
-                        "fp16": 585.54,
-                        "fp32": "",
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@@ -5848,18 +5848,18 @@
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-        "Platform": "Intel® Core™ Ultra 9 processor 288V iGPU-only",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 155H CPU+iGPU",
+        "Model": "efficientdet-d0",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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-                        "fp16": 38.3,
-                        "fp32": "",
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@@ -5882,18 +5882,18 @@
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     {
-        "Platform": "Intel® Core™ Ultra 9 processor 288V iGPU-only",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU+iGPU",
+        "Model": "efficientdet-d0",
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         "Parameters": {
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-                        "fp16": 830.99,
-                        "fp32": "",
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@@ -5916,18 +5916,18 @@
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-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Celeron® 6305E CPU-only",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
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+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 101.99,
-                        "fp16": 72.98,
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@@ -5938,7 +5938,7 @@
                 "Precisions": [
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@@ -5950,18 +5950,18 @@
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-        "Platform": "Intel® Core™ Ultra 9 processor 288V iGPU-only",
-        "Model": "yolo_v3_tiny",
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+        "Model": "mask_rcnn_resnet50_atrous_coco",
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         "Parameters": {
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-                        "fp16": 766.89,
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@@ -5972,7 +5972,7 @@
                 "Precisions": [
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@@ -5984,18 +5984,18 @@
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-        "Platform": "Intel® Core™ Ultra 9 processor 288V iGPU-only",
-        "Model": "yolo_v8n",
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+        "Model": "mask_rcnn_resnet50_atrous_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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@@ -6006,7 +6006,7 @@
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@@ -6018,18 +6018,18 @@
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-        "Platform": "Intel® Core™ i3-8100 CPU-only",
-        "Model": "bert-base-cased",
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+        "Platform": "Intel® Core™ i5-1235U Processor CPU-only",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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@@ -6040,7 +6040,7 @@
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@@ -6052,18 +6052,18 @@
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-        "Platform": "Intel® Core™ i3-8100 CPU-only",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
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+        "Platform": "Intel® Core™ i5-1335U Processor CPU-only",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
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         "Parameters": {
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@@ -6074,7 +6074,7 @@
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@@ -6086,18 +6086,18 @@
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-        "Platform": "Intel® Core™ i3-8100 CPU-only",
-        "Model": "efficientdet-d0",
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             "throughput": {
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@@ -6108,7 +6108,7 @@
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@@ -6120,18 +6120,18 @@
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-        "Platform": "Intel® Core™ i3-8100 CPU-only",
+        "Platform": "Intel® Core™ i5-8500 CPU-only",
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+        "Checked": "true",
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         "Parameters": {
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-                        "int8": 0.3,
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@@ -6142,7 +6142,7 @@
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@@ -6154,18 +6154,18 @@
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     {
-        "Platform": "Intel® Core™ i3-8100 CPU-only",
-        "Model": "mobilenet-v2",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185G7 CPU-only",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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-                        "int8": 532.22,
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@@ -6176,7 +6176,7 @@
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@@ -6188,18 +6188,18 @@
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-        "Platform": "Intel® Core™ i3-8100 CPU-only",
-        "Model": "resnet-50",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185GRE CPU-only",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
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         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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-                        "int8": 96.34,
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@@ -6210,7 +6210,7 @@
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@@ -6222,18 +6222,18 @@
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-        "Platform": "Intel® Core™ i3-8100 CPU-only",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-12700H CPU-only",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
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         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -6244,7 +6244,7 @@
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@@ -6256,18 +6256,18 @@
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     {
-        "Platform": "Intel® Core™ i3-8100 CPU-only",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1355U Processor CPU-only",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
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         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -6278,7 +6278,7 @@
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@@ -6290,18 +6290,18 @@
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-        "Platform": "Intel® Core™ i3-8100 CPU-only",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1360P CPU-only",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
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         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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-                        "int8": 2.52,
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@@ -6312,7 +6312,7 @@
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@@ -6324,18 +6324,18 @@
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     {
-        "Platform": "Intel® Core™ i3-8100 CPU-only",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-8700T CPU-only",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
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         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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@@ -6346,7 +6346,7 @@
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@@ -6358,18 +6358,18 @@
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     {
-        "Platform": "Intel® Core™ i3-8100 CPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
+        "Platform": "Intel® Core™ i9-10900TE CPU-only",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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-                        "int8": 53.67,
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@@ -6380,7 +6380,7 @@
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@@ -6392,18 +6392,18 @@
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-        "Platform": "Intel® Core™ i5-10500TE CPU-only",
-        "Model": "bert-base-cased",
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+        "Model": "mask_rcnn_resnet50_atrous_coco",
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         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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@@ -6414,7 +6414,7 @@
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@@ -6426,18 +6426,18 @@
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-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® W1290P CPU-only",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
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+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
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@@ -6448,7 +6448,7 @@
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@@ -6460,18 +6460,18 @@
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-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® E-2124G CPU-only",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
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@@ -6482,7 +6482,7 @@
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@@ -6494,18 +6494,18 @@
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     {
-        "Platform": "Intel® Core™ i5-10500TE CPU-only",
+        "Platform": "Intel® Xeon® Gold 5218T CPU-only",
         "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
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@@ -6516,7 +6516,7 @@
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@@ -6528,18 +6528,18 @@
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-        "Model": "mobilenet-v2",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
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+        "Model": "mask_rcnn_resnet50_atrous_coco",
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+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
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@@ -6550,7 +6550,7 @@
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@@ -6562,18 +6562,18 @@
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-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Platinum 8380 CPU-only",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
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@@ -6584,7 +6584,7 @@
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@@ -6596,19 +6596,19 @@
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-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Platinum 8480+ CPU-only",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
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@@ -6618,10 +6618,10 @@
                 "Precisions": [
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@@ -6630,19 +6630,19 @@
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-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Platinum 8580 CPU-only",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
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+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 329.06,
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                 "Unit": "FPS",
@@ -6652,10 +6652,10 @@
                 "Precisions": [
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@@ -6664,18 +6664,18 @@
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-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Gold 6238L CPU-only",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
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                         "int4": "",
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@@ -6686,7 +6686,7 @@
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@@ -6698,18 +6698,18 @@
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-        "PlatformType": "Client Platforms (Intel® Core™)",
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+        "Model": "mask_rcnn_resnet50_atrous_coco",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
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-                        "int8": 166.67,
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@@ -6720,7 +6720,7 @@
                 "Precisions": [
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@@ -6732,18 +6732,18 @@
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     {
-        "Platform": "Intel® Core™ i5-10500TE CPU-only",
-        "Model": "yolo_v8n",
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+        "Model": "mask_rcnn_resnet50_atrous_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -6754,7 +6754,7 @@
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@@ -6766,18 +6766,18 @@
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     {
-        "Platform": "Intel® Core™ i5-1235U Processor CPU+iGPU",
-        "Model": "bert-base-cased",
-        "Checked": true,
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+        "Model": "mask_rcnn_resnet50_atrous_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -6788,7 +6788,7 @@
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@@ -6800,18 +6800,18 @@
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-        "Platform": "Intel® Core™ i5-1235U Processor CPU+iGPU",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
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+        "Platform": "Intel® Core™ Ultra 9 processor 288V CPU-only",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -6822,7 +6822,7 @@
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@@ -6834,18 +6834,18 @@
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-        "Model": "efficientdet-d0",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Data Center GPU Flex 170 dGPU",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
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-                        "int8": 54.04,
-                        "fp16": "",
-                        "fp32": 31.23,
+                        "int8": 39.0,
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@@ -6856,7 +6856,7 @@
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@@ -6868,18 +6868,18 @@
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     {
-        "Platform": "Intel® Core™ i5-1235U Processor CPU+iGPU",
+        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
         "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
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@@ -6890,7 +6890,7 @@
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@@ -6902,18 +6902,18 @@
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-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
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+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
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                         "int4": "",
-                        "int8": 876.52,
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@@ -6924,7 +6924,7 @@
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@@ -6936,18 +6936,18 @@
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-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Celeron® 6305E iGPU-only",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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-                        "int8": 195.29,
-                        "fp16": "",
-                        "fp32": 65.09,
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@@ -6958,7 +6958,7 @@
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@@ -6970,18 +6970,18 @@
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-        "Platform": "Intel® Core™ i5-1235U Processor CPU+iGPU",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1235U Processor iGPU-only",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
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         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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-                        "int8": 5.79,
-                        "fp16": "",
-                        "fp32": 1.29,
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@@ -6992,7 +6992,7 @@
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@@ -7004,18 +7004,18 @@
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-        "Platform": "Intel® Core™ i5-1235U Processor CPU+iGPU",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1335U Processor iGPU-only",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
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         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 384.96,
-                        "fp16": "",
-                        "fp32": 132.61,
+                        "int8": 0.54,
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@@ -7026,7 +7026,7 @@
                 "Precisions": [
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@@ -7038,18 +7038,18 @@
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-        "Platform": "Intel® Core™ i5-1235U Processor CPU+iGPU",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185G7 iGPU-only",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 5.64,
-                        "fp16": "",
-                        "fp32": 2.0,
+                        "int8": 0.83,
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@@ -7060,7 +7060,7 @@
                 "Precisions": [
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@@ -7072,18 +7072,18 @@
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     {
-        "Platform": "Intel® Core™ i5-1235U Processor CPU+iGPU",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185GRE iGPU-only",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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-                        "int8": 238.44,
-                        "fp16": "",
-                        "fp32": 83.92,
+                        "int8": 0.57,
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@@ -7094,7 +7094,7 @@
                 "Precisions": [
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@@ -7106,18 +7106,18 @@
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     {
-        "Platform": "Intel® Core™ i5-1235U Processor CPU+iGPU",
-        "Model": "yolo_v8n",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-12700H iGPU-only",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 94.98,
-                        "fp16": "",
-                        "fp32": 42.09,
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@@ -7128,7 +7128,7 @@
                 "Precisions": [
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@@ -7140,18 +7140,18 @@
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     {
-        "Platform": "Intel® Core™ i5-1235U Processor CPU-only",
-        "Model": "bert-base-cased",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1355U Processor iGPU-only",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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-                        "int8": 32.98,
-                        "fp16": "",
-                        "fp32": 12.72,
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@@ -7162,7 +7162,7 @@
                 "Precisions": [
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@@ -7174,18 +7174,18 @@
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-        "Platform": "Intel® Core™ i5-1235U Processor CPU-only",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
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+        "Platform": "Intel® Core™ i7-1360P iGPU-only",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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-                        "int8": 2.53,
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@@ -7196,7 +7196,7 @@
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@@ -7208,18 +7208,18 @@
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     {
-        "Platform": "Intel® Core™ i5-1235U Processor CPU-only",
-        "Model": "efficientdet-d0",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 155H iGPU-only",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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-                        "int8": 46.89,
-                        "fp16": "",
-                        "fp32": 24.24,
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@@ -7230,7 +7230,7 @@
                 "Precisions": [
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@@ -7242,18 +7242,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i5-1235U Processor CPU-only",
+        "Platform": "Intel® Core™ Ultra 7 processor 165H iGPU-only",
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@@ -7264,7 +7264,7 @@
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@@ -7276,18 +7276,18 @@
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     {
-        "Platform": "Intel® Core™ i5-1235U Processor CPU-only",
-        "Model": "mobilenet-v2",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 9 processor 288V iGPU-only",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
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         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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-                        "int8": 900.76,
-                        "fp16": "",
-                        "fp32": 334.56,
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@@ -7298,7 +7298,7 @@
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@@ -7310,18 +7310,18 @@
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-        "Model": "resnet-50",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Celeron® 6305E CPU+iGPU",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 160.94,
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@@ -7332,7 +7332,7 @@
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@@ -7344,9 +7344,9 @@
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-        "Platform": "Intel® Core™ i5-1235U Processor CPU-only",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1235U Processor CPU+iGPU",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
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         "Parameters": {
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@@ -7355,7 +7355,7 @@
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@@ -7378,18 +7378,18 @@
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-        "Platform": "Intel® Core™ i5-1235U Processor CPU-only",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1335U Processor CPU+iGPU",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 351.97,
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@@ -7400,7 +7400,7 @@
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@@ -7412,18 +7412,18 @@
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-        "Platform": "Intel® Core™ i5-1235U Processor CPU-only",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185G7 CPU+iGPU",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 3.89,
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@@ -7434,7 +7434,7 @@
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@@ -7446,18 +7446,18 @@
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-        "Platform": "Intel® Core™ i5-1235U Processor CPU-only",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185GRE CPU+iGPU",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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-                        "int8": 182.6,
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@@ -7468,7 +7468,7 @@
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@@ -7480,18 +7480,18 @@
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-        "Platform": "Intel® Core™ i5-1235U Processor CPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-12700H CPU+iGPU",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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@@ -7502,7 +7502,7 @@
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@@ -7514,18 +7514,18 @@
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-        "Platform": "Intel® Core™ i5-1235U Processor iGPU-only",
-        "Model": "bert-base-cased",
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+        "Platform": "Intel® Core™ i7-1355U Processor CPU+iGPU",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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-                        "int8": 48.26,
-                        "fp16": 40.03,
-                        "fp32": "",
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@@ -7536,7 +7536,7 @@
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@@ -7548,18 +7548,18 @@
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-        "Platform": "Intel® Core™ i5-1235U Processor iGPU-only",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
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+        "Platform": "Intel® Core™ i7-1360P CPU+iGPU",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
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         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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-                        "int8": 5.38,
-                        "fp16": 4.11,
-                        "fp32": "",
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@@ -7570,7 +7570,7 @@
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@@ -7582,18 +7582,18 @@
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-        "Model": "efficientdet-d0",
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+        "Platform": "Intel® Core™ Ultra 7 processor 155H CPU+iGPU",
+        "Model": "mask_rcnn_resnet50_atrous_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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-                        "int8": 69.88,
-                        "fp16": 52.51,
-                        "fp32": "",
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@@ -7604,7 +7604,7 @@
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@@ -7616,18 +7616,18 @@
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     {
-        "Platform": "Intel® Core™ i5-1235U Processor iGPU-only",
+        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU+iGPU",
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+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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-                        "int8": 0.54,
-                        "fp16": 0.53,
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@@ -7638,7 +7638,7 @@
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@@ -7650,18 +7650,18 @@
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     {
-        "Platform": "Intel® Core™ i5-1235U Processor iGPU-only",
+        "Platform": "Intel® Atom® x7425E CPU-only",
         "Model": "mobilenet-v2",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 791.77,
-                        "fp16": 502.34,
-                        "fp32": "",
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@@ -7672,7 +7672,7 @@
                 "Precisions": [
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@@ -7684,18 +7684,18 @@
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-        "Model": "resnet-50",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Atom® X6425E CPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
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                         "int4": "",
-                        "int8": 217.18,
-                        "fp16": 121.8,
-                        "fp32": "",
+                        "int8": 133.76,
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@@ -7706,7 +7706,7 @@
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@@ -7718,18 +7718,18 @@
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-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Celeron® 6305E CPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 5.82,
-                        "fp16": 2.84,
-                        "fp32": "",
+                        "int8": 300.95,
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@@ -7740,7 +7740,7 @@
                 "Precisions": [
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@@ -7752,18 +7752,18 @@
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     {
-        "Platform": "Intel® Core™ i5-1235U Processor iGPU-only",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ i3-8100 CPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
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-                        "int8": 401.08,
-                        "fp16": 235.24,
-                        "fp32": "",
+                        "int8": 534.91,
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@@ -7774,7 +7774,7 @@
                 "Precisions": [
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@@ -7786,18 +7786,18 @@
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     },
     {
-        "Platform": "Intel® Core™ i5-1235U Processor iGPU-only",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-10500TE CPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
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-                        "int8": 10.05,
-                        "fp16": 4.74,
-                        "fp32": "",
+                        "int8": 893.76,
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@@ -7808,7 +7808,7 @@
                 "Precisions": [
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-                        "int8": 79.14,
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@@ -7820,18 +7820,18 @@
         }
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     {
-        "Platform": "Intel® Core™ i5-1235U Processor iGPU-only",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1235U Processor CPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 327.91,
-                        "fp16": 166.92,
-                        "fp32": "",
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@@ -7842,7 +7842,7 @@
                 "Precisions": [
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-                        "int8": 2.95,
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@@ -7854,18 +7854,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i5-1235U Processor iGPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1335U Processor CPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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-                        "int8": 133.75,
-                        "fp16": 82.09,
-                        "fp32": "",
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@@ -7876,7 +7876,7 @@
                 "Precisions": [
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@@ -7888,18 +7888,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i5-1335U Processor CPU+iGPU",
-        "Model": "bert-base-cased",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-13600K CPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 50.08,
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-                        "fp32": 26.89,
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@@ -7910,7 +7910,7 @@
                 "Precisions": [
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@@ -7922,18 +7922,18 @@
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-        "Platform": "Intel® Core™ i5-1335U Processor CPU+iGPU",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-8500 CPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 5.15,
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@@ -7944,7 +7944,7 @@
                 "Precisions": [
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@@ -7956,18 +7956,18 @@
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     },
     {
-        "Platform": "Intel® Core™ i5-1335U Processor CPU+iGPU",
-        "Model": "efficientdet-d0",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185G7 CPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 75.84,
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-                        "fp32": 49.82,
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@@ -7978,7 +7978,7 @@
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@@ -7990,18 +7990,18 @@
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     {
-        "Platform": "Intel® Core™ i5-1335U Processor CPU+iGPU",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185GRE CPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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-                        "int8": 0.7,
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@@ -8012,7 +8012,7 @@
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@@ -8024,18 +8024,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i5-1335U Processor CPU+iGPU",
+        "Platform": "Intel® Core™ i7-12700H CPU-only",
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+        "Checked": "true",
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         "Parameters": {
             "throughput": {
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-                        "int8": 1068.2,
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@@ -8046,7 +8046,7 @@
                 "Precisions": [
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@@ -8058,18 +8058,18 @@
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     {
-        "Platform": "Intel® Core™ i5-1335U Processor CPU+iGPU",
-        "Model": "resnet-50",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1355U Processor CPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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-                        "int8": 242.35,
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@@ -8080,7 +8080,7 @@
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@@ -8092,18 +8092,18 @@
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     {
-        "Platform": "Intel® Core™ i5-1335U Processor CPU+iGPU",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1360P CPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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@@ -8126,18 +8126,18 @@
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     {
-        "Platform": "Intel® Core™ i5-1335U Processor CPU+iGPU",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-8700T CPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 479.66,
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-                        "fp32": 192.54,
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@@ -8148,7 +8148,7 @@
                 "Precisions": [
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@@ -8160,18 +8160,18 @@
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     },
     {
-        "Platform": "Intel® Core™ i5-1335U Processor CPU+iGPU",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ i9-10900TE CPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 7.49,
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@@ -8182,7 +8182,7 @@
                 "Precisions": [
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@@ -8194,18 +8194,18 @@
         }
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     {
-        "Platform": "Intel® Core™ i5-1335U Processor CPU+iGPU",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
+        "Platform": "Intel® Core™ i9-13900K CPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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-                        "int8": 287.86,
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@@ -8216,7 +8216,7 @@
                 "Precisions": [
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@@ -8228,18 +8228,18 @@
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-        "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Processor N100 CPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 129.45,
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-                        "fp32": 58.75,
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@@ -8250,7 +8250,7 @@
                 "Precisions": [
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@@ -8262,18 +8262,18 @@
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-        "Model": "bert-base-cased",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® W1290P CPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
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                         "int4": "",
-                        "int8": 40.42,
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-                        "fp32": 16.12,
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@@ -8284,7 +8284,7 @@
                 "Precisions": [
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@@ -8296,18 +8296,18 @@
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-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® E-2124G CPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
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                         "int4": "",
-                        "int8": 3.39,
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@@ -8318,7 +8318,7 @@
                 "Precisions": [
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@@ -8330,18 +8330,18 @@
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-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Gold 5218T CPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
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                         "int4": "",
-                        "int8": 56.95,
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@@ -8352,7 +8352,7 @@
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@@ -8364,18 +8364,18 @@
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-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Platinum 8280 CPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
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                         "int4": "",
-                        "int8": 0.58,
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@@ -8386,7 +8386,7 @@
                 "Precisions": [
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-                        "int8": 2050.89,
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@@ -8398,18 +8398,18 @@
         }
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     {
-        "Platform": "Intel® Core™ i5-1335U Processor CPU-only",
+        "Platform": "Intel® Xeon® Platinum 8380 CPU-only",
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-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
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-                        "int8": 962.54,
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-                        "fp32": 469.49,
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@@ -8420,7 +8420,7 @@
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@@ -8432,19 +8432,19 @@
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-        "Model": "resnet-50",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Platinum 8480+ CPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 187.24,
+                        "int8": 38642.1,
                         "fp16": "",
-                        "fp32": 54.08,
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+                        "fp32": 10319.56,
+                        "bf16": 25708.49
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                 "Unit": "FPS",
@@ -8454,10 +8454,10 @@
                 "Precisions": [
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-                        "int8": 9.42,
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@@ -8466,18 +8466,18 @@
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-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Platinum 8580 CPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
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+                        "int8": 39913.42,
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@@ -8491,7 +8491,7 @@
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@@ -8500,18 +8500,18 @@
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-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Gold 6238L CPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
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-                        "int8": 388.55,
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-                        "fp32": 137.54,
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@@ -8522,7 +8522,7 @@
                 "Precisions": [
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                         "fp32": "",
                         "bf16": ""
@@ -8534,18 +8534,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i5-1335U Processor CPU-only",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Silver 4316 CPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 4.66,
+                        "int8": 11955.69,
                         "fp16": "",
-                        "fp32": 0.84,
+                        "fp32": 3563.39,
                         "bf16": ""
                     }
                 ],
@@ -8556,7 +8556,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 308.83,
+                        "int8": "",
                         "fp16": "",
                         "fp32": "",
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@@ -8568,18 +8568,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i5-1335U Processor CPU-only",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 155H CPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 211.93,
+                        "int8": 1984.45,
                         "fp16": "",
-                        "fp32": 69.29,
+                        "fp32": 814.84,
                         "bf16": ""
                     }
                 ],
@@ -8590,7 +8590,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 7.77,
+                        "int8": 1.35,
                         "fp16": "",
                         "fp32": "",
                         "bf16": ""
@@ -8602,18 +8602,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i5-1335U Processor CPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 92.71,
+                        "int8": 1516.8,
                         "fp16": "",
-                        "fp32": 36.74,
+                        "fp32": 588.51,
                         "bf16": ""
                     }
                 ],
@@ -8624,7 +8624,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 18.06,
+                        "int8": 1.61,
                         "fp16": "",
                         "fp32": "",
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@@ -8636,18 +8636,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i5-1335U Processor iGPU-only",
-        "Model": "bert-base-cased",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 9 processor 288V CPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 48.14,
-                        "fp16": 40.18,
-                        "fp32": "",
+                        "int8": 1651.84,
+                        "fp16": "",
+                        "fp32": 775.78,
                         "bf16": ""
                     }
                 ],
@@ -8658,7 +8658,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 17.95,
+                        "int8": 1.02,
                         "fp16": "",
                         "fp32": "",
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@@ -8670,17 +8670,17 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i5-1335U Processor iGPU-only",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Data Center GPU Flex 170 dGPU",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 6.29,
-                        "fp16": 4.3,
+                        "int8": 2423.58,
+                        "fp16": 2444.62,
                         "fp32": "",
                         "bf16": ""
                     }
@@ -8692,7 +8692,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 137.43,
+                        "int8": 0.64,
                         "fp16": "",
                         "fp32": "",
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@@ -8704,17 +8704,17 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i5-1335U Processor iGPU-only",
-        "Model": "efficientdet-d0",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 82.42,
-                        "fp16": 61.15,
+                        "int8": "",
+                        "fp16": "",
                         "fp32": "",
                         "bf16": ""
                     }
@@ -8726,7 +8726,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 14.52,
+                        "int8": 0.99,
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                         "fp32": "",
                         "bf16": ""
@@ -8738,17 +8738,17 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i5-1335U Processor iGPU-only",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 0.55,
-                        "fp16": 0.59,
+                        "int8": 1862.03,
+                        "fp16": 1564.35,
                         "fp32": "",
                         "bf16": ""
                     }
@@ -8760,7 +8760,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1464.69,
+                        "int8": 0.78,
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                         "fp32": "",
                         "bf16": ""
@@ -8772,17 +8772,17 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i5-1335U Processor iGPU-only",
+        "Platform": "Intel® Core™ Ultra 7 processor 155H NPU-only",
         "Model": "mobilenet-v2",
-        "Checked": true,
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 811.57,
-                        "fp16": 542.42,
+                        "int8": 1956.69,
+                        "fp16": 1343.46,
                         "fp32": "",
                         "bf16": ""
                     }
@@ -8794,7 +8794,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1.44,
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                         "fp32": "",
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@@ -8806,17 +8806,17 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i5-1335U Processor iGPU-only",
-        "Model": "resnet-50",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 165H NPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 229.04,
-                        "fp16": 128.78,
+                        "int8": 1999.97,
+                        "fp16": 1359.27,
                         "fp32": "",
                         "bf16": ""
                     }
@@ -8828,7 +8828,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 4.2,
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@@ -8840,17 +8840,17 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i5-1335U Processor iGPU-only",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 9 processor 288V NPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 6.05,
-                        "fp16": 2.96,
+                        "int8": 3751.95,
+                        "fp16": 2877.38,
                         "fp32": "",
                         "bf16": ""
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@@ -8862,7 +8862,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 143.23,
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@@ -8874,17 +8874,17 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i5-1335U Processor iGPU-only",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Atom® x7425E iGPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 412.4,
-                        "fp16": 244.34,
+                        "int8": 415.36,
+                        "fp16": 324.74,
                         "fp32": "",
                         "bf16": ""
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@@ -8896,7 +8896,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 2.71,
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@@ -8908,17 +8908,17 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i5-1335U Processor iGPU-only",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Atom® X6425E iGPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 10.0,
-                        "fp16": 4.8,
+                        "int8": 188.4,
+                        "fp16": 223.08,
                         "fp32": "",
                         "bf16": ""
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@@ -8930,7 +8930,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 82.06,
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@@ -8942,17 +8942,17 @@
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     },
     {
-        "Platform": "Intel® Core™ i5-1335U Processor iGPU-only",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Celeron® 6305E iGPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 324.18,
-                        "fp16": 168.85,
+                        "int8": 673.52,
+                        "fp16": 505.05,
                         "fp32": "",
                         "bf16": ""
                     }
@@ -8964,7 +8964,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 2.88,
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@@ -8976,17 +8976,17 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i5-1335U Processor iGPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1235U Processor iGPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 135.98,
-                        "fp16": 84.06,
+                        "int8": 779.69,
+                        "fp16": 496.63,
                         "fp32": "",
                         "bf16": ""
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@@ -8998,7 +8998,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 6.88,
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@@ -9010,18 +9010,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i5-13600K CPU-only",
-        "Model": "bert-base-cased",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1335U Processor iGPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 119.86,
+                        "int8": "",
                         "fp16": "",
-                        "fp32": 47.19,
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                         "bf16": ""
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                 ],
@@ -9032,7 +9032,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 13.16,
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                         "fp16": "",
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@@ -9044,18 +9044,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i5-13600K CPU-only",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185G7 iGPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 10.28,
-                        "fp16": "",
-                        "fp32": 3.91,
+                        "int8": "",
+                        "fp16": 574.04,
+                        "fp32": "",
                         "bf16": ""
                     }
                 ],
@@ -9066,7 +9066,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 128.91,
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@@ -9078,18 +9078,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i5-13600K CPU-only",
-        "Model": "efficientdet-d0",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185GRE iGPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 152.58,
-                        "fp16": "",
-                        "fp32": 93.16,
+                        "int8": 630.96,
+                        "fp16": 442.65,
+                        "fp32": "",
                         "bf16": ""
                     }
                 ],
@@ -9100,7 +9100,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 9.12,
+                        "int8": 1.74,
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@@ -9112,18 +9112,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i5-13600K CPU-only",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-12700H iGPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1.71,
-                        "fp16": "",
-                        "fp32": 0.5,
+                        "int8": 1281.44,
+                        "fp16": 911.94,
+                        "fp32": "",
                         "bf16": ""
                     }
                 ],
@@ -9134,7 +9134,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 709.42,
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                         "bf16": ""
@@ -9146,18 +9146,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i5-13600K CPU-only",
+        "Platform": "Intel® Core™ i7-1355U Processor iGPU-only",
         "Model": "mobilenet-v2",
-        "Checked": true,
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 2994.84,
-                        "fp16": "",
-                        "fp32": 1327.68,
+                        "int8": 890.07,
+                        "fp16": 624.4,
+                        "fp32": "",
                         "bf16": ""
                     }
                 ],
@@ -9168,7 +9168,7 @@
                 "Precisions": [
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                         "int4": "",
-                        "int8": 0.69,
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@@ -9180,18 +9180,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i5-13600K CPU-only",
-        "Model": "resnet-50",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1360P iGPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 544.19,
+                        "int8": "",
                         "fp16": "",
-                        "fp32": 150.23,
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                         "bf16": ""
                     }
                 ],
@@ -9202,7 +9202,7 @@
                 "Precisions": [
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                         "int4": "",
-                        "int8": 2.84,
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@@ -9214,18 +9214,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i5-13600K CPU-only",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Processor N100 iGPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
-                        "fp16": "",
-                        "fp32": 2.5,
+                        "int8": 339.71,
+                        "fp16": 267.18,
+                        "fp32": "",
                         "bf16": ""
                     }
                 ],
@@ -9236,7 +9236,7 @@
                 "Precisions": [
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                         "int4": "",
-                        "int8": "",
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@@ -9248,18 +9248,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i5-13600K CPU-only",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 155H iGPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1092.37,
-                        "fp16": "",
-                        "fp32": 389.37,
+                        "int8": 1298.67,
+                        "fp16": 1337.24,
+                        "fp32": "",
                         "bf16": ""
                     }
                 ],
@@ -9270,7 +9270,7 @@
                 "Precisions": [
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                         "int4": "",
-                        "int8": 1.32,
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@@ -9282,18 +9282,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i5-13600K CPU-only",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 165H iGPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 12.64,
-                        "fp16": "",
-                        "fp32": 3.3,
+                        "int8": 1422.4,
+                        "fp16": 1361.15,
+                        "fp32": "",
                         "bf16": ""
                     }
                 ],
@@ -9304,7 +9304,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 93.33,
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@@ -9316,18 +9316,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i5-13600K CPU-only",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 9 processor 288V iGPU-only",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 638.85,
-                        "fp16": "",
-                        "fp32": 199.4,
+                        "int8": 1200.66,
+                        "fp16": 1554.15,
+                        "fp32": "",
                         "bf16": ""
                     }
                 ],
@@ -9338,7 +9338,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 2.38,
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@@ -9350,18 +9350,18 @@
         }
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-        "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Atom® x7425E CPU+iGPU",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 271.84,
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@@ -9372,7 +9372,7 @@
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@@ -9384,18 +9384,18 @@
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-        "Model": "bert-base-cased",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Atom® X6425E CPU+iGPU",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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-                        "int8": 33.09,
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-                        "fp32": 22.84,
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@@ -9406,7 +9406,7 @@
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@@ -9418,18 +9418,18 @@
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-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Celeron® 6305E CPU+iGPU",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 3.0,
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-                        "fp32": 1.91,
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@@ -9440,7 +9440,7 @@
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@@ -9452,18 +9452,18 @@
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     {
-        "Platform": "Intel® Core™ i5-8500 CPU-only",
-        "Model": "efficientdet-d0",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1235U Processor CPU+iGPU",
+        "Model": "mobilenet-v2",
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         "Parameters": {
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-                        "int8": 59.52,
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-                        "fp32": 42.47,
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@@ -9474,7 +9474,7 @@
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@@ -9486,18 +9486,18 @@
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-        "Platform": "Intel® Core™ i5-8500 CPU-only",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1335U Processor CPU+iGPU",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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-                        "int8": 0.48,
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-                        "fp32": 0.22,
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@@ -9508,7 +9508,7 @@
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@@ -9520,18 +9520,18 @@
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     {
-        "Platform": "Intel® Core™ i5-8500 CPU-only",
+        "Platform": "Intel® Core™ i7-1185G7 CPU+iGPU",
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+        "Checked": "true",
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-                        "int8": 849.65,
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@@ -9542,7 +9542,7 @@
                 "Precisions": [
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@@ -9554,18 +9554,18 @@
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     {
-        "Platform": "Intel® Core™ i5-8500 CPU-only",
-        "Model": "resnet-50",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185GRE CPU+iGPU",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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-                        "int8": 150.12,
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-                        "fp32": 76.35,
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@@ -9576,7 +9576,7 @@
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@@ -9588,18 +9588,18 @@
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     {
-        "Platform": "Intel® Core™ i5-8500 CPU-only",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-12700H CPU+iGPU",
+        "Model": "mobilenet-v2",
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         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
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-                        "fp32": 1.35,
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@@ -9622,18 +9622,18 @@
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     {
-        "Platform": "Intel® Core™ i5-8500 CPU-only",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1355U Processor CPU+iGPU",
+        "Model": "mobilenet-v2",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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-                        "int8": 336.17,
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-                        "fp32": 183.19,
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@@ -9644,7 +9644,7 @@
                 "Precisions": [
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@@ -9656,18 +9656,18 @@
         }
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     {
-        "Platform": "Intel® Core™ i5-8500 CPU-only",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1360P CPU+iGPU",
+        "Model": "mobilenet-v2",
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         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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-                        "int8": 3.92,
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@@ -9678,7 +9678,7 @@
                 "Precisions": [
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@@ -9690,18 +9690,18 @@
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-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
-        "Parameters": {
-            "throughput": {
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+        "Model": "mobilenet-v2",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Parameters": {
+            "throughput": {
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-                        "fp32": 90.91,
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@@ -9712,7 +9712,7 @@
                 "Precisions": [
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@@ -9724,18 +9724,18 @@
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     },
     {
-        "Platform": "Intel® Core™ i5-8500 CPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 155H CPU+iGPU",
+        "Model": "mobilenet-v2",
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         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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-                        "int8": 83.81,
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@@ -9746,7 +9746,7 @@
                 "Precisions": [
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@@ -9758,18 +9758,18 @@
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     },
     {
-        "Platform": "Intel® Core™ i7-1185G7 CPU+iGPU",
-        "Model": "bert-base-cased",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU+iGPU",
+        "Model": "mobilenet-v2",
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         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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@@ -9792,18 +9792,18 @@
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-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
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-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Atom® x7425E CPU-only",
+        "Model": "resnet-50",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 9.61,
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@@ -9814,7 +9814,7 @@
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@@ -9826,18 +9826,18 @@
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-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Atom® X6425E CPU-only",
+        "Model": "resnet-50",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 104.42,
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@@ -9848,7 +9848,7 @@
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@@ -9860,18 +9860,18 @@
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-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Celeron® 6305E CPU-only",
+        "Model": "resnet-50",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
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@@ -9882,7 +9882,7 @@
                 "Precisions": [
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@@ -9894,18 +9894,18 @@
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     {
-        "Platform": "Intel® Core™ i7-1185G7 CPU+iGPU",
-        "Model": "mobilenet-v2",
-        "Checked": true,
+        "Platform": "Intel® Core™ i3-8100 CPU-only",
+        "Model": "resnet-50",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 1407.44,
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-                        "fp32": 706.14,
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@@ -9916,7 +9916,7 @@
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@@ -9928,18 +9928,18 @@
         }
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     {
-        "Platform": "Intel® Core™ i7-1185G7 CPU+iGPU",
+        "Platform": "Intel® Core™ i5-10500TE CPU-only",
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+        "Checked": "true",
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         "Parameters": {
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@@ -9950,7 +9950,7 @@
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@@ -9962,18 +9962,18 @@
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     {
-        "Platform": "Intel® Core™ i7-1185G7 CPU+iGPU",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1235U Processor CPU-only",
+        "Model": "resnet-50",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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@@ -9984,7 +9984,7 @@
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@@ -9996,18 +9996,18 @@
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-        "Platform": "Intel® Core™ i7-1185G7 CPU+iGPU",
-        "Model": "ssd_mobilenet_v1_coco",
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+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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-                        "int8": 680.43,
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@@ -10018,7 +10018,7 @@
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@@ -10030,18 +10030,18 @@
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-        "Platform": "Intel® Core™ i7-1185G7 CPU+iGPU",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
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         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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@@ -10052,7 +10052,7 @@
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@@ -10064,18 +10064,18 @@
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     {
-        "Platform": "Intel® Core™ i7-1185G7 CPU+iGPU",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
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+        "Model": "resnet-50",
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         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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@@ -10086,7 +10086,7 @@
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@@ -10098,18 +10098,18 @@
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-        "Model": "yolo_v8n",
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+        "Platform": "Intel® Core™ i7-1185G7 CPU-only",
+        "Model": "resnet-50",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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@@ -10120,7 +10120,7 @@
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@@ -10132,18 +10132,18 @@
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     {
-        "Platform": "Intel® Core™ i7-1185G7 CPU-only",
-        "Model": "bert-base-cased",
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+        "Platform": "Intel® Core™ i7-1185GRE CPU-only",
+        "Model": "resnet-50",
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         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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@@ -10154,7 +10154,7 @@
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@@ -10166,18 +10166,18 @@
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-        "Platform": "Intel® Core™ i7-1185G7 CPU-only",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
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+        "Platform": "Intel® Core™ i7-12700H CPU-only",
+        "Model": "resnet-50",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -10188,7 +10188,7 @@
                 "Precisions": [
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@@ -10200,18 +10200,18 @@
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-        "Model": "efficientdet-d0",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1355U Processor CPU-only",
+        "Model": "resnet-50",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 75.77,
+                        "int8": 203.93,
                         "fp16": "",
-                        "fp32": 41.38,
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@@ -10222,7 +10222,7 @@
                 "Precisions": [
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@@ -10234,18 +10234,18 @@
         }
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     {
-        "Platform": "Intel® Core™ i7-1185G7 CPU-only",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1360P CPU-only",
+        "Model": "resnet-50",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 0.72,
+                        "int8": 298.27,
                         "fp16": "",
-                        "fp32": 0.19,
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                 ],
@@ -10256,7 +10256,7 @@
                 "Precisions": [
                     {
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-                        "int8": 1349.56,
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@@ -10268,18 +10268,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1185G7 CPU-only",
-        "Model": "mobilenet-v2",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-8700T CPU-only",
+        "Model": "resnet-50",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1356.45,
+                        "int8": 122.15,
                         "fp16": "",
-                        "fp32": 514.42,
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@@ -10290,7 +10290,7 @@
                 "Precisions": [
                     {
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-                        "int8": 0.95,
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@@ -10302,18 +10302,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1185G7 CPU-only",
+        "Platform": "Intel® Core™ i9-10900TE CPU-only",
         "Model": "resnet-50",
-        "Checked": true,
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 225.03,
+                        "int8": 152.19,
                         "fp16": "",
-                        "fp32": 61.51,
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@@ -10324,7 +10324,7 @@
                 "Precisions": [
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-                        "int8": 4.94,
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@@ -10336,18 +10336,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1185G7 CPU-only",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
+        "Platform": "Intel® Core™ i9-13900K CPU-only",
+        "Model": "resnet-50",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 766.25,
                         "fp16": "",
-                        "fp32": 1.01,
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@@ -10358,7 +10358,7 @@
                 "Precisions": [
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                         "int4": "",
-                        "int8": "",
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@@ -10370,18 +10370,18 @@
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     {
-        "Platform": "Intel® Core™ i7-1185G7 CPU-only",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Processor N100 CPU-only",
+        "Model": "resnet-50",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 516.94,
+                        "int8": 48.79,
                         "fp16": "",
-                        "fp32": 149.57,
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@@ -10392,7 +10392,7 @@
                 "Precisions": [
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-                        "int8": 2.18,
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@@ -10404,18 +10404,18 @@
         }
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     {
-        "Platform": "Intel® Core™ i7-1185G7 CPU-only",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® W1290P CPU-only",
+        "Model": "resnet-50",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 6.55,
+                        "int8": 245.58,
                         "fp16": "",
-                        "fp32": 1.66,
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@@ -10426,7 +10426,7 @@
                 "Precisions": [
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-                        "int8": 154.77,
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@@ -10438,18 +10438,18 @@
         }
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     {
-        "Platform": "Intel® Core™ i7-1185G7 CPU-only",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® E-2124G CPU-only",
+        "Model": "resnet-50",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 247.12,
+                        "int8": 92.24,
                         "fp16": "",
-                        "fp32": 77.37,
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@@ -10460,7 +10460,7 @@
                 "Precisions": [
                     {
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-                        "int8": 4.23,
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@@ -10472,18 +10472,18 @@
         }
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     {
-        "Platform": "Intel® Core™ i7-1185G7 CPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Gold 5218T CPU-only",
+        "Model": "resnet-50",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 111.26,
+                        "int8": 971.95,
                         "fp16": "",
-                        "fp32": 40.37,
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                 ],
@@ -10494,7 +10494,7 @@
                 "Precisions": [
                     {
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-                        "int8": 10.16,
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@@ -10506,18 +10506,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1185G7 iGPU-only",
-        "Model": "bert-base-cased",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Platinum 8280 CPU-only",
+        "Model": "resnet-50",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 68.19,
-                        "fp16": 53.99,
-                        "fp32": "",
+                        "int8": 2968.31,
+                        "fp16": "",
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                 ],
@@ -10528,7 +10528,7 @@
                 "Precisions": [
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                         "int4": "",
-                        "int8": 16.35,
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@@ -10540,18 +10540,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1185G7 iGPU-only",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Platinum 8380 CPU-only",
+        "Model": "resnet-50",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 8.87,
-                        "fp16": 6.55,
-                        "fp32": "",
+                        "int8": 4948.09,
+                        "fp16": "",
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                 ],
@@ -10562,7 +10562,7 @@
                 "Precisions": [
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-                        "int8": 106.01,
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@@ -10574,19 +10574,19 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1185G7 iGPU-only",
-        "Model": "efficientdet-d0",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Platinum 8480+ CPU-only",
+        "Model": "resnet-50",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 93.8,
-                        "fp16": 73.29,
-                        "fp32": "",
-                        "bf16": ""
+                        "int8": 19506.31,
+                        "fp16": "",
+                        "fp32": 1609.79,
+                        "bf16": 7600.31
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                 ],
                 "Unit": "FPS",
@@ -10596,10 +10596,10 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 17.07,
+                        "int8": 0.99,
                         "fp16": "",
                         "fp32": "",
-                        "bf16": ""
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                 ],
                 "Unit": "ms",
@@ -10608,19 +10608,19 @@
         }
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     {
-        "Platform": "Intel® Core™ i7-1185G7 iGPU-only",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Platinum 8580 CPU-only",
+        "Model": "resnet-50",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 0.83,
-                        "fp16": 0.9,
-                        "fp32": "",
-                        "bf16": ""
+                        "int8": 21749.45,
+                        "fp16": "",
+                        "fp32": 2011.71,
+                        "bf16": 13645.99
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                 ],
                 "Unit": "FPS",
@@ -10630,10 +10630,10 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1120.72,
+                        "int8": 0.98,
                         "fp16": "",
                         "fp32": "",
-                        "bf16": ""
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                 ],
                 "Unit": "ms",
@@ -10642,18 +10642,18 @@
         }
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     {
-        "Platform": "Intel® Core™ i7-1185G7 iGPU-only",
-        "Model": "mobilenet-v2",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Gold 6238L CPU-only",
+        "Model": "resnet-50",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 783.89,
-                        "fp16": 587.88,
-                        "fp32": "",
+                        "int8": 2122.71,
+                        "fp16": "",
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                 ],
@@ -10664,7 +10664,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1.91,
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@@ -10676,18 +10676,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1185G7 iGPU-only",
+        "Platform": "Intel® Xeon® Silver 4316 CPU-only",
         "Model": "resnet-50",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 280.59,
-                        "fp16": 182.59,
-                        "fp32": "",
+                        "int8": 2249.41,
+                        "fp16": "",
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                 ],
@@ -10698,7 +10698,7 @@
                 "Precisions": [
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                         "int4": "",
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@@ -10710,18 +10710,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1185G7 iGPU-only",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 155H CPU-only",
+        "Model": "resnet-50",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 8.3,
-                        "fp16": 4.69,
-                        "fp32": "",
+                        "int8": 392.72,
+                        "fp16": "",
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                         "bf16": ""
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                 ],
@@ -10732,7 +10732,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 116.67,
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@@ -10744,18 +10744,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1185G7 iGPU-only",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU-only",
+        "Model": "resnet-50",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 482.46,
-                        "fp16": 311.99,
-                        "fp32": "",
+                        "int8": 290.74,
+                        "fp16": "",
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                         "bf16": ""
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                 ],
@@ -10766,7 +10766,7 @@
                 "Precisions": [
                     {
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@@ -10778,18 +10778,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1185G7 iGPU-only",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 9 processor 288V CPU-only",
+        "Model": "resnet-50",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 14.79,
-                        "fp16": 7.69,
-                        "fp32": "",
+                        "int8": 318.23,
+                        "fp16": "",
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                         "bf16": ""
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                 ],
@@ -10800,7 +10800,7 @@
                 "Precisions": [
                     {
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-                        "int8": 63.56,
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@@ -10812,17 +10812,17 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1185G7 iGPU-only",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Data Center GPU Flex 170 dGPU",
+        "Model": "resnet-50",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 428.87,
-                        "fp16": 250.36,
+                        "int8": 2318.55,
+                        "fp16": 1375.72,
                         "fp32": "",
                         "bf16": ""
                     }
@@ -10834,7 +10834,7 @@
                 "Precisions": [
                     {
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-                        "int8": 2.92,
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@@ -10846,17 +10846,17 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1185G7 iGPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
-        "Parameters": {
-            "throughput": {
+        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
+        "Model": "resnet-50",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
+        "Parameters": {
+            "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 168.28,
-                        "fp16": 115.07,
+                        "int8": "",
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                         "bf16": ""
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@@ -10868,7 +10868,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 7.55,
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@@ -10880,18 +10880,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1185GRE CPU+iGPU",
-        "Model": "bert-base-cased",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
+        "Model": "resnet-50",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 49.86,
-                        "fp16": "",
-                        "fp32": 26.12,
+                        "int8": 854.09,
+                        "fp16": 539.78,
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                     }
                 ],
@@ -10902,7 +10902,7 @@
                 "Precisions": [
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                         "int4": "",
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@@ -10914,18 +10914,18 @@
         }
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     {
-        "Platform": "Intel® Core™ i7-1185GRE CPU+iGPU",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 155H NPU-only",
+        "Model": "resnet-50",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 5.15,
-                        "fp16": "",
-                        "fp32": 2.68,
+                        "int8": 768.87,
+                        "fp16": 382.94,
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                         "bf16": ""
                     }
                 ],
@@ -10936,7 +10936,7 @@
                 "Precisions": [
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                         "int4": "",
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@@ -10948,18 +10948,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1185GRE CPU+iGPU",
-        "Model": "efficientdet-d0",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 165H NPU-only",
+        "Model": "resnet-50",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 57.89,
-                        "fp16": "",
-                        "fp32": 28.17,
+                        "int8": 772.54,
+                        "fp16": 383.99,
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                 ],
@@ -10970,7 +10970,7 @@
                 "Precisions": [
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                         "int4": "",
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@@ -10982,18 +10982,18 @@
         }
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     {
-        "Platform": "Intel® Core™ i7-1185GRE CPU+iGPU",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 9 processor 288V NPU-only",
+        "Model": "resnet-50",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 0.7,
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@@ -11004,7 +11004,7 @@
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@@ -11016,18 +11016,18 @@
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-        "PlatformType": "Client Platforms (Intel® Core™)",
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         "Parameters": {
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-                        "fp32": 348.67,
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@@ -11038,7 +11038,7 @@
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@@ -11050,18 +11050,18 @@
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-        "Platform": "Intel® Core™ i7-1185GRE CPU+iGPU",
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-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
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-                        "int8": 230.76,
-                        "fp16": "",
-                        "fp32": 85.68,
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@@ -11072,7 +11072,7 @@
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@@ -11084,18 +11084,18 @@
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-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
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+        "Model": "resnet-50",
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+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 5.6,
-                        "fp16": "",
-                        "fp32": 1.76,
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@@ -11106,7 +11106,7 @@
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@@ -11118,18 +11118,18 @@
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-        "Platform": "Intel® Core™ i7-1185GRE CPU+iGPU",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
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         "Parameters": {
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-                        "int8": 448.31,
-                        "fp16": "",
-                        "fp32": 164.3,
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@@ -11140,7 +11140,7 @@
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@@ -11152,18 +11152,18 @@
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-        "Platform": "Intel® Core™ i7-1185GRE CPU+iGPU",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
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         "Parameters": {
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-                        "int8": 7.89,
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-                        "fp32": 2.92,
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@@ -11174,7 +11174,7 @@
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@@ -11186,18 +11186,18 @@
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     {
-        "Platform": "Intel® Core™ i7-1185GRE CPU+iGPU",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
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         "PlatformType": "Client Platforms (Intel® Core™)",
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-                        "int8": 263.65,
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-                        "fp32": 108.88,
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@@ -11220,18 +11220,18 @@
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-        "Platform": "Intel® Core™ i7-1185GRE CPU+iGPU",
-        "Model": "yolo_v8n",
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         "Parameters": {
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-                        "int8": 103.93,
-                        "fp16": "",
-                        "fp32": 53.38,
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@@ -11242,7 +11242,7 @@
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@@ -11254,18 +11254,18 @@
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     {
-        "Platform": "Intel® Core™ i7-1185GRE CPU-only",
-        "Model": "bert-base-cased",
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         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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-                        "fp32": 13.64,
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@@ -11276,7 +11276,7 @@
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@@ -11288,18 +11288,18 @@
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-        "Platform": "Intel® Core™ i7-1185GRE CPU-only",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
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-                        "fp32": 1.2,
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@@ -11310,7 +11310,7 @@
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@@ -11322,18 +11322,18 @@
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-        "Platform": "Intel® Core™ i7-1185GRE CPU-only",
-        "Model": "efficientdet-d0",
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         "Parameters": {
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@@ -11344,7 +11344,7 @@
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@@ -11356,18 +11356,18 @@
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-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Processor N100 iGPU-only",
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+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 0.52,
-                        "fp16": "",
-                        "fp32": 0.13,
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@@ -11378,7 +11378,7 @@
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@@ -11390,18 +11390,18 @@
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-        "Platform": "Intel® Core™ i7-1185GRE CPU-only",
-        "Model": "mobilenet-v2",
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+        "Platform": "Intel® Core™ Ultra 7 processor 155H iGPU-only",
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         "Parameters": {
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-                        "int8": 966.53,
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-                        "fp32": 308.92,
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@@ -11412,7 +11412,7 @@
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@@ -11424,18 +11424,18 @@
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     {
-        "Platform": "Intel® Core™ i7-1185GRE CPU-only",
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-                        "fp16": "",
-                        "fp32": 45.22,
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@@ -11446,7 +11446,7 @@
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@@ -11458,18 +11458,18 @@
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-        "Platform": "Intel® Core™ i7-1185GRE CPU-only",
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-                        "fp32": 0.71,
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@@ -11480,7 +11480,7 @@
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@@ -11492,18 +11492,18 @@
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-        "PlatformType": "Client Platforms (Intel® Core™)",
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+        "Model": "resnet-50",
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+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
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@@ -11514,7 +11514,7 @@
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@@ -11526,18 +11526,18 @@
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-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Atom® X6425E CPU+iGPU",
+        "Model": "resnet-50",
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+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
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@@ -11548,7 +11548,7 @@
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@@ -11560,18 +11560,18 @@
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-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Celeron® 6305E CPU+iGPU",
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+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
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@@ -11582,7 +11582,7 @@
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@@ -11594,18 +11594,18 @@
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@@ -11616,7 +11616,7 @@
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@@ -11628,18 +11628,18 @@
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-        "Model": "bert-base-cased",
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-                        "fp16": 41.69,
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@@ -11650,7 +11650,7 @@
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@@ -11662,18 +11662,18 @@
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-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
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+        "Platform": "Intel® Core™ i7-1185G7 CPU+iGPU",
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         "Parameters": {
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-                        "int8": 5.35,
-                        "fp16": 4.47,
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@@ -11684,7 +11684,7 @@
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@@ -11696,18 +11696,18 @@
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         "Parameters": {
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@@ -11718,7 +11718,7 @@
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@@ -11730,17 +11730,17 @@
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-        "Model": "mask_rcnn_resnet50_atrous_coco",
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+        "Platform": "Intel® Core™ i7-12700H CPU+iGPU",
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         "Parameters": {
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@@ -11752,7 +11752,7 @@
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@@ -11764,18 +11764,18 @@
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@@ -11786,7 +11786,7 @@
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@@ -11798,18 +11798,18 @@
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@@ -11820,7 +11820,7 @@
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@@ -11832,18 +11832,18 @@
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-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Processor N100 CPU+iGPU",
+        "Model": "resnet-50",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 5.52,
-                        "fp16": 2.99,
-                        "fp32": "",
+                        "int8": 112.49,
+                        "fp16": "",
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@@ -11854,7 +11854,7 @@
                 "Precisions": [
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-                        "int8": 172.91,
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@@ -11866,18 +11866,18 @@
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     },
     {
-        "Platform": "Intel® Core™ i7-1185GRE iGPU-only",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 155H CPU+iGPU",
+        "Model": "resnet-50",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 353.15,
-                        "fp16": 223.66,
-                        "fp32": "",
+                        "int8": 1107.14,
+                        "fp16": "",
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@@ -11888,7 +11888,7 @@
                 "Precisions": [
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@@ -11900,18 +11900,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1185GRE iGPU-only",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU+iGPU",
+        "Model": "resnet-50",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 10.08,
-                        "fp16": 5.18,
-                        "fp32": "",
+                        "int8": "",
+                        "fp16": "",
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@@ -11922,7 +11922,7 @@
                 "Precisions": [
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-                        "int8": 93.7,
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@@ -11934,18 +11934,18 @@
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-        "Platform": "Intel® Core™ i7-1185GRE iGPU-only",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Atom® x7425E CPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 307.99,
-                        "fp16": 169.63,
-                        "fp32": "",
+                        "int8": 0.76,
+                        "fp16": "",
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@@ -11956,7 +11956,7 @@
                 "Precisions": [
                     {
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-                        "int8": 3.36,
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@@ -11968,18 +11968,18 @@
         }
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     {
-        "Platform": "Intel® Core™ i7-1185GRE iGPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Atom® X6425E CPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 117.48,
-                        "fp16": 78.18,
-                        "fp32": "",
+                        "int8": 0.33,
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@@ -11990,7 +11990,7 @@
                 "Precisions": [
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-                        "int8": 9.63,
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@@ -12002,18 +12002,18 @@
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-        "Platform": "Intel® Core™ i7-12700H CPU-only",
-        "Model": "bert-base-cased",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Celeron® 6305E CPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 85.3,
+                        "int8": 0.89,
                         "fp16": "",
-                        "fp32": 33.64,
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@@ -12024,7 +12024,7 @@
                 "Precisions": [
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-                        "int8": 16.56,
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@@ -12036,18 +12036,18 @@
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     {
-        "Platform": "Intel® Core™ i7-12700H CPU-only",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ i3-8100 CPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 7.59,
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-                        "fp32": 2.83,
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@@ -12058,7 +12058,7 @@
                 "Precisions": [
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-                        "int8": 171.55,
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@@ -12070,18 +12070,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-12700H CPU-only",
-        "Model": "efficientdet-d0",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-10500TE CPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 112.34,
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                         "fp16": "",
-                        "fp32": 61.11,
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@@ -12092,7 +12092,7 @@
                 "Precisions": [
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-                        "int8": 11.55,
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@@ -12104,18 +12104,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-12700H CPU-only",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1235U Processor CPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1.24,
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                         "fp16": "",
-                        "fp32": 0.35,
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@@ -12126,7 +12126,7 @@
                 "Precisions": [
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                         "int4": "",
-                        "int8": 936.3,
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@@ -12138,18 +12138,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-12700H CPU-only",
-        "Model": "mobilenet-v2",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1335U Processor CPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1968.65,
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                         "fp16": "",
-                        "fp32": 955.03,
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@@ -12160,7 +12160,7 @@
                 "Precisions": [
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-                        "int8": 0.94,
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@@ -12172,18 +12172,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-12700H CPU-only",
-        "Model": "resnet-50",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-13600K CPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 416.36,
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                         "fp16": "",
-                        "fp32": 107.68,
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@@ -12194,7 +12194,7 @@
                 "Precisions": [
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@@ -12206,18 +12206,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-12700H CPU-only",
+        "Platform": "Intel® Core™ i5-8500 CPU-only",
         "Model": "ssd-resnet34-1200",
-        "Checked": true,
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": "",
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-                        "fp32": 1.89,
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@@ -12228,7 +12228,7 @@
                 "Precisions": [
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                         "int4": "",
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@@ -12240,18 +12240,18 @@
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     },
     {
-        "Platform": "Intel® Core™ i7-12700H CPU-only",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185G7 CPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 819.61,
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                         "fp16": "",
-                        "fp32": 286.69,
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@@ -12262,7 +12262,7 @@
                 "Precisions": [
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                         "int4": "",
-                        "int8": 1.69,
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@@ -12274,18 +12274,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-12700H CPU-only",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185GRE CPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 9.61,
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                 ],
@@ -12296,7 +12296,7 @@
                 "Precisions": [
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@@ -12309,17 +12309,17 @@
     },
     {
         "Platform": "Intel® Core™ i7-12700H CPU-only",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 479.88,
+                        "int8": 6.82,
                         "fp16": "",
-                        "fp32": 140.14,
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                 ],
@@ -12330,7 +12330,7 @@
                 "Precisions": [
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@@ -12342,18 +12342,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-12700H CPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1355U Processor CPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 194.08,
+                        "int8": 3.5,
                         "fp16": "",
-                        "fp32": 76.35,
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                 ],
@@ -12364,7 +12364,7 @@
                 "Precisions": [
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                         "int4": "",
-                        "int8": 6.51,
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@@ -12376,17 +12376,17 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-12700H iGPU-only",
-        "Model": "bert-base-cased",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1360P CPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 88.93,
-                        "fp16": 69.66,
+                        "int8": 5.14,
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                         "bf16": ""
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@@ -12398,7 +12398,7 @@
                 "Precisions": [
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-                        "int8": 12.88,
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@@ -12410,18 +12410,18 @@
         }
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-        "Platform": "Intel® Core™ i7-12700H iGPU-only",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-8700T CPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 11.64,
-                        "fp16": 8.35,
-                        "fp32": "",
+                        "int8": 2.0,
+                        "fp16": "",
+                        "fp32": 1.06,
                         "bf16": ""
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                 ],
@@ -12432,7 +12432,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 87.57,
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@@ -12444,18 +12444,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-12700H iGPU-only",
-        "Model": "efficientdet-d0",
-        "Checked": true,
+        "Platform": "Intel® Core™ i9-10900TE CPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 127.94,
-                        "fp16": 97.66,
-                        "fp32": "",
+                        "int8": 2.57,
+                        "fp16": "",
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                         "bf16": ""
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                 ],
@@ -12466,7 +12466,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 12.7,
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@@ -12478,18 +12478,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-12700H iGPU-only",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ i9-13900K CPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1.03,
-                        "fp16": 1.14,
-                        "fp32": "",
+                        "int8": 12.97,
+                        "fp16": "",
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                         "bf16": ""
                     }
                 ],
@@ -12500,7 +12500,7 @@
                 "Precisions": [
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-                        "int8": 972.95,
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@@ -12512,18 +12512,18 @@
         }
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     {
-        "Platform": "Intel® Core™ i7-12700H iGPU-only",
-        "Model": "mobilenet-v2",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Processor N100 CPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1275.12,
-                        "fp16": 910.23,
-                        "fp32": "",
+                        "int8": 0.82,
+                        "fp16": "",
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                     }
                 ],
@@ -12534,7 +12534,7 @@
                 "Precisions": [
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                         "int4": "",
-                        "int8": 1.09,
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@@ -12546,18 +12546,18 @@
         }
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     {
-        "Platform": "Intel® Core™ i7-12700H iGPU-only",
-        "Model": "resnet-50",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® W1290P CPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 386.47,
-                        "fp16": 228.04,
-                        "fp32": "",
+                        "int8": 4.34,
+                        "fp16": "",
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                         "bf16": ""
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                 ],
@@ -12568,7 +12568,7 @@
                 "Precisions": [
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-                        "int8": 3.24,
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@@ -12580,18 +12580,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-12700H iGPU-only",
+        "Platform": "Intel® Xeon® E-2124G CPU-only",
         "Model": "ssd-resnet34-1200",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 10.44,
-                        "fp16": 6.09,
-                        "fp32": "",
+                        "int8": 1.59,
+                        "fp16": "",
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                         "bf16": ""
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                 ],
@@ -12602,7 +12602,7 @@
                 "Precisions": [
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-                        "int8": 100.09,
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@@ -12614,18 +12614,18 @@
         }
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     {
-        "Platform": "Intel® Core™ i7-12700H iGPU-only",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Gold 5218T CPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 739.14,
-                        "fp16": 415.33,
-                        "fp32": "",
+                        "int8": 17.67,
+                        "fp16": "",
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                     }
                 ],
@@ -12636,7 +12636,7 @@
                 "Precisions": [
                     {
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-                        "int8": 1.86,
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@@ -12648,18 +12648,18 @@
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-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Platinum 8280 CPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 18.34,
-                        "fp16": 9.7,
-                        "fp32": "",
+                        "int8": 58.24,
+                        "fp16": "",
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@@ -12670,7 +12670,7 @@
                 "Precisions": [
                     {
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-                        "int8": 54.73,
+                        "int8": 36.32,
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@@ -12682,18 +12682,18 @@
         }
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     {
-        "Platform": "Intel® Core™ i7-12700H iGPU-only",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Platinum 8380 CPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 591.82,
-                        "fp16": 319.3,
-                        "fp32": "",
+                        "int8": 85.63,
+                        "fp16": "",
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                 ],
@@ -12704,7 +12704,7 @@
                 "Precisions": [
                     {
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-                        "int8": 1.97,
+                        "int8": 25.38,
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@@ -12716,19 +12716,19 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-12700H iGPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Platinum 8480+ CPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 218.11,
-                        "fp16": 148.84,
-                        "fp32": "",
-                        "bf16": ""
+                        "int8": 438.33,
+                        "fp16": "",
+                        "fp32": 30.86,
+                        "bf16": 213.33
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                 ],
                 "Unit": "FPS",
@@ -12738,10 +12738,10 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 5.54,
+                        "int8": 7.94,
                         "fp16": "",
                         "fp32": "",
-                        "bf16": ""
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@@ -12750,19 +12750,19 @@
         }
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     {
-        "Platform": "Intel® Core™ i7-1355U Processor CPU+iGPU",
-        "Model": "bert-base-cased",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Platinum 8580 CPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 62.94,
+                        "int8": 512.76,
                         "fp16": "",
-                        "fp32": 33.05,
-                        "bf16": ""
+                        "fp32": 35.43,
+                        "bf16": 276.38
                     }
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                 "Unit": "FPS",
@@ -12772,10 +12772,10 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 7.12,
                         "fp16": "",
                         "fp32": "",
-                        "bf16": ""
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@@ -12784,18 +12784,18 @@
         }
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     {
-        "Platform": "Intel® Core™ i7-1355U Processor CPU+iGPU",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Gold 6238L CPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 6.62,
+                        "int8": 41.98,
                         "fp16": "",
-                        "fp32": 3.23,
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@@ -12806,7 +12806,7 @@
                 "Precisions": [
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                         "int4": "",
-                        "int8": "",
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@@ -12818,18 +12818,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1355U Processor CPU+iGPU",
-        "Model": "efficientdet-d0",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Silver 4316 CPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 89.9,
+                        "int8": 42.23,
                         "fp16": "",
-                        "fp32": 60.09,
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@@ -12840,7 +12840,7 @@
                 "Precisions": [
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                         "int4": "",
-                        "int8": "",
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@@ -12852,18 +12852,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1355U Processor CPU+iGPU",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 155H CPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 0.88,
+                        "int8": 6.45,
                         "fp16": "",
-                        "fp32": 0.45,
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@@ -12874,7 +12874,7 @@
                 "Precisions": [
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@@ -12886,18 +12886,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1355U Processor CPU+iGPU",
-        "Model": "mobilenet-v2",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
-        "Parameters": {
+        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1230.71,
+                        "int8": 4.88,
                         "fp16": "",
-                        "fp32": 660.1,
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@@ -12908,7 +12908,7 @@
                 "Precisions": [
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                         "int4": "",
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@@ -12920,18 +12920,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1355U Processor CPU+iGPU",
-        "Model": "resnet-50",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 9 processor 288V CPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 293.95,
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                         "fp16": "",
-                        "fp32": 112.37,
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@@ -12942,7 +12942,7 @@
                 "Precisions": [
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                         "int4": "",
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@@ -12954,18 +12954,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1355U Processor CPU+iGPU",
+        "Platform": "Intel® Data Center GPU Flex 170 dGPU",
         "Model": "ssd-resnet34-1200",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 7.16,
-                        "fp16": "",
-                        "fp32": 2.19,
+                        "int8": 140.65,
+                        "fp16": 92.11,
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@@ -12976,7 +12976,7 @@
                 "Precisions": [
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@@ -12988,18 +12988,18 @@
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     {
-        "Platform": "Intel® Core™ i7-1355U Processor CPU+iGPU",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 561.31,
+                        "int8": "",
                         "fp16": "",
-                        "fp32": 236.99,
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@@ -13010,7 +13010,7 @@
                 "Precisions": [
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@@ -13022,18 +13022,18 @@
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     {
-        "Platform": "Intel® Core™ i7-1355U Processor CPU+iGPU",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 9.87,
-                        "fp16": "",
-                        "fp32": 3.67,
+                        "int8": 35.4,
+                        "fp16": 17.45,
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@@ -13044,7 +13044,7 @@
                 "Precisions": [
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@@ -13056,18 +13056,18 @@
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     {
-        "Platform": "Intel® Core™ i7-1355U Processor CPU+iGPU",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Atom® x7425E iGPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 349.32,
-                        "fp16": "",
-                        "fp32": 135.28,
+                        "int8": 2.16,
+                        "fp16": 1.32,
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@@ -13078,7 +13078,7 @@
                 "Precisions": [
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@@ -13090,18 +13090,18 @@
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     {
-        "Platform": "Intel® Core™ i7-1355U Processor CPU+iGPU",
-        "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Atom® X6425E iGPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 156.93,
-                        "fp16": "",
-                        "fp32": 74.81,
+                        "int8": 1.16,
+                        "fp16": 1.16,
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                         "bf16": ""
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                 ],
@@ -13112,7 +13112,7 @@
                 "Precisions": [
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                         "int4": "",
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@@ -13124,18 +13124,18 @@
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     },
     {
-        "Platform": "Intel® Core™ i7-1355U Processor CPU-only",
-        "Model": "bert-base-cased",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Celeron® 6305E iGPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 44.98,
-                        "fp16": "",
-                        "fp32": 17.99,
+                        "int8": 5.09,
+                        "fp16": 2.78,
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                         "bf16": ""
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                 ],
@@ -13146,7 +13146,7 @@
                 "Precisions": [
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-                        "int8": 37.43,
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@@ -13158,18 +13158,18 @@
         }
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     {
-        "Platform": "Intel® Core™ i7-1355U Processor CPU-only",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1235U Processor iGPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 4.07,
-                        "fp16": "",
-                        "fp32": 1.55,
+                        "int8": 5.8,
+                        "fp16": 2.81,
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                         "bf16": ""
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                 ],
@@ -13180,7 +13180,7 @@
                 "Precisions": [
                     {
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-                        "int8": 357.9,
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@@ -13192,18 +13192,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1355U Processor CPU-only",
-        "Model": "efficientdet-d0",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1335U Processor iGPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 62.65,
-                        "fp16": "",
-                        "fp32": 39.85,
+                        "int8": 5.92,
+                        "fp16": 2.89,
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                 ],
@@ -13214,7 +13214,7 @@
                 "Precisions": [
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-                        "int8": 26.75,
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@@ -13226,18 +13226,18 @@
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     {
-        "Platform": "Intel® Core™ i7-1355U Processor CPU-only",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185G7 iGPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 0.64,
-                        "fp16": "",
-                        "fp32": 0.18,
+                        "int8": 8.32,
+                        "fp16": 4.74,
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                 ],
@@ -13248,7 +13248,7 @@
                 "Precisions": [
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-                        "int8": 1903.03,
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@@ -13260,18 +13260,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1355U Processor CPU-only",
-        "Model": "mobilenet-v2",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185GRE iGPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1044.1,
-                        "fp16": "",
-                        "fp32": 514.83,
+                        "int8": 5.43,
+                        "fp16": 3.17,
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                 ],
@@ -13282,7 +13282,7 @@
                 "Precisions": [
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                         "int4": "",
-                        "int8": 1.6,
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@@ -13294,18 +13294,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1355U Processor CPU-only",
-        "Model": "resnet-50",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-12700H iGPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 205.61,
-                        "fp16": "",
-                        "fp32": 59.59,
+                        "int8": 10.46,
+                        "fp16": 6.1,
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                 ],
@@ -13316,7 +13316,7 @@
                 "Precisions": [
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-                        "int8": 8.92,
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@@ -13328,18 +13328,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1355U Processor CPU-only",
+        "Platform": "Intel® Core™ i7-1355U Processor iGPU-only",
         "Model": "ssd-resnet34-1200",
-        "Checked": true,
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
-                        "fp16": "",
-                        "fp32": 1.03,
+                        "int8": 7.12,
+                        "fp16": 4.0,
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                 ],
@@ -13350,7 +13350,7 @@
                 "Precisions": [
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@@ -13362,18 +13362,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1355U Processor CPU-only",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1360P iGPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 427.09,
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-                        "fp32": 154.82,
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                 ],
@@ -13384,7 +13384,7 @@
                 "Precisions": [
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@@ -13396,18 +13396,18 @@
         }
     },
     {
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-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Processor N100 iGPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 5.17,
-                        "fp16": "",
-                        "fp32": 0.98,
+                        "int8": 1.62,
+                        "fp16": 1.01,
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                 ],
@@ -13418,7 +13418,7 @@
                 "Precisions": [
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-                        "int8": 289.96,
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@@ -13430,18 +13430,18 @@
         }
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     {
-        "Platform": "Intel® Core™ i7-1355U Processor CPU-only",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 155H iGPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 232.99,
-                        "fp16": "",
-                        "fp32": 77.57,
+                        "int8": 21.37,
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@@ -13452,7 +13452,7 @@
                 "Precisions": [
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@@ -13464,18 +13464,18 @@
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     {
-        "Platform": "Intel® Core™ i7-1355U Processor CPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 165H iGPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 102.69,
-                        "fp16": "",
-                        "fp32": 41.19,
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+                        "fp16": 10.09,
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@@ -13486,7 +13486,7 @@
                 "Precisions": [
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@@ -13498,17 +13498,17 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1355U Processor iGPU-only",
-        "Model": "bert-base-cased",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 9 processor 288V iGPU-only",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 66.57,
-                        "fp16": 53.45,
+                        "int8": 65.86,
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@@ -13520,7 +13520,7 @@
                 "Precisions": [
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-                        "int8": 14.4,
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@@ -13532,18 +13532,18 @@
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-        "Platform": "Intel® Core™ i7-1355U Processor iGPU-only",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Atom® x7425E CPU+iGPU",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 8.22,
-                        "fp16": 5.94,
-                        "fp32": "",
+                        "int8": 2.53,
+                        "fp16": "",
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@@ -13554,7 +13554,7 @@
                 "Precisions": [
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@@ -13566,18 +13566,18 @@
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-        "Platform": "Intel® Core™ i7-1355U Processor iGPU-only",
-        "Model": "efficientdet-d0",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Atom® X6425E CPU+iGPU",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 101.06,
-                        "fp16": 75.39,
-                        "fp32": "",
+                        "int8": 1.25,
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@@ -13588,7 +13588,7 @@
                 "Precisions": [
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@@ -13600,18 +13600,18 @@
         }
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-        "Platform": "Intel® Core™ i7-1355U Processor iGPU-only",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Celeron® 6305E CPU+iGPU",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 0.75,
-                        "fp16": 0.79,
-                        "fp32": "",
+                        "int8": 5.37,
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@@ -13622,7 +13622,7 @@
                 "Precisions": [
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-                        "int8": 1176.92,
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@@ -13634,18 +13634,18 @@
         }
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     {
-        "Platform": "Intel® Core™ i7-1355U Processor iGPU-only",
-        "Model": "mobilenet-v2",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1235U Processor CPU+iGPU",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 892.66,
-                        "fp16": 630.28,
-                        "fp32": "",
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@@ -13656,7 +13656,7 @@
                 "Precisions": [
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@@ -13668,18 +13668,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1355U Processor iGPU-only",
-        "Model": "resnet-50",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1335U Processor CPU+iGPU",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 279.02,
-                        "fp16": 168.18,
-                        "fp32": "",
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@@ -13690,7 +13690,7 @@
                 "Precisions": [
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@@ -13702,18 +13702,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1355U Processor iGPU-only",
+        "Platform": "Intel® Core™ i7-1185G7 CPU+iGPU",
         "Model": "ssd-resnet34-1200",
-        "Checked": true,
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 7.22,
-                        "fp16": 4.03,
-                        "fp32": "",
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@@ -13724,7 +13724,7 @@
                 "Precisions": [
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-                        "int8": 126.31,
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@@ -13736,18 +13736,18 @@
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     {
-        "Platform": "Intel® Core™ i7-1355U Processor iGPU-only",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185GRE CPU+iGPU",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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-                        "int8": 495.37,
-                        "fp16": 302.64,
-                        "fp32": "",
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@@ -13758,7 +13758,7 @@
                 "Precisions": [
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@@ -13770,17 +13770,17 @@
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     },
     {
-        "Platform": "Intel® Core™ i7-1355U Processor iGPU-only",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-12700H CPU+iGPU",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 12.93,
-                        "fp16": 6.58,
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@@ -13792,7 +13792,7 @@
                 "Precisions": [
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@@ -13804,18 +13804,18 @@
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     {
-        "Platform": "Intel® Core™ i7-1355U Processor iGPU-only",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1355U Processor CPU+iGPU",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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-                        "int8": 411.85,
-                        "fp16": 219.54,
-                        "fp32": "",
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@@ -13826,7 +13826,7 @@
                 "Precisions": [
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@@ -13838,18 +13838,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1355U Processor iGPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1360P CPU+iGPU",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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-                        "int8": 167.77,
-                        "fp16": 110.14,
-                        "fp32": "",
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@@ -13860,7 +13860,7 @@
                 "Precisions": [
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@@ -13872,18 +13872,18 @@
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-        "Model": "bert-base-cased",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Processor N100 CPU+iGPU",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 98.23,
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-                        "fp32": 56.22,
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@@ -13906,18 +13906,18 @@
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-        "Platform": "Intel® Core™ i7-1360P CPU+iGPU",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 155H CPU+iGPU",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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-                        "int8": 10.19,
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@@ -13940,18 +13940,18 @@
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     },
     {
-        "Platform": "Intel® Core™ i7-1360P CPU+iGPU",
-        "Model": "efficientdet-d0",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU+iGPU",
+        "Model": "ssd-resnet34-1200",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 108.41,
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-                        "fp32": 57.85,
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@@ -13974,18 +13974,18 @@
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     {
-        "Platform": "Intel® Core™ i7-1360P CPU+iGPU",
-        "Model": "mobilenet-v2",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Atom® x7425E CPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1608.64,
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-                        "fp32": 849.7,
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@@ -13996,7 +13996,7 @@
                 "Precisions": [
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@@ -14008,18 +14008,18 @@
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-        "Model": "resnet-50",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Atom® X6425E CPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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-                        "int8": 453.79,
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-                        "fp32": 181.36,
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@@ -14030,7 +14030,7 @@
                 "Precisions": [
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@@ -14042,18 +14042,18 @@
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-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Celeron® 6305E CPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 10.66,
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@@ -14064,7 +14064,7 @@
                 "Precisions": [
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@@ -14076,18 +14076,18 @@
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     },
     {
-        "Platform": "Intel® Core™ i7-1360P CPU+iGPU",
+        "Platform": "Intel® Core™ i3-8100 CPU-only",
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+        "Checked": "true",
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         "Parameters": {
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-                        "int8": 811.05,
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@@ -14098,7 +14098,7 @@
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@@ -14110,18 +14110,18 @@
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     {
-        "Platform": "Intel® Core™ i7-1360P CPU+iGPU",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-10500TE CPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
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         "Parameters": {
             "throughput": {
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-                        "int8": 20.73,
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@@ -14132,7 +14132,7 @@
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@@ -14144,18 +14144,18 @@
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-        "Platform": "Intel® Core™ i7-1360P CPU+iGPU",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1235U Processor CPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -14166,7 +14166,7 @@
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@@ -14178,18 +14178,18 @@
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-        "Platform": "Intel® Core™ i7-1360P CPU+iGPU",
-        "Model": "yolo_v8n",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1335U Processor CPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 227.41,
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@@ -14200,7 +14200,7 @@
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@@ -14212,18 +14212,18 @@
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     {
-        "Platform": "Intel® Core™ i7-1360P CPU-only",
-        "Model": "bert-base-cased",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-13600K CPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 64.14,
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@@ -14234,7 +14234,7 @@
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@@ -14246,18 +14246,18 @@
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     {
-        "Platform": "Intel® Core™ i7-1360P CPU-only",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-8500 CPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
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         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 5.9,
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@@ -14268,7 +14268,7 @@
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@@ -14280,18 +14280,18 @@
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-        "Platform": "Intel® Core™ i7-1360P CPU-only",
-        "Model": "efficientdet-d0",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185G7 CPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
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-                        "int8": 86.28,
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-                        "fp32": 44.24,
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@@ -14302,7 +14302,7 @@
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@@ -14314,18 +14314,18 @@
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     {
-        "Platform": "Intel® Core™ i7-1360P CPU-only",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185GRE CPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 0.98,
+                        "int8": 385.05,
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-                        "fp32": 0.27,
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@@ -14336,7 +14336,7 @@
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-                        "int8": 1392.43,
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@@ -14348,18 +14348,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1360P CPU-only",
-        "Model": "mobilenet-v2",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-12700H CPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1356.83,
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-                        "fp32": 719.36,
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@@ -14370,7 +14370,7 @@
                 "Precisions": [
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@@ -14382,18 +14382,18 @@
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     },
     {
-        "Platform": "Intel® Core™ i7-1360P CPU-only",
-        "Model": "resnet-50",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1355U Processor CPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 310.2,
+                        "int8": 423.45,
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-                        "fp32": 81.12,
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@@ -14404,7 +14404,7 @@
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@@ -14417,8 +14417,8 @@
     },
     {
         "Platform": "Intel® Core™ i7-1360P CPU-only",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
+        "Model": "ssd_mobilenet_v1_coco",
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         "Parameters": {
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@@ -14427,7 +14427,7 @@
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@@ -14438,7 +14438,7 @@
                 "Precisions": [
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@@ -14450,18 +14450,18 @@
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     },
     {
-        "Platform": "Intel® Core™ i7-1360P CPU-only",
+        "Platform": "Intel® Core™ i7-8700T CPU-only",
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-        "Checked": true,
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 611.66,
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-                        "fp32": 219.37,
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@@ -14472,7 +14472,7 @@
                 "Precisions": [
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@@ -14484,18 +14484,18 @@
         }
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     {
-        "Platform": "Intel® Core™ i7-1360P CPU-only",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ i9-10900TE CPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 7.44,
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-                        "fp32": 1.55,
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@@ -14506,7 +14506,7 @@
                 "Precisions": [
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-                        "int8": 183.66,
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@@ -14518,18 +14518,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1360P CPU-only",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
+        "Platform": "Intel® Core™ i9-13900K CPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 354.22,
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-                        "fp32": 104.25,
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@@ -14540,7 +14540,7 @@
                 "Precisions": [
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-                        "int8": 4.36,
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@@ -14552,18 +14552,18 @@
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-        "Platform": "Intel® Core™ i7-1360P CPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Processor N100 CPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 144.91,
+                        "int8": 105.81,
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-                        "fp32": 56.17,
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@@ -14574,7 +14574,7 @@
                 "Precisions": [
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@@ -14586,18 +14586,18 @@
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-        "Platform": "Intel® Core™ i7-1360P iGPU-only",
-        "Model": "bert-base-cased",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® W1290P CPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 92.26,
-                        "fp16": 71.67,
-                        "fp32": "",
+                        "int8": 575.93,
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@@ -14608,7 +14608,7 @@
                 "Precisions": [
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@@ -14620,18 +14620,18 @@
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-        "Platform": "Intel® Core™ i7-1360P iGPU-only",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® E-2124G CPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 11.25,
-                        "fp16": 8.61,
-                        "fp32": "",
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@@ -14642,7 +14642,7 @@
                 "Precisions": [
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@@ -14654,18 +14654,18 @@
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-        "Platform": "Intel® Core™ i7-1360P iGPU-only",
-        "Model": "chatglm2-6b",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Gold 5218T CPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": "",
+                        "int8": 2068.2,
                         "fp16": "",
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@@ -14675,8 +14675,8 @@
             "latency": {
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@@ -14688,18 +14688,18 @@
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-        "Model": "efficientdet-d0",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Platinum 8280 CPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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-                        "int8": 109.11,
-                        "fp16": 84.61,
-                        "fp32": "",
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@@ -14710,7 +14710,7 @@
                 "Precisions": [
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@@ -14722,10 +14722,10 @@
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-        "Model": "falcon-7b-instruct",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Platinum 8380 CPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
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         "Parameters": {
             "throughput": {
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@@ -14733,7 +14733,7 @@
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@@ -14743,8 +14743,8 @@
             "latency": {
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@@ -14756,19 +14756,19 @@
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-        "Platform": "Intel® Core™ i7-1360P iGPU-only",
-        "Model": "llama-2-7b-chat-hf",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Platinum 8480+ CPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": "",
+                        "int8": 23599.57,
                         "fp16": "",
-                        "fp32": "",
-                        "bf16": ""
+                        "fp32": 3405.56,
+                        "bf16": 12205.1
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@@ -14777,11 +14777,11 @@
             "latency": {
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-                        "int8": "",
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@@ -14790,10 +14790,10 @@
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-        "Model": "llama-3-8b",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Platinum 8580 CPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
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@@ -14801,8 +14801,8 @@
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                         "fp16": "",
-                        "fp32": "",
-                        "bf16": ""
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@@ -14811,11 +14811,11 @@
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-                        "int8": "",
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@@ -14824,18 +14824,18 @@
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-        "Model": "mistral-7b-v0.1",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Gold 6238L CPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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-                        "int8": "",
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@@ -14845,8 +14845,8 @@
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@@ -14858,18 +14858,18 @@
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-        "Model": "mobilenet-v2",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Xeon® Silver 4316 CPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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-                        "int8": 1168.27,
-                        "fp16": 809.9,
-                        "fp32": "",
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@@ -14880,7 +14880,7 @@
                 "Precisions": [
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@@ -14892,18 +14892,18 @@
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     {
-        "Platform": "Intel® Core™ i7-1360P iGPU-only",
-        "Model": "phi-3-mini-4k-instruct",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 155H CPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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-                        "int8": "",
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@@ -14913,8 +14913,8 @@
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-                        "int8": 163.2,
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@@ -14926,18 +14926,18 @@
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     {
-        "Platform": "Intel® Core™ i7-1360P iGPU-only",
-        "Model": "resnet-50",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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-                        "int8": 367.05,
-                        "fp16": 230.75,
-                        "fp32": "",
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@@ -14948,7 +14948,7 @@
                 "Precisions": [
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@@ -14960,18 +14960,18 @@
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     {
-        "Platform": "Intel® Core™ i7-1360P iGPU-only",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 9 processor 288V CPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
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-                        "int8": 10.74,
-                        "fp16": 6.21,
-                        "fp32": "",
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@@ -14982,7 +14982,7 @@
                 "Precisions": [
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-                        "int8": 94.66,
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@@ -14994,17 +14994,17 @@
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     },
     {
-        "Platform": "Intel® Core™ i7-1360P iGPU-only",
+        "Platform": "Intel® Data Center GPU Flex 170 dGPU",
         "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
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                     {
                         "int4": "",
-                        "int8": 659.36,
-                        "fp16": 396.71,
+                        "int8": 2300.75,
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@@ -15016,7 +15016,7 @@
                 "Precisions": [
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@@ -15028,12 +15028,12 @@
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-        "Model": "stable-diffusion-v2-1",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
-        "Parameters": {
-            "throughput": {
+        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
+        "Parameters": {
+            "throughput": {
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@@ -15050,8 +15050,8 @@
                 "Precisions": [
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-                        "fp16": 18.94,
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@@ -15062,17 +15062,17 @@
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-        "Model": "stable-diffusion-v2-1",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
-                        "fp16": "",
+                        "int8": 1024.85,
+                        "fp16": 777.67,
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@@ -15084,8 +15084,8 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 19.99,
-                        "fp16": 19.03,
+                        "int8": 1.33,
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@@ -15096,17 +15096,17 @@
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     {
-        "Platform": "Intel® Core™ i7-1360P iGPU-only",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 155H NPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 19.42,
-                        "fp16": 10.25,
+                        "int8": 704.76,
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@@ -15118,7 +15118,7 @@
                 "Precisions": [
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@@ -15130,17 +15130,17 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1360P iGPU-only",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 165H NPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 591.87,
-                        "fp16": 321.77,
+                        "int8": 705.61,
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                         "bf16": ""
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@@ -15152,7 +15152,7 @@
                 "Precisions": [
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-                        "int8": 2.22,
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@@ -15164,17 +15164,17 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-1360P iGPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 9 processor 288V NPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 208.72,
-                        "fp16": 145.7,
+                        "int8": 229.58,
+                        "fp16": 198.86,
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@@ -15186,7 +15186,7 @@
                 "Precisions": [
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-                        "int8": 5.84,
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@@ -15198,18 +15198,18 @@
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-        "Platform": "Intel® Core™ i7-8700T CPU-only",
-        "Model": "bert-base-cased",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Atom® x7425E iGPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 27.23,
-                        "fp16": "",
-                        "fp32": 18.28,
+                        "int8": 210.98,
+                        "fp16": 137.08,
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@@ -15220,7 +15220,7 @@
                 "Precisions": [
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-                        "int8": 43.5,
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@@ -15232,18 +15232,18 @@
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-        "Platform": "Intel® Core™ i7-8700T CPU-only",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Atom® X6425E iGPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 2.5,
-                        "fp16": "",
-                        "fp32": 1.54,
+                        "int8": 92.82,
+                        "fp16": 95.83,
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@@ -15254,7 +15254,7 @@
                 "Precisions": [
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-                        "int8": 464.53,
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@@ -15266,18 +15266,18 @@
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-        "Platform": "Intel® Core™ i7-8700T CPU-only",
-        "Model": "efficientdet-d0",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Celeron® 6305E iGPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 52.58,
-                        "fp16": "",
-                        "fp32": 37.77,
+                        "int8": 396.49,
+                        "fp16": 222.23,
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@@ -15288,7 +15288,7 @@
                 "Precisions": [
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-                        "int8": 23.64,
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@@ -15300,18 +15300,18 @@
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     },
     {
-        "Platform": "Intel® Core™ i7-8700T CPU-only",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1235U Processor iGPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 0.37,
-                        "fp16": "",
-                        "fp32": 0.15,
+                        "int8": 392.98,
+                        "fp16": 229.59,
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@@ -15322,7 +15322,7 @@
                 "Precisions": [
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-                        "int8": 2909.82,
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@@ -15334,18 +15334,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-8700T CPU-only",
-        "Model": "mobilenet-v2",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1335U Processor iGPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 738.24,
-                        "fp16": "",
-                        "fp32": 485.65,
+                        "int8": 405.21,
+                        "fp16": 238.92,
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                 ],
@@ -15356,7 +15356,7 @@
                 "Precisions": [
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-                        "int8": 1.89,
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@@ -15368,18 +15368,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-8700T CPU-only",
-        "Model": "resnet-50",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185G7 iGPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 122.21,
-                        "fp16": "",
-                        "fp32": 60.73,
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@@ -15390,7 +15390,7 @@
                 "Precisions": [
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@@ -15402,18 +15402,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-8700T CPU-only",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185GRE iGPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": "",
-                        "fp16": "",
-                        "fp32": 1.05,
+                        "int8": 360.04,
+                        "fp16": 225.51,
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@@ -15424,7 +15424,7 @@
                 "Precisions": [
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@@ -15436,18 +15436,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-8700T CPU-only",
+        "Platform": "Intel® Core™ i7-12700H iGPU-only",
         "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 277.07,
-                        "fp16": "",
-                        "fp32": 152.76,
+                        "int8": 748.06,
+                        "fp16": 416.28,
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                 ],
@@ -15458,7 +15458,7 @@
                 "Precisions": [
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@@ -15470,18 +15470,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i7-8700T CPU-only",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1355U Processor iGPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 3.07,
-                        "fp16": "",
-                        "fp32": 1.65,
+                        "int8": 488.8,
+                        "fp16": 302.47,
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@@ -15492,7 +15492,7 @@
                 "Precisions": [
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-                        "int8": 366.29,
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@@ -15504,18 +15504,18 @@
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     },
     {
-        "Platform": "Intel® Core™ i7-8700T CPU-only",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1360P iGPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 137.09,
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@@ -15526,7 +15526,7 @@
                 "Precisions": [
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@@ -15538,18 +15538,18 @@
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-        "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Processor N100 iGPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 70.85,
-                        "fp16": "",
-                        "fp32": 40.95,
+                        "int8": 164.35,
+                        "fp16": 106.86,
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                 ],
@@ -15560,7 +15560,7 @@
                 "Precisions": [
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@@ -15572,18 +15572,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i9-10900TE CPU-only",
-        "Model": "bert-base-cased",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 155H iGPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 32.92,
-                        "fp16": "",
-                        "fp32": 21.43,
+                        "int8": 979.63,
+                        "fp16": 802.16,
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                 ],
@@ -15594,7 +15594,7 @@
                 "Precisions": [
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@@ -15606,18 +15606,18 @@
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     {
-        "Platform": "Intel® Core™ i9-10900TE CPU-only",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 165H iGPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 3.15,
-                        "fp16": "",
-                        "fp32": 1.9,
+                        "int8": 974.05,
+                        "fp16": 701.6,
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@@ -15628,7 +15628,7 @@
                 "Precisions": [
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-                        "int8": 389.61,
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@@ -15640,18 +15640,18 @@
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     },
     {
-        "Platform": "Intel® Core™ i9-10900TE CPU-only",
-        "Model": "efficientdet-d0",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 9 processor 288V iGPU-only",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 64.89,
-                        "fp16": "",
-                        "fp32": 41.07,
+                        "int8": 985.08,
+                        "fp16": 990.93,
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                 ],
@@ -15662,7 +15662,7 @@
                 "Precisions": [
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@@ -15674,18 +15674,18 @@
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-        "Platform": "Intel® Core™ i9-10900TE CPU-only",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Atom® x7425E CPU+iGPU",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 0.47,
+                        "int8": 234.86,
                         "fp16": "",
-                        "fp32": 0.17,
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                 ],
@@ -15696,7 +15696,7 @@
                 "Precisions": [
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-                        "int8": 2244.49,
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@@ -15708,18 +15708,18 @@
         }
     },
     {
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-        "Model": "mobilenet-v2",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Atom® X6425E CPU+iGPU",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 906.7,
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                         "fp16": "",
-                        "fp32": 572.68,
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                 ],
@@ -15730,7 +15730,7 @@
                 "Precisions": [
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@@ -15742,18 +15742,18 @@
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-        "Model": "resnet-50",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Celeron® 6305E CPU+iGPU",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 153.1,
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-                        "fp32": 71.18,
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                 ],
@@ -15764,7 +15764,7 @@
                 "Precisions": [
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@@ -15776,9 +15776,9 @@
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     },
     {
-        "Platform": "Intel® Core™ i9-10900TE CPU-only",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1235U Processor CPU+iGPU",
+        "Model": "ssd_mobilenet_v1_coco",
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         "Parameters": {
             "throughput": {
@@ -15787,7 +15787,7 @@
                         "int4": "",
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-                        "fp32": 1.27,
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@@ -15810,18 +15810,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i9-10900TE CPU-only",
+        "Platform": "Intel® Core™ i5-1335U Processor CPU+iGPU",
         "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 352.77,
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-                        "fp32": 183.36,
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@@ -15832,7 +15832,7 @@
                 "Precisions": [
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@@ -15844,18 +15844,18 @@
         }
     },
     {
-        "Platform": "Intel® Core™ i9-10900TE CPU-only",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185G7 CPU+iGPU",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 3.79,
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@@ -15866,7 +15866,7 @@
                 "Precisions": [
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@@ -15878,18 +15878,18 @@
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-        "Model": "yolo_v3_tiny",
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+        "Platform": "Intel® Core™ i7-1185GRE CPU+iGPU",
+        "Model": "ssd_mobilenet_v1_coco",
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         "Parameters": {
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@@ -15900,7 +15900,7 @@
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@@ -15912,18 +15912,18 @@
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     {
-        "Platform": "Intel® Core™ i9-10900TE CPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-12700H CPU+iGPU",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
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         "Parameters": {
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-                        "int8": 89.62,
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@@ -15934,7 +15934,7 @@
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@@ -15946,18 +15946,18 @@
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     {
-        "Platform": "Intel® Core™ i9-13900K CPU-only",
-        "Model": "bert-base-cased",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1355U Processor CPU+iGPU",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 168.58,
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@@ -15968,7 +15968,7 @@
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@@ -15980,18 +15980,18 @@
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-        "Platform": "Intel® Core™ i9-13900K CPU-only",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1360P CPU+iGPU",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -16002,7 +16002,7 @@
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@@ -16014,18 +16014,18 @@
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-        "Model": "chatglm2-6b",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Processor N100 CPU+iGPU",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
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@@ -16035,9 +16035,9 @@
             "latency": {
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-                        "int8": 91.17,
-                        "fp16": 177.71,
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@@ -16048,18 +16048,18 @@
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-        "Platform": "Intel® Core™ i9-13900K CPU-only",
-        "Model": "efficientdet-d0",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 155H CPU+iGPU",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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-                        "int8": 219.68,
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@@ -16070,7 +16070,7 @@
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@@ -16082,18 +16082,18 @@
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     {
-        "Platform": "Intel® Core™ i9-13900K CPU-only",
-        "Model": "falcon-7b-instruct",
-        "Checked": true,
+        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU+iGPU",
+        "Model": "ssd_mobilenet_v1_coco",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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@@ -16103,9 +16103,9 @@
             "latency": {
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-                        "int8": 106.59,
-                        "fp16": 206.8,
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@@ -16116,18 +16116,18 @@
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-        "Model": "llama-2-7b-chat-hf",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Platform": "Intel® Celeron® 6305E CPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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@@ -16137,9 +16137,9 @@
             "latency": {
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-                        "int8": 107.89,
-                        "fp16": 202.31,
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@@ -16150,18 +16150,18 @@
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     {
-        "Platform": "Intel® Core™ i9-13900K CPU-only",
-        "Model": "llama-3-8b",
-        "Checked": true,
+        "Platform": "Intel® Core™ i3-8100 CPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -16171,9 +16171,9 @@
             "latency": {
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-                        "int8": 115.97,
-                        "fp16": 223.88,
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@@ -16184,18 +16184,18 @@
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     {
-        "Platform": "Intel® Core™ i9-13900K CPU-only",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-10500TE CPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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@@ -16206,7 +16206,7 @@
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@@ -16218,18 +16218,18 @@
         }
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     {
-        "Platform": "Intel® Core™ i9-13900K CPU-only",
-        "Model": "mistral-7b-v0.1",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1235U Processor CPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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@@ -16239,9 +16239,9 @@
             "latency": {
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-                        "int8": 109.38,
-                        "fp16": 212.63,
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@@ -16252,18 +16252,18 @@
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     {
-        "Platform": "Intel® Core™ i9-13900K CPU-only",
-        "Model": "mobilenet-v2",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-1335U Processor CPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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-                        "int8": 4246.79,
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@@ -16274,7 +16274,7 @@
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@@ -16286,18 +16286,18 @@
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     {
-        "Platform": "Intel® Core™ i9-13900K CPU-only",
-        "Model": "phi-3-mini-4k-instruct",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-13600K CPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -16307,9 +16307,9 @@
             "latency": {
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-                        "int8": 64.19,
-                        "fp16": 117.53,
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@@ -16320,18 +16320,18 @@
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     {
-        "Platform": "Intel® Core™ i9-13900K CPU-only",
-        "Model": "resnet-50",
-        "Checked": true,
+        "Platform": "Intel® Core™ i5-8500 CPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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-                        "int8": 765.18,
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@@ -16342,7 +16342,7 @@
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@@ -16354,18 +16354,18 @@
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     {
-        "Platform": "Intel® Core™ i9-13900K CPU-only",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185G7 CPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -16376,7 +16376,7 @@
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@@ -16388,18 +16388,18 @@
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-        "Platform": "Intel® Core™ i9-13900K CPU-only",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
+        "Platform": "Intel® Core™ i7-1185GRE CPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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@@ -16410,7 +16410,7 @@
                 "Precisions": [
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@@ -16422,18 +16422,18 @@
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     {
-        "Platform": "Intel® Core™ i9-13900K CPU-only",
-        "Model": "stable-diffusion-v2-1",
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+        "Platform": "Intel® Core™ i7-12700H CPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -16444,8 +16444,8 @@
                 "Precisions": [
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-                        "fp16": 43.36,
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@@ -16456,16 +16456,16 @@
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     {
-        "Platform": "Intel® Core™ i9-13900K CPU-only",
-        "Model": "stable-diffusion-v2-1",
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+        "Platform": "Intel® Core™ i7-1355U Processor CPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
         "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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@@ -16478,8 +16478,8 @@
                 "Precisions": [
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@@ -16490,18 +16490,18 @@
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     {
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+        "Platform": "Intel® Core™ i7-1360P CPU-only",
         "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™) CPU-only",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -16512,7 +16512,7 @@
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@@ -16524,18 +16524,18 @@
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-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™) CPU-only",
+        "Platform": "Intel® Core™ i7-8700T CPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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@@ -16546,7 +16546,7 @@
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@@ -16558,18 +16558,18 @@
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-        "Checked": true,
-        "PlatformType": "Client Platforms (Intel® Core™) CPU-only",
+        "Platform": "Intel® Core™ i9-10900TE CPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -16580,7 +16580,7 @@
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@@ -16592,18 +16592,18 @@
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     {
-        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
-        "Model": "bert-base-cased",
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-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Core™ i9-13900K CPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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-                        "fp16": 136.35,
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@@ -16614,7 +16614,7 @@
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@@ -16626,18 +16626,18 @@
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-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
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-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Xeon® W1290P CPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
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-                        "fp16": 22.83,
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@@ -16648,7 +16648,7 @@
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@@ -16660,18 +16660,18 @@
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-        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
-        "Model": "chatglm2-6b",
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-        "PlatformType": "Accelerator Platforms",
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+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 2.45,
                         "fp16": "",
-                        "fp32": "",
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                 ],
@@ -16681,8 +16681,8 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": 77.22,
-                        "int8": "",
+                        "int4": "",
+                        "int8": 415.75,
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@@ -16694,18 +16694,18 @@
         }
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     {
-        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
-        "Model": "efficientdet-d0",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Xeon® Gold 5218T CPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 146.84,
-                        "fp16": 120.95,
-                        "fp32": "",
+                        "int8": 28.98,
+                        "fp16": "",
+                        "fp32": 7.41,
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                 ],
@@ -16716,7 +16716,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 9.61,
+                        "int8": 69.96,
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@@ -16728,18 +16728,18 @@
         }
     },
     {
-        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
-        "Model": "falcon-7b-instruct",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Xeon® Platinum 8280 CPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 96.65,
                         "fp16": "",
-                        "fp32": "",
+                        "fp32": 22.64,
                         "bf16": ""
                     }
                 ],
@@ -16749,8 +16749,8 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": 169.76,
-                        "int8": "",
+                        "int4": "",
+                        "int8": 23.08,
                         "fp16": "",
                         "fp32": "",
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@@ -16762,18 +16762,18 @@
         }
     },
     {
-        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
-        "Model": "llama-2-7b-chat-hf",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Xeon® Platinum 8380 CPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 139.91,
                         "fp16": "",
-                        "fp32": "",
+                        "fp32": 33.26,
                         "bf16": ""
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                 ],
@@ -16783,8 +16783,8 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": 193.46,
-                        "int8": "",
+                        "int4": "",
+                        "int8": 15.34,
                         "fp16": "",
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@@ -16796,19 +16796,19 @@
         }
     },
     {
-        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Xeon® Platinum 8480+ CPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 6.0,
+                        "int8": 511.53,
                         "fp16": "",
-                        "fp32": "",
-                        "bf16": ""
+                        "fp32": 48.78,
+                        "bf16": 276.34
                     }
                 ],
                 "Unit": "FPS",
@@ -16818,10 +16818,10 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 187.27,
+                        "int8": 6.66,
                         "fp16": "",
                         "fp32": "",
-                        "bf16": ""
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                     }
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                 "Unit": "ms",
@@ -16830,19 +16830,19 @@
         }
     },
     {
-        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
-        "Model": "mistral-7b-v0.1",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Xeon® Platinum 8580 CPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 617.57,
                         "fp16": "",
-                        "fp32": "",
-                        "bf16": ""
+                        "fp32": 56.93,
+                        "bf16": 334.64
                     }
                 ],
                 "Unit": "FPS",
@@ -16851,11 +16851,11 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": 82.49,
-                        "int8": "",
+                        "int4": "",
+                        "int8": 5.71,
                         "fp16": "",
                         "fp32": "",
-                        "bf16": ""
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                 "Unit": "ms",
@@ -16864,18 +16864,18 @@
         }
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     {
-        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
-        "Model": "mobilenet-v2",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Xeon® Gold 6238L CPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1637.84,
-                        "fp16": 1426.18,
-                        "fp32": "",
+                        "int8": 69.09,
+                        "fp16": "",
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                         "bf16": ""
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                 ],
@@ -16886,7 +16886,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 0.82,
+                        "int8": 29.59,
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@@ -16898,18 +16898,18 @@
         }
     },
     {
-        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
-        "Model": "phi-3-mini-4k-instruct",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Xeon® Silver 4316 CPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 69.98,
                         "fp16": "",
-                        "fp32": "",
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                 ],
@@ -16919,8 +16919,8 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": 51.42,
-                        "int8": 77.7,
+                        "int4": "",
+                        "int8": 30.05,
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@@ -16932,18 +16932,18 @@
         }
     },
     {
-        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
-        "Model": "resnet-50",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Core™ Ultra 7 processor 155H CPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 733.73,
-                        "fp16": 505.75,
-                        "fp32": "",
+                        "int8": 8.9,
+                        "fp16": "",
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                         "bf16": ""
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                 ],
@@ -16954,7 +16954,7 @@
                 "Precisions": [
                     {
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-                        "int8": 1.55,
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@@ -16966,18 +16966,18 @@
         }
     },
     {
-        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 30.37,
-                        "fp16": 15.28,
-                        "fp32": "",
+                        "int8": 6.79,
+                        "fp16": "",
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                         "bf16": ""
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                 ],
@@ -16988,7 +16988,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 39.94,
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@@ -17000,18 +17000,18 @@
         }
     },
     {
-        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Core™ Ultra 9 processor 288V CPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 982.12,
-                        "fp16": 761.15,
-                        "fp32": "",
+                        "int8": 7.92,
+                        "fp16": "",
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                         "bf16": ""
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                 ],
@@ -17022,7 +17022,7 @@
                 "Precisions": [
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@@ -17034,17 +17034,17 @@
         }
     },
     {
-        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
-        "Model": "stable-diffusion-v2-1",
-        "Checked": true,
+        "Platform": "Intel® Data Center GPU Flex 170 dGPU",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
         "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
-                        "fp16": "",
+                        "int8": 276.55,
+                        "fp16": 194.61,
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@@ -17056,8 +17056,8 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 9.96,
-                        "fp16": 9.81,
+                        "int8": 5.38,
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@@ -17068,9 +17068,9 @@
         }
     },
     {
-        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
-        "Model": "stable-diffusion-v2-1",
-        "Checked": true,
+        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
         "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
@@ -17090,8 +17090,8 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 10.01,
-                        "fp16": 9.84,
+                        "int8": 6.54,
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@@ -17104,15 +17104,15 @@
     {
         "Platform": "Intel® Data Center GPU Flex 140 dGPU",
         "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
+        "Checked": "true",
         "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 55.03,
-                        "fp16": 34.37,
+                        "int8": 55.61,
+                        "fp16": 34.5,
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                         "bf16": ""
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@@ -17124,7 +17124,7 @@
                 "Precisions": [
                     {
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-                        "int8": 19.14,
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@@ -17136,17 +17136,17 @@
         }
     },
     {
-        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Core™ Ultra 7 processor 155H NPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 637.68,
-                        "fp16": 654.7,
+                        "int8": "",
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                         "bf16": ""
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@@ -17158,7 +17158,7 @@
                 "Precisions": [
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-                        "int8": 1.84,
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@@ -17170,17 +17170,17 @@
         }
     },
     {
-        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
-        "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Accelerator Platforms",
+        "Platform": "Intel® Core™ Ultra 7 processor 165H NPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 249.88,
-                        "fp16": 274.64,
+                        "int8": "",
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                         "bf16": ""
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@@ -17192,7 +17192,7 @@
                 "Precisions": [
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-                        "int8": 4.51,
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@@ -17204,18 +17204,18 @@
         }
     },
     {
-        "Platform": "Intel® Processor N100 CPU+iGPU",
-        "Model": "efficientdet-d0",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ Ultra 9 processor 288V NPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 37.29,
-                        "fp16": "",
-                        "fp32": 28.25,
+                        "int8": "",
+                        "fp16": 46.18,
+                        "fp32": "",
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                 ],
@@ -17238,18 +17238,18 @@
         }
     },
     {
-        "Platform": "Intel® Processor N100 CPU+iGPU",
-        "Model": "mobilenet-v2",
-        "Checked": true,
+        "Platform": "Intel® Atom® x7425E iGPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
         "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 489.13,
+                        "int8": 3.4,
                         "fp16": "",
-                        "fp32": 282.44,
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                 ],
@@ -17260,7 +17260,7 @@
                 "Precisions": [
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@@ -17272,18 +17272,18 @@
         }
     },
     {
-        "Platform": "Intel® Processor N100 CPU+iGPU",
-        "Model": "resnet-50",
-        "Checked": true,
+        "Platform": "Intel® Atom® X6425E iGPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
         "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 113.02,
+                        "int8": 1.98,
                         "fp16": "",
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                 ],
@@ -17294,7 +17294,7 @@
                 "Precisions": [
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@@ -17306,18 +17306,18 @@
         }
     },
     {
-        "Platform": "Intel® Processor N100 CPU+iGPU",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
+        "Platform": "Intel® Celeron® 6305E iGPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
         "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1.62,
-                        "fp16": "",
-                        "fp32": 0.64,
+                        "int8": 8.29,
+                        "fp16": 4.48,
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                 ],
@@ -17328,7 +17328,7 @@
                 "Precisions": [
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@@ -17340,18 +17340,18 @@
         }
     },
     {
-        "Platform": "Intel® Processor N100 CPU+iGPU",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i5-1235U Processor iGPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 217.68,
-                        "fp16": "",
-                        "fp32": 96.69,
+                        "int8": 10.02,
+                        "fp16": 4.68,
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                 ],
@@ -17362,7 +17362,7 @@
                 "Precisions": [
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@@ -17374,18 +17374,18 @@
         }
     },
     {
-        "Platform": "Intel® Processor N100 CPU+iGPU",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i5-1335U Processor iGPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 127.08,
-                        "fp16": "",
-                        "fp32": 44.27,
+                        "int8": 9.86,
+                        "fp16": 4.73,
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                 ],
@@ -17396,7 +17396,7 @@
                 "Precisions": [
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@@ -17408,18 +17408,18 @@
         }
     },
     {
-        "Platform": "Intel® Processor N100 CPU+iGPU",
-        "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i7-1185G7 iGPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 61.22,
-                        "fp16": "",
-                        "fp32": 29.19,
+                        "int8": 14.62,
+                        "fp16": 7.64,
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                 ],
@@ -17430,7 +17430,7 @@
                 "Precisions": [
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@@ -17442,18 +17442,18 @@
         }
     },
     {
-        "Platform": "Intel® Processor N100 CPU-only",
-        "Model": "efficientdet-d0",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™) CPU-only",
+        "Platform": "Intel® Core™ i7-1185GRE iGPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 15.45,
-                        "fp16": "",
-                        "fp32": 12.76,
+                        "int8": "",
+                        "fp16": 5.13,
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                 ],
@@ -17464,7 +17464,7 @@
                 "Precisions": [
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@@ -17476,18 +17476,18 @@
         }
     },
     {
-        "Platform": "Intel® Processor N100 CPU-only",
-        "Model": "mobilenet-v2",
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-        "PlatformType": "Mobile Platforms (Intel® Atom™) CPU-only",
+        "Platform": "Intel® Core™ i7-12700H iGPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 296.43,
-                        "fp16": "",
-                        "fp32": 184.84,
+                        "int8": 18.44,
+                        "fp16": 9.72,
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@@ -17498,7 +17498,7 @@
                 "Precisions": [
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-                        "int8": 3.82,
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@@ -17510,18 +17510,18 @@
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-        "Platform": "Intel® Processor N100 CPU-only",
-        "Model": "resnet-50",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™) CPU-only",
+        "Platform": "Intel® Core™ i7-1355U Processor iGPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 48.78,
-                        "fp16": "",
-                        "fp32": 20.2,
+                        "int8": 12.84,
+                        "fp16": 6.49,
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@@ -17532,7 +17532,7 @@
                 "Precisions": [
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-                        "int8": 21.89,
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@@ -17544,10 +17544,10 @@
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-        "Platform": "Intel® Processor N100 CPU-only",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™) CPU-only",
+        "Platform": "Intel® Core™ i7-1360P iGPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -17555,7 +17555,7 @@
                         "int4": "",
                         "int8": "",
                         "fp16": "",
-                        "fp32": 0.32,
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@@ -17578,18 +17578,18 @@
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-        "Platform": "Intel® Processor N100 CPU-only",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™) CPU-only",
+        "Platform": "Intel® Processor N100 iGPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 106.18,
+                        "int8": 2.56,
                         "fp16": "",
-                        "fp32": 49.82,
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@@ -17600,7 +17600,7 @@
                 "Precisions": [
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-                        "int8": 9.73,
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@@ -17612,18 +17612,18 @@
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-        "Platform": "Intel® Processor N100 CPU-only",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™) CPU-only",
+        "Platform": "Intel® Core™ Ultra 7 processor 155H iGPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 57.05,
-                        "fp16": "",
-                        "fp32": 24.81,
+                        "int8": 36.35,
+                        "fp16": 23.49,
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@@ -17634,7 +17634,7 @@
                 "Precisions": [
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-                        "int8": 18.81,
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@@ -17646,18 +17646,18 @@
         }
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-        "Platform": "Intel® Processor N100 CPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ Ultra 7 processor 165H iGPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 23.66,
-                        "fp16": "",
-                        "fp32": 12.9,
+                        "int8": 31.67,
+                        "fp16": 18.53,
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@@ -17668,7 +17668,7 @@
                 "Precisions": [
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@@ -17680,17 +17680,17 @@
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     {
-        "Platform": "Intel® Processor N100 iGPU-only",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ Ultra 9 processor 288V iGPU-only",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 2.36,
-                        "fp16": "",
+                        "int8": 105.25,
+                        "fp16": 74.67,
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@@ -17702,7 +17702,7 @@
                 "Precisions": [
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-                        "int8": 407.92,
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@@ -17714,18 +17714,18 @@
         }
     },
     {
-        "Platform": "Intel® Processor N100 iGPU-only",
-        "Model": "efficientdet-d0",
-        "Checked": true,
+        "Platform": "Intel® Celeron® 6305E CPU+iGPU",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
         "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 33.64,
-                        "fp16": 30.93,
-                        "fp32": "",
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@@ -17736,7 +17736,7 @@
                 "Precisions": [
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-                        "int8": 38.0,
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@@ -17748,18 +17748,18 @@
         }
     },
     {
-        "Platform": "Intel® Processor N100 iGPU-only",
-        "Model": "mobilenet-v2",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i5-1235U Processor CPU+iGPU",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 340.87,
-                        "fp16": 267.71,
-                        "fp32": "",
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@@ -17770,7 +17770,7 @@
                 "Precisions": [
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@@ -17782,18 +17782,18 @@
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     {
-        "Platform": "Intel® Processor N100 iGPU-only",
-        "Model": "resnet-50",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i5-1335U Processor CPU+iGPU",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 81.72,
-                        "fp16": 49.76,
-                        "fp32": "",
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@@ -17804,7 +17804,7 @@
                 "Precisions": [
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-                        "int8": 13.14,
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@@ -17816,18 +17816,18 @@
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-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i7-1185G7 CPU+iGPU",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1.62,
-                        "fp16": 1.01,
-                        "fp32": "",
+                        "int8": 15.79,
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@@ -17838,7 +17838,7 @@
                 "Precisions": [
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-                        "int8": 623.4,
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@@ -17850,18 +17850,18 @@
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-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i7-1185GRE CPU+iGPU",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 164.34,
-                        "fp16": 106.95,
-                        "fp32": "",
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@@ -17872,7 +17872,7 @@
                 "Precisions": [
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@@ -17884,18 +17884,18 @@
         }
     },
     {
-        "Platform": "Intel® Processor N100 iGPU-only",
+        "Platform": "Intel® Core™ i7-12700H CPU+iGPU",
         "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 2.56,
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@@ -17906,7 +17906,7 @@
                 "Precisions": [
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@@ -17918,18 +17918,18 @@
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-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i7-1355U Processor CPU+iGPU",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 95.73,
-                        "fp16": 62.51,
-                        "fp32": "",
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@@ -17940,7 +17940,7 @@
                 "Precisions": [
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@@ -17952,18 +17952,18 @@
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-        "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Platform": "Intel® Core™ i7-1360P CPU+iGPU",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 47.05,
-                        "fp16": 34.98,
-                        "fp32": "",
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@@ -17974,7 +17974,7 @@
                 "Precisions": [
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@@ -17986,18 +17986,18 @@
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-        "Model": "bert-base-cased",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ Ultra 7 processor 155H CPU+iGPU",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 20.54,
+                        "int8": 31.21,
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@@ -18008,7 +18008,7 @@
                 "Precisions": [
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@@ -18020,18 +18020,18 @@
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-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
-        "Parameters": {
-            "throughput": {
-                "Precisions": [
+        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU+iGPU",
+        "Model": "unet-camvid-onnx-0001",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Parameters": {
+            "throughput": {
+                "Precisions": [
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-                        "fp32": 1.27,
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@@ -18042,7 +18042,7 @@
                 "Precisions": [
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@@ -18054,18 +18054,18 @@
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-        "Model": "efficientdet-d0",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Atom® x7425E CPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 35.83,
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-                        "fp32": 30.12,
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@@ -18076,7 +18076,7 @@
                 "Precisions": [
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-                        "int8": 28.57,
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@@ -18088,18 +18088,18 @@
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-        "Platform": "Intel® Xeon® E-2124G CPU-only",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Atom® X6425E CPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 0.29,
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-                        "fp32": 0.15,
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@@ -18110,7 +18110,7 @@
                 "Precisions": [
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-                        "int8": 3413.65,
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@@ -18122,18 +18122,18 @@
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-        "Model": "mobilenet-v2",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Celeron® 6305E CPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 517.55,
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-                        "fp32": 420.54,
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@@ -18144,7 +18144,7 @@
                 "Precisions": [
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-                        "int8": 2.1,
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@@ -18156,18 +18156,18 @@
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-        "Model": "resnet-50",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i3-8100 CPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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                         "int4": "",
-                        "int8": 91.9,
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-                        "fp32": 49.4,
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@@ -18178,7 +18178,7 @@
                 "Precisions": [
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@@ -18190,18 +18190,18 @@
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-        "Platform": "Intel® Xeon® E-2124G CPU-only",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i5-10500TE CPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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-                        "int8": "",
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@@ -18212,7 +18212,7 @@
                 "Precisions": [
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@@ -18224,18 +18224,18 @@
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-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i5-1235U Processor CPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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                         "int4": "",
-                        "int8": 203.48,
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@@ -18246,7 +18246,7 @@
                 "Precisions": [
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@@ -18258,18 +18258,18 @@
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-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i5-1335U Processor CPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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-                        "int8": 2.44,
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-                        "fp32": 1.35,
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@@ -18280,7 +18280,7 @@
                 "Precisions": [
                     {
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-                        "int8": 420.33,
+                        "int8": 70.0,
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@@ -18292,18 +18292,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® E-2124G CPU-only",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i5-13600K CPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 104.89,
+                        "int8": 66.59,
                         "fp16": "",
-                        "fp32": 59.76,
+                        "fp32": 20.16,
                         "bf16": ""
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                 ],
@@ -18314,7 +18314,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 9.55,
+                        "int8": 19.62,
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@@ -18326,18 +18326,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® E-2124G CPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i5-8500 CPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 51.87,
+                        "int8": 19.4,
                         "fp16": "",
-                        "fp32": 32.46,
+                        "fp32": 10.16,
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                 ],
@@ -18348,7 +18348,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 19.57,
+                        "int8": 52.75,
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@@ -18360,18 +18360,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Gold 5218T CPU-only",
-        "Model": "bert-base-cased",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i7-1185G7 CPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 217.64,
+                        "int8": 27.81,
                         "fp16": "",
-                        "fp32": 80.49,
+                        "fp32": 7.85,
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                 ],
@@ -18382,7 +18382,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 14.48,
+                        "int8": 37.86,
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@@ -18394,18 +18394,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Gold 5218T CPU-only",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i7-1185GRE CPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 21.88,
+                        "int8": 20.29,
                         "fp16": "",
-                        "fp32": 7.16,
+                        "fp32": 5.69,
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                 ],
@@ -18416,7 +18416,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 102.56,
+                        "int8": 49.78,
                         "fp16": "",
                         "fp32": "",
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@@ -18428,18 +18428,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Gold 5218T CPU-only",
-        "Model": "efficientdet-d0",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i7-12700H CPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 271.59,
+                        "int8": 49.8,
                         "fp16": "",
-                        "fp32": 166.47,
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                 ],
@@ -18450,7 +18450,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 10.86,
+                        "int8": 25.53,
                         "fp16": "",
                         "fp32": "",
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@@ -18462,18 +18462,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Gold 5218T CPU-only",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i7-1355U Processor CPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 3.24,
+                        "int8": 25.87,
                         "fp16": "",
-                        "fp32": 0.9,
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                 ],
@@ -18484,7 +18484,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 645.82,
+                        "int8": 65.38,
                         "fp16": "",
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@@ -18496,18 +18496,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Gold 5218T CPU-only",
-        "Model": "mobilenet-v2",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i7-1360P CPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 5417.69,
+                        "int8": 37.03,
                         "fp16": "",
-                        "fp32": 1951.56,
+                        "fp32": 10.74,
                         "bf16": ""
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                 ],
@@ -18518,7 +18518,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1.55,
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@@ -18530,18 +18530,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Gold 5218T CPU-only",
-        "Model": "resnet-50",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i7-8700T CPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 977.75,
+                        "int8": 15.32,
                         "fp16": "",
-                        "fp32": 268.95,
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                 ],
@@ -18552,7 +18552,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 3.03,
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@@ -18564,18 +18564,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Gold 5218T CPU-only",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i9-10900TE CPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 19.78,
                         "fp16": "",
-                        "fp32": 4.58,
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                 ],
@@ -18586,7 +18586,7 @@
                 "Precisions": [
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                         "int4": "",
-                        "int8": "",
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@@ -18598,18 +18598,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Gold 5218T CPU-only",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i9-13900K CPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 2098.35,
+                        "int8": 95.49,
                         "fp16": "",
-                        "fp32": 638.36,
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                 ],
@@ -18620,7 +18620,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1.63,
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@@ -18632,18 +18632,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Gold 5218T CPU-only",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Processor N100 CPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 28.95,
+                        "int8": 6.13,
                         "fp16": "",
-                        "fp32": 7.41,
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                 ],
@@ -18654,7 +18654,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 69.9,
+                        "int8": 167.91,
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@@ -18666,18 +18666,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Gold 5218T CPU-only",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
+        "Platform": "Intel® Xeon® W1290P CPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
         "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1046.46,
+                        "int8": 32.67,
                         "fp16": "",
-                        "fp32": 337.12,
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                 ],
@@ -18688,7 +18688,7 @@
                 "Precisions": [
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-                        "int8": 2.54,
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@@ -18700,18 +18700,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Gold 5218T CPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
+        "Platform": "Intel® Xeon® E-2124G CPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
         "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 444.54,
+                        "int8": 12.06,
                         "fp16": "",
-                        "fp32": 173.35,
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                 ],
@@ -18722,7 +18722,7 @@
                 "Precisions": [
                     {
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-                        "int8": 6.01,
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@@ -18734,18 +18734,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Platinum 8280 CPU-only",
-        "Model": "bert-base-cased",
-        "Checked": true,
+        "Platform": "Intel® Xeon® Gold 5218T CPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
         "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 591.59,
+                        "int8": 117.81,
                         "fp16": "",
-                        "fp32": 224.32,
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                 ],
@@ -18756,7 +18756,7 @@
                 "Precisions": [
                     {
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-                        "int8": 8.97,
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@@ -18769,17 +18769,17 @@
     },
     {
         "Platform": "Intel® Xeon® Platinum 8280 CPU-only",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
+        "Model": "yolo_v5m",
+        "Checked": "true",
         "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 51.23,
+                        "int8": 334.59,
                         "fp16": "",
-                        "fp32": 18.18,
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                 ],
@@ -18790,7 +18790,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 48.54,
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@@ -18802,18 +18802,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Platinum 8280 CPU-only",
-        "Model": "efficientdet-d0",
-        "Checked": true,
+        "Platform": "Intel® Xeon® Platinum 8380 CPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
         "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 586.49,
+                        "int8": 533.18,
                         "fp16": "",
-                        "fp32": 330.88,
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                         "bf16": ""
                     }
                 ],
@@ -18824,7 +18824,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 6.81,
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@@ -18836,19 +18836,19 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Platinum 8280 CPU-only",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
+        "Platform": "Intel® Xeon® Platinum 8480+ CPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
         "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 8.47,
+                        "int8": 1419.98,
                         "fp16": "",
-                        "fp32": 2.26,
-                        "bf16": ""
+                        "fp32": 214.44,
+                        "bf16": 830.31
                     }
                 ],
                 "Unit": "FPS",
@@ -18858,10 +18858,10 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 240.77,
+                        "int8": 4.62,
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                         "fp32": "",
-                        "bf16": ""
+                        "bf16": 5.98
                     }
                 ],
                 "Unit": "ms",
@@ -18870,19 +18870,19 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Platinum 8280 CPU-only",
-        "Model": "mobilenet-v2",
-        "Checked": true,
+        "Platform": "Intel® Xeon® Platinum 8580 CPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
         "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 15049.28,
+                        "int8": 1755.21,
                         "fp16": "",
-                        "fp32": 4720.87,
-                        "bf16": ""
+                        "fp32": 265.47,
+                        "bf16": 1055.23
                     }
                 ],
                 "Unit": "FPS",
@@ -18892,10 +18892,10 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 0.98,
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                         "fp32": "",
-                        "bf16": ""
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                     }
                 ],
                 "Unit": "ms",
@@ -18904,18 +18904,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Platinum 8280 CPU-only",
-        "Model": "resnet-50",
-        "Checked": true,
+        "Platform": "Intel® Xeon® Gold 6238L CPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
         "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 2983.04,
+                        "int8": 251.62,
                         "fp16": "",
-                        "fp32": 761.91,
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                         "bf16": ""
                     }
                 ],
@@ -18926,7 +18926,7 @@
                 "Precisions": [
                     {
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-                        "int8": 1.63,
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@@ -18938,18 +18938,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Platinum 8280 CPU-only",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
+        "Platform": "Intel® Xeon® Silver 4316 CPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
         "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
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                         "fp16": "",
-                        "fp32": 15.12,
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                     }
                 ],
@@ -18960,7 +18960,7 @@
                 "Precisions": [
                     {
                         "int4": "",
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@@ -18972,18 +18972,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Platinum 8280 CPU-only",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ Ultra 7 processor 155H CPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 6154.14,
+                        "int8": 44.74,
                         "fp16": "",
-                        "fp32": 1647.46,
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                     }
                 ],
@@ -18994,7 +18994,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1.16,
+                        "int8": 36.67,
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@@ -19006,18 +19006,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Platinum 8280 CPU-only",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 96.99,
+                        "int8": 33.1,
                         "fp16": "",
-                        "fp32": 22.68,
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                 ],
@@ -19028,7 +19028,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 22.95,
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@@ -19040,18 +19040,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Platinum 8280 CPU-only",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Data Center GPU Flex 170 dGPU",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 2888.11,
-                        "fp16": "",
-                        "fp32": 900.74,
+                        "int8": 288.05,
+                        "fp16": 333.83,
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                         "bf16": ""
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                 ],
@@ -19062,7 +19062,7 @@
                 "Precisions": [
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-                        "int8": 1.32,
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@@ -19074,18 +19074,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Platinum 8280 CPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1008.75,
+                        "int8": "",
                         "fp16": "",
-                        "fp32": 455.37,
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@@ -19096,7 +19096,7 @@
                 "Precisions": [
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-                        "int8": 3.62,
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@@ -19108,18 +19108,18 @@
         }
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     {
-        "Platform": "Intel® Xeon® Platinum 8380 CPU-only",
-        "Model": "bert-base-cased",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 884.16,
-                        "fp16": "",
-                        "fp32": 340.66,
+                        "int8": 105.43,
+                        "fp16": 93.59,
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@@ -19130,7 +19130,7 @@
                 "Precisions": [
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-                        "int8": 5.03,
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@@ -19142,18 +19142,18 @@
         }
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     {
-        "Platform": "Intel® Xeon® Platinum 8380 CPU-only",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ Ultra 7 processor 155H NPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 78.5,
-                        "fp16": "",
-                        "fp32": 30.65,
+                        "int8": 50.31,
+                        "fp16": 40.99,
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@@ -19164,7 +19164,7 @@
                 "Precisions": [
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-                        "int8": 38.23,
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@@ -19176,17 +19176,17 @@
         }
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-        "Platform": "Intel® Xeon® Platinum 8380 CPU-only",
-        "Model": "chatglm2-6b",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ Ultra 7 processor 165H NPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
-                        "fp16": "",
+                        "int8": 50.34,
+                        "fp16": 41.31,
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@@ -19197,9 +19197,9 @@
             "latency": {
                 "Precisions": [
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-                        "int4": 32.24,
-                        "int8": 43.21,
-                        "fp16": 78.06,
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+                        "int8": 21.03,
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@@ -19210,18 +19210,18 @@
         }
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     {
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-        "Model": "efficientdet-d0",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Atom® x7425E iGPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1013.0,
-                        "fp16": "",
-                        "fp32": 578.0,
+                        "int8": 14.53,
+                        "fp16": 9.52,
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@@ -19232,7 +19232,7 @@
                 "Precisions": [
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-                        "int8": 4.25,
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@@ -19244,17 +19244,17 @@
         }
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-        "Platform": "Intel® Xeon® Platinum 8380 CPU-only",
-        "Model": "falcon-7b-instruct",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Atom® X6425E iGPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
-                        "fp16": "",
+                        "int8": 7.37,
+                        "fp16": 7.57,
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@@ -19265,9 +19265,9 @@
             "latency": {
                 "Precisions": [
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-                        "int4": 34.77,
-                        "int8": 50.16,
-                        "fp16": 91.17,
+                        "int4": "",
+                        "int8": 141.78,
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@@ -19278,17 +19278,17 @@
         }
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     {
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-        "Model": "llama-2-7b-chat-hf",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Celeron® 6305E iGPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
-                        "fp16": "",
+                        "int8": 31.36,
+                        "fp16": 17.91,
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@@ -19299,9 +19299,9 @@
             "latency": {
                 "Precisions": [
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-                        "int4": 39.36,
-                        "int8": 53.72,
-                        "fp16": 93.67,
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+                        "int8": 34.6,
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@@ -19312,17 +19312,17 @@
         }
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     {
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-        "Model": "llama-3-8b",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i5-1235U Processor iGPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
-                        "fp16": "",
+                        "int8": 33.47,
+                        "fp16": 18.2,
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@@ -19333,9 +19333,9 @@
             "latency": {
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-                        "int4": 37.82,
-                        "int8": 56.11,
-                        "fp16": 101.67,
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+                        "int8": 24.97,
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@@ -19346,18 +19346,18 @@
         }
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     {
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-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i5-1335U Processor iGPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 14.58,
-                        "fp16": "",
-                        "fp32": 3.48,
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+                        "fp16": 18.84,
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@@ -19368,7 +19368,7 @@
                 "Precisions": [
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-                        "int8": 156.62,
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@@ -19380,17 +19380,17 @@
         }
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-        "Platform": "Intel® Xeon® Platinum 8380 CPU-only",
-        "Model": "mistral-7b-v0.1",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i7-1185G7 iGPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
-                        "fp16": "",
+                        "int8": 48.16,
+                        "fp16": 28.25,
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@@ -19401,9 +19401,9 @@
             "latency": {
                 "Precisions": [
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-                        "int4": 37.27,
-                        "int8": 54.13,
-                        "fp16": 96.82,
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+                        "int8": 22.07,
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@@ -19414,18 +19414,18 @@
         }
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-        "Model": "mobilenet-v2",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i7-1185GRE iGPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 22842.75,
-                        "fp16": "",
-                        "fp32": 7176.51,
+                        "int8": 33.73,
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@@ -19436,7 +19436,7 @@
                 "Precisions": [
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@@ -19448,17 +19448,17 @@
         }
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-        "Model": "phi-3-mini-4k-instruct",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i7-12700H iGPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
-                        "fp16": "",
+                        "int8": 63.37,
+                        "fp16": 36.52,
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@@ -19469,9 +19469,9 @@
             "latency": {
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-                        "int4": 24.27,
-                        "int8": 34.78,
-                        "fp16": 57.87,
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@@ -19482,18 +19482,18 @@
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-        "Model": "resnet-50",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i7-1355U Processor iGPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 4956.56,
-                        "fp16": "",
-                        "fp32": 1156.87,
+                        "int8": 44.64,
+                        "fp16": 24.85,
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@@ -19504,7 +19504,7 @@
                 "Precisions": [
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-                        "int8": 1.06,
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@@ -19516,18 +19516,18 @@
         }
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-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i7-1360P iGPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": "",
-                        "fp16": "",
-                        "fp32": 21.03,
+                        "int8": 53.52,
+                        "fp16": 30.53,
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@@ -19538,7 +19538,7 @@
                 "Precisions": [
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@@ -19550,18 +19550,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Platinum 8380 CPU-only",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Processor N100 iGPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 10271.81,
-                        "fp16": "",
-                        "fp32": 2546.94,
+                        "int8": 11.12,
+                        "fp16": 7.41,
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@@ -19572,7 +19572,7 @@
                 "Precisions": [
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@@ -19584,17 +19584,17 @@
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-        "Model": "stable-diffusion-v2-1",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ Ultra 7 processor 155H iGPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
-                        "fp16": "",
+                        "int8": 132.43,
+                        "fp16": 70.84,
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@@ -19606,8 +19606,8 @@
                 "Precisions": [
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                         "int4": "",
-                        "int8": 12.54,
-                        "fp16": 12.72,
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@@ -19618,17 +19618,17 @@
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-        "Model": "stable-diffusion-v2-1",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ Ultra 7 processor 165H iGPU-only",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
-                        "fp16": "",
+                        "int8": 120.91,
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@@ -19640,8 +19640,8 @@
                 "Precisions": [
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-                        "fp16": 12.75,
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@@ -19652,18 +19652,18 @@
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-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Atom® x7425E CPU+iGPU",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 139.91,
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-                        "fp32": 33.12,
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@@ -19674,7 +19674,7 @@
                 "Precisions": [
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@@ -19686,18 +19686,18 @@
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-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Atom® X6425E CPU+iGPU",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 4576.15,
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-                        "fp32": 1350.44,
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                 ],
@@ -19708,7 +19708,7 @@
                 "Precisions": [
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@@ -19720,18 +19720,18 @@
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-        "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Celeron® 6305E CPU+iGPU",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 1720.12,
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-                        "fp32": 701.48,
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@@ -19742,7 +19742,7 @@
                 "Precisions": [
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@@ -19754,19 +19754,19 @@
         }
     },
     {
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-        "Model": "bert-base-cased",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i5-1235U Processor CPU+iGPU",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
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                         "int4": "",
-                        "int8": 2931.47,
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                         "fp16": "",
-                        "fp32": 473.83,
-                        "bf16": 1895.22
+                        "fp32": 7.54,
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                 "Unit": "FPS",
@@ -19776,10 +19776,10 @@
                 "Precisions": [
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-                        "int8": 3.85,
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                 "Unit": "ms",
@@ -19788,19 +19788,19 @@
         }
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     {
-        "Platform": "Intel® Xeon® Platinum 8480+ CPU-only",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i5-1335U Processor CPU+iGPU",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 290.4,
+                        "int8": 32.16,
                         "fp16": "",
-                        "fp32": 39.74,
-                        "bf16": 198.51
+                        "fp32": 12.68,
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                 "Unit": "FPS",
@@ -19810,10 +19810,10 @@
                 "Precisions": [
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-                        "int8": 22.19,
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                 "Unit": "ms",
@@ -19822,18 +19822,18 @@
         }
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     {
-        "Platform": "Intel® Xeon® Platinum 8480+ CPU-only",
-        "Model": "chatglm2-6b",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i7-1185G7 CPU+iGPU",
+        "Model": "yolo_v5m",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
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@@ -19856,19 +19856,19 @@
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@@ -19924,18 +19924,18 @@
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-        "PlatformType": "Server Platforms (Intel® Xeon®)",
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@@ -19958,18 +19958,18 @@
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@@ -19992,19 +19992,19 @@
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-        "PlatformType": "Server Platforms (Intel® Xeon®)",
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         "Parameters": {
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@@ -20014,10 +20014,10 @@
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@@ -20047,9 +20047,9 @@
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@@ -20060,19 +20060,19 @@
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@@ -20128,19 +20128,19 @@
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@@ -20150,10 +20150,10 @@
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@@ -20252,8 +20252,8 @@
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@@ -20264,18 +20264,18 @@
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@@ -20286,8 +20286,8 @@
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@@ -20366,19 +20366,19 @@
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@@ -20400,19 +20400,19 @@
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@@ -20422,10 +20422,10 @@
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@@ -20434,19 +20434,19 @@
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@@ -20456,10 +20456,10 @@
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@@ -20468,18 +20468,18 @@
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@@ -20489,9 +20489,9 @@
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@@ -20502,19 +20502,19 @@
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@@ -20524,10 +20524,10 @@
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@@ -20536,10 +20536,10 @@
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+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
@@ -20557,9 +20557,9 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": 27.7,
-                        "int8": 39.66,
-                        "fp16": 61.46,
+                        "int4": "",
+                        "int8": 9.34,
+                        "fp16": "",
                         "fp32": "",
                         "bf16": ""
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@@ -20570,18 +20570,18 @@
         }
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     {
-        "Platform": "Intel® Xeon® Platinum 8580 CPU-only",
-        "Model": "llama-2-7b-chat-hf",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i7-8700T CPU-only",
+        "Model": "yolo_v8n",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 70.91,
                         "fp16": "",
-                        "fp32": "",
+                        "fp32": 40.94,
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                     }
                 ],
@@ -20591,9 +20591,9 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": 31.44,
-                        "int8": 41.97,
-                        "fp16": 63.62,
+                        "int4": "",
+                        "int8": 11.95,
+                        "fp16": "",
                         "fp32": "",
                         "bf16": ""
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@@ -20604,18 +20604,18 @@
         }
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     {
-        "Platform": "Intel® Xeon® Platinum 8580 CPU-only",
-        "Model": "llama-3-8b",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i9-10900TE CPU-only",
+        "Model": "yolo_v8n",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 89.73,
                         "fp16": "",
-                        "fp32": "",
+                        "fp32": 48.88,
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                 ],
@@ -20625,9 +20625,9 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": 34.15,
-                        "int8": 43.97,
-                        "fp16": 70.83,
+                        "int4": "",
+                        "int8": 12.74,
+                        "fp16": "",
                         "fp32": "",
                         "bf16": ""
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@@ -20638,19 +20638,19 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Platinum 8580 CPU-only",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i9-13900K CPU-only",
+        "Model": "yolo_v8n",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 76.64,
+                        "int8": 387.96,
                         "fp16": "",
-                        "fp32": 6.4,
-                        "bf16": 49.11
+                        "fp32": 154.46,
+                        "bf16": ""
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                 ],
                 "Unit": "FPS",
@@ -20660,10 +20660,10 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 55.27,
+                        "int8": 4.11,
                         "fp16": "",
                         "fp32": "",
-                        "bf16": 70.44
+                        "bf16": ""
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                 "Unit": "ms",
@@ -20672,18 +20672,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Platinum 8580 CPU-only",
-        "Model": "mistral-7b-v0.1",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Processor N100 CPU-only",
+        "Model": "yolo_v8n",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 23.66,
                         "fp16": "",
-                        "fp32": "",
+                        "fp32": 12.89,
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                 ],
@@ -20693,9 +20693,9 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": 29.76,
-                        "int8": 43.56,
-                        "fp16": 67.94,
+                        "int4": "",
+                        "int8": 43.5,
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                         "fp32": "",
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@@ -20706,19 +20706,19 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Platinum 8580 CPU-only",
-        "Model": "mobilenet-v2",
-        "Checked": true,
+        "Platform": "Intel® Xeon® W1290P CPU-only",
+        "Model": "yolo_v8n",
+        "Checked": "true",
         "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 40565.45,
+                        "int8": 136.65,
                         "fp16": "",
-                        "fp32": 16295.77,
-                        "bf16": 31334.21
+                        "fp32": 71.98,
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                 ],
                 "Unit": "FPS",
@@ -20728,10 +20728,10 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 0.76,
+                        "int8": 8.7,
                         "fp16": "",
                         "fp32": "",
-                        "bf16": 0.77
+                        "bf16": ""
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                 ],
                 "Unit": "ms",
@@ -20740,18 +20740,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Platinum 8580 CPU-only",
-        "Model": "phi-3-mini-4k-instruct",
-        "Checked": true,
+        "Platform": "Intel® Xeon® E-2124G CPU-only",
+        "Model": "yolo_v8n",
+        "Checked": "true",
         "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 51.95,
                         "fp16": "",
-                        "fp32": "",
+                        "fp32": 32.5,
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                 ],
@@ -20761,9 +20761,9 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": 18.67,
-                        "int8": 26.01,
-                        "fp16": 39.35,
+                        "int4": "",
+                        "int8": 19.42,
+                        "fp16": "",
                         "fp32": "",
                         "bf16": ""
                     }
@@ -20774,19 +20774,19 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Platinum 8580 CPU-only",
-        "Model": "resnet-50",
-        "Checked": true,
+        "Platform": "Intel® Xeon® Gold 5218T CPU-only",
+        "Model": "yolo_v8n",
+        "Checked": "true",
         "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 22345.32,
+                        "int8": 442.04,
                         "fp16": "",
-                        "fp32": 2027.96,
-                        "bf16": 13919.52
+                        "fp32": 173.4,
+                        "bf16": ""
                     }
                 ],
                 "Unit": "FPS",
@@ -20796,10 +20796,10 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1.19,
+                        "int8": 6.02,
                         "fp16": "",
                         "fp32": "",
-                        "bf16": 1.32
+                        "bf16": ""
                     }
                 ],
                 "Unit": "ms",
@@ -20808,19 +20808,19 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Platinum 8580 CPU-only",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
+        "Platform": "Intel® Xeon® Platinum 8280 CPU-only",
+        "Model": "yolo_v8n",
+        "Checked": "true",
         "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 997.47,
                         "fp16": "",
-                        "fp32": 35.75,
-                        "bf16": 280.99
+                        "fp32": 452.11,
+                        "bf16": ""
                     }
                 ],
                 "Unit": "FPS",
@@ -20833,7 +20833,7 @@
                         "int8": "",
                         "fp16": "",
                         "fp32": "",
-                        "bf16": 11.56
+                        "bf16": ""
                     }
                 ],
                 "Unit": "ms",
@@ -20842,19 +20842,19 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Platinum 8580 CPU-only",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
+        "Platform": "Intel® Xeon® Platinum 8380 CPU-only",
+        "Model": "yolo_v8n",
+        "Checked": "true",
         "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 28110.55,
+                        "int8": 1718.36,
                         "fp16": "",
-                        "fp32": 4751.56,
-                        "bf16": 17087.34
+                        "fp32": 701.27,
+                        "bf16": ""
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                 ],
                 "Unit": "FPS",
@@ -20864,10 +20864,10 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 0.74,
+                        "int8": 2.39,
                         "fp16": "",
                         "fp32": "",
-                        "bf16": 1.0
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                 ],
                 "Unit": "ms",
@@ -20876,19 +20876,19 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Platinum 8580 CPU-only",
-        "Model": "stable-diffusion-v2-1",
-        "Checked": true,
+        "Platform": "Intel® Xeon® Platinum 8480+ CPU-only",
+        "Model": "yolo_v8n",
+        "Checked": "true",
         "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 2411.67,
                         "fp16": "",
-                        "fp32": "",
-                        "bf16": ""
+                        "fp32": 959.63,
+                        "bf16": 2381.88
                     }
                 ],
                 "Unit": "FPS",
@@ -20898,10 +20898,10 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 3.33,
-                        "fp16": 3.24,
-                        "fp32": "",
-                        "bf16": ""
+                        "int8": 3.11,
+                        "fp16": "",
+                        "fp32": "",
+                        "bf16": 2.53
                     }
                 ],
                 "Unit": "ms",
@@ -20911,8 +20911,8 @@
     },
     {
         "Platform": "Intel® Xeon® Platinum 8580 CPU-only",
-        "Model": "stable-diffusion-v2-1",
-        "Checked": true,
+        "Model": "yolo_v8n",
+        "Checked": "true",
         "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
@@ -20921,8 +20921,8 @@
                         "int4": "",
                         "int8": "",
                         "fp16": "",
-                        "fp32": "",
-                        "bf16": ""
+                        "fp32": 1262.79,
+                        "bf16": 3461.09
                     }
                 ],
                 "Unit": "FPS",
@@ -20932,10 +20932,10 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 3.34,
-                        "fp16": 3.25,
+                        "int8": 3.15,
+                        "fp16": "",
                         "fp32": "",
-                        "bf16": ""
+                        "bf16": 2.62
                     }
                 ],
                 "Unit": "ms",
@@ -20944,19 +20944,19 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Platinum 8580 CPU-only",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
+        "Platform": "Intel® Xeon® Gold 6238L CPU-only",
+        "Model": "yolo_v8n",
+        "Checked": "true",
         "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 618.84,
+                        "int8": 757.45,
                         "fp16": "",
-                        "fp32": 57.43,
-                        "bf16": 340.8
+                        "fp32": 337.98,
+                        "bf16": ""
                     }
                 ],
                 "Unit": "FPS",
@@ -20966,10 +20966,10 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 5.61,
+                        "int8": 4.31,
                         "fp16": "",
                         "fp32": "",
-                        "bf16": 7.75
+                        "bf16": ""
                     }
                 ],
                 "Unit": "ms",
@@ -20978,19 +20978,19 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Platinum 8580 CPU-only",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
+        "Platform": "Intel® Xeon® Silver 4316 CPU-only",
+        "Model": "yolo_v8n",
+        "Checked": "true",
         "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 15025.26,
+                        "int8": 859.9,
                         "fp16": "",
-                        "fp32": 2508.91,
-                        "bf16": 13648.94
+                        "fp32": 340.51,
+                        "bf16": ""
                     }
                 ],
                 "Unit": "FPS",
@@ -21000,10 +21000,10 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 0.96,
+                        "int8": 3.38,
                         "fp16": "",
                         "fp32": "",
-                        "bf16": 0.85
+                        "bf16": ""
                     }
                 ],
                 "Unit": "ms",
@@ -21012,19 +21012,19 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Platinum 8580 CPU-only",
+        "Platform": "Intel® Core™ Ultra 7 processor 155H CPU-only",
         "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 3054.7,
+                        "int8": 166.62,
                         "fp16": "",
-                        "fp32": 1267.93,
-                        "bf16": 3458.4
+                        "fp32": 64.56,
+                        "bf16": ""
                     }
                 ],
                 "Unit": "FPS",
@@ -21034,10 +21034,10 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 3.12,
+                        "int8": 12.93,
                         "fp16": "",
                         "fp32": "",
-                        "bf16": 2.58
+                        "bf16": ""
                     }
                 ],
                 "Unit": "ms",
@@ -21046,18 +21046,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Silver 4216R CPU-only",
-        "Model": "bert-base-cased",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU-only",
+        "Model": "yolo_v8n",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 209.57,
+                        "int8": 127.76,
                         "fp16": "",
-                        "fp32": 76.84,
+                        "fp32": 46.89,
                         "bf16": ""
                     }
                 ],
@@ -21068,7 +21068,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 15.15,
+                        "int8": 14.67,
                         "fp16": "",
                         "fp32": "",
                         "bf16": ""
@@ -21080,18 +21080,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Silver 4216R CPU-only",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ Ultra 9 processor 288V CPU-only",
+        "Model": "yolo_v8n",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 20.82,
+                        "int8": 139.58,
                         "fp16": "",
-                        "fp32": 6.86,
+                        "fp32": 59.12,
                         "bf16": ""
                     }
                 ],
@@ -21102,7 +21102,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 106.02,
+                        "int8": 9.46,
                         "fp16": "",
                         "fp32": "",
                         "bf16": ""
@@ -21114,18 +21114,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Silver 4216R CPU-only",
-        "Model": "efficientdet-d0",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Data Center GPU Flex 170 dGPU",
+        "Model": "yolo_v8n",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 262.51,
-                        "fp16": "",
-                        "fp32": 160.86,
+                        "int8": 803.05,
+                        "fp16": 733.02,
+                        "fp32": "",
                         "bf16": ""
                     }
                 ],
@@ -21136,7 +21136,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 11.26,
+                        "int8": 2.32,
                         "fp16": "",
                         "fp32": "",
                         "bf16": ""
@@ -21148,18 +21148,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Silver 4216R CPU-only",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
+        "Model": "yolo_v8n",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 3.11,
+                        "int8": "",
                         "fp16": "",
-                        "fp32": 0.87,
+                        "fp32": "",
                         "bf16": ""
                     }
                 ],
@@ -21170,7 +21170,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 657.59,
+                        "int8": 3.14,
                         "fp16": "",
                         "fp32": "",
                         "bf16": ""
@@ -21182,18 +21182,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Silver 4216R CPU-only",
-        "Model": "mobilenet-v2",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
+        "Model": "yolo_v8n",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 5175.19,
-                        "fp16": "",
-                        "fp32": 1873.96,
+                        "int8": 322.1,
+                        "fp16": 299.84,
+                        "fp32": "",
                         "bf16": ""
                     }
                 ],
@@ -21204,7 +21204,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1.62,
+                        "int8": 3.75,
                         "fp16": "",
                         "fp32": "",
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@@ -21216,18 +21216,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Silver 4216R CPU-only",
-        "Model": "resnet-50",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ Ultra 7 processor 155H NPU-only",
+        "Model": "yolo_v8n",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 927.96,
-                        "fp16": "",
-                        "fp32": 259.21,
+                        "int8": 126.74,
+                        "fp16": 129.8,
+                        "fp32": "",
                         "bf16": ""
                     }
                 ],
@@ -21238,7 +21238,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 3.14,
+                        "int8": 9.1,
                         "fp16": "",
                         "fp32": "",
                         "bf16": ""
@@ -21250,18 +21250,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Silver 4216R CPU-only",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ Ultra 7 processor 165H NPU-only",
+        "Model": "yolo_v8n",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
-                        "fp16": "",
-                        "fp32": 4.37,
+                        "int8": 127.62,
+                        "fp16": 131.24,
+                        "fp32": "",
                         "bf16": ""
                     }
                 ],
@@ -21272,7 +21272,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
+                        "int8": 8.95,
                         "fp16": "",
                         "fp32": "",
                         "bf16": ""
@@ -21284,18 +21284,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Silver 4216R CPU-only",
-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ Ultra 9 processor 288V NPU-only",
+        "Model": "yolo_v8n",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1997.4,
-                        "fp16": "",
-                        "fp32": 617.19,
+                        "int8": 390.98,
+                        "fp16": 506.71,
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                         "bf16": ""
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@@ -21306,7 +21306,7 @@
                 "Precisions": [
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-                        "int8": 1.69,
+                        "int8": 3.51,
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                         "fp32": "",
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@@ -21318,18 +21318,18 @@
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-        "Platform": "Intel® Xeon® Silver 4216R CPU-only",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Atom® x7425E iGPU-only",
+        "Model": "yolo_v8n",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 27.5,
-                        "fp16": "",
-                        "fp32": 7.08,
+                        "int8": 60.91,
+                        "fp16": 44.63,
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                         "bf16": ""
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                 ],
@@ -21340,7 +21340,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 73.55,
+                        "int8": 18.54,
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@@ -21352,18 +21352,18 @@
         }
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     {
-        "Platform": "Intel® Xeon® Silver 4216R CPU-only",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Atom® X6425E iGPU-only",
+        "Model": "yolo_v8n",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1007.33,
-                        "fp16": "",
-                        "fp32": 320.41,
+                        "int8": 31.72,
+                        "fp16": 33.09,
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                 ],
@@ -21374,7 +21374,7 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 2.68,
+                        "int8": 35.85,
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                         "bf16": ""
@@ -21386,18 +21386,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Silver 4216R CPU-only",
+        "Platform": "Intel® Celeron® 6305E iGPU-only",
         "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 425.85,
-                        "fp16": "",
-                        "fp32": 165.87,
+                        "int8": 121.95,
+                        "fp16": 81.83,
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@@ -21408,7 +21408,7 @@
                 "Precisions": [
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                         "int4": "",
-                        "int8": 6.27,
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@@ -21420,18 +21420,52 @@
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-        "Platform": "Intel® Xeon® Silver 4316 CPU-only",
-        "Model": "bert-base-cased",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i5-1235U Processor iGPU-only",
+        "Model": "yolo_v8n",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 435.85,
+                        "int8": 130.79,
+                        "fp16": 80.07,
+                        "fp32": "",
+                        "bf16": ""
+                    }
+                ],
+                "Unit": "FPS",
+                "UnitDesc": "higher is better"
+            },
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+                        "int8": 8.12,
                         "fp16": "",
-                        "fp32": 167.84,
+                        "fp32": "",
+                        "bf16": ""
+                    }
+                ],
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+                "UnitDesc": "lower is better"
+            }
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+    },
+    {
+        "Platform": "Intel® Core™ i5-1335U Processor iGPU-only",
+        "Model": "yolo_v8n",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Parameters": {
+            "throughput": {
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+                        "int8": 133.28,
+                        "fp16": 83.52,
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@@ -21442,7 +21476,7 @@
                 "Precisions": [
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@@ -21454,18 +21488,52 @@
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-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i7-1185G7 iGPU-only",
+        "Model": "yolo_v8n",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 39.05,
+                        "int8": 164.13,
+                        "fp16": 112.15,
+                        "fp32": "",
+                        "bf16": ""
+                    }
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+                "Unit": "FPS",
+                "UnitDesc": "higher is better"
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                         "fp16": "",
-                        "fp32": 15.17,
+                        "fp32": "",
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+                    }
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+    {
+        "Platform": "Intel® Core™ i7-1185GRE iGPU-only",
+        "Model": "yolo_v8n",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Parameters": {
+            "throughput": {
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+                        "int8": 117.62,
+                        "fp16": 77.67,
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                 ],
@@ -21476,7 +21544,7 @@
                 "Precisions": [
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-                        "int8": 62.3,
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@@ -21488,18 +21556,52 @@
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     {
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-        "Model": "efficientdet-d0",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i7-12700H iGPU-only",
+        "Model": "yolo_v8n",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 500.81,
+                        "int8": 218.62,
+                        "fp16": 149.58,
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+                    }
+                ],
+                "Unit": "FPS",
+                "UnitDesc": "higher is better"
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+                        "int8": 5.56,
                         "fp16": "",
-                        "fp32": 302.76,
+                        "fp32": "",
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+                    }
+                ],
+                "Unit": "ms",
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+            }
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+    {
+        "Platform": "Intel® Core™ i7-1355U Processor iGPU-only",
+        "Model": "yolo_v8n",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Parameters": {
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+                        "int8": 164.07,
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@@ -21510,7 +21612,7 @@
                 "Precisions": [
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-                        "int8": 5.83,
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@@ -21522,18 +21624,2466 @@
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-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i7-1360P iGPU-only",
+        "Model": "yolo_v8n",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
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+                        "int8": "",
+                        "fp16": "",
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+                        "bf16": ""
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+                "Unit": "FPS",
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+                    }
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+        }
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+        "Platform": "Intel® Processor N100 iGPU-only",
+        "Model": "yolo_v8n",
+        "Checked": "true",
+        "PlatformType": "Mobile Platforms (Intel® Atom™)",
+        "Parameters": {
+            "throughput": {
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+                        "int8": 47.04,
+                        "fp16": 34.98,
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+        "Model": "yolo_v8n",
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+        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
+        "Model": "mistral-7b-v0.1",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
+        "Parameters": {
+            "throughput": {
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+                        "int8": "",
+                        "fp16": "",
+                        "fp32": "",
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+                "UnitDesc": "lower is better"
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+        "Model": "mistral-7b-v0.1",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Parameters": {
+            "throughput": {
+                "Precisions": [
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+                        "int4": 5.8,
+                        "int8": "",
+                        "fp16": "",
+                        "fp32": "",
+                        "bf16": ""
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+                "UnitDesc": "higher is better"
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+                        "int8": "",
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+                        "fp32": "",
+                        "bf16": ""
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+                "UnitDesc": "lower is better"
+            }
+        }
+    },
+    {
+        "Platform": "Intel® Core™ Ultra 7 processor 165H iGPU-only",
+        "Model": "mistral-7b-v0.1",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Parameters": {
+            "throughput": {
+                "Precisions": [
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+                        "int4": 14.6,
+                        "int8": 9.3,
+                        "fp16": "",
+                        "fp32": "",
+                        "bf16": ""
+                    }
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+                "UnitDesc": "higher is better"
+            },
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+                        "int8": 108.02,
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+                        "fp32": "",
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+                    }
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+                "UnitDesc": "lower is better"
+            }
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+        "Platform": "Intel® Core™ Ultra 9 processor 288V iGPU-only",
+        "Model": "mistral-7b-v0.1",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
+        "Parameters": {
+            "throughput": {
+                "Precisions": [
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+                        "int4": 16.7,
+                        "int8": 10.2,
+                        "fp16": 5.7,
+                        "fp32": "",
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+                "UnitDesc": "higher is better"
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+                        "int8": 98.33,
+                        "fp16": 176.33,
+                        "fp32": "",
+                        "bf16": ""
+                    }
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+                "Unit": "ms",
+                "UnitDesc": "lower is better"
+            }
+        }
+    },
+    {
+        "Platform": "Intel® Core™ i9-13900K CPU-only",
+        "Model": "phi-3-mini-4k-instruct",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
-                        "int4": "",
-                        "int8": 7.22,
-                        "fp16": "",
-                        "fp32": 1.76,
+                        "int4": 23.3,
+                        "int8": 15.6,
+                        "fp16": 8.5,
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                         "bf16": ""
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                 ],
@@ -21543,9 +24093,9 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": "",
-                        "int8": 281.71,
-                        "fp16": "",
+                        "int4": 42.95,
+                        "int8": 64.22,
+                        "fp16": 117.2,
                         "fp32": "",
                         "bf16": ""
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@@ -21556,18 +24106,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Silver 4316 CPU-only",
-        "Model": "mobilenet-v2",
-        "Checked": true,
+        "Platform": "Intel® Xeon® Platinum 8380 CPU-only",
+        "Model": "phi-3-mini-4k-instruct",
+        "Checked": "true",
         "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
-                        "int4": "",
-                        "int8": 12272.08,
-                        "fp16": "",
-                        "fp32": 3591.32,
+                        "int4": 40.8,
+                        "int8": 29.1,
+                        "fp16": 17.1,
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                         "bf16": ""
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@@ -21577,9 +24127,9 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": "",
-                        "int8": 0.63,
-                        "fp16": "",
+                        "int4": 24.53,
+                        "int8": 34.38,
+                        "fp16": 58.33,
                         "fp32": "",
                         "bf16": ""
                     }
@@ -21590,18 +24140,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Silver 4316 CPU-only",
-        "Model": "resnet-50",
-        "Checked": true,
+        "Platform": "Intel® Xeon® Platinum 8480+ CPU-only",
+        "Model": "phi-3-mini-4k-instruct",
+        "Checked": "true",
         "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
-                        "int4": "",
-                        "int8": 2264.37,
-                        "fp16": "",
-                        "fp32": 569.28,
+                        "int4": 43.6,
+                        "int8": 33.3,
+                        "fp16": 22.0,
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                         "bf16": ""
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@@ -21611,9 +24161,9 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": "",
-                        "int8": 1.55,
-                        "fp16": "",
+                        "int4": 22.94,
+                        "int8": 30.03,
+                        "fp16": 45.44,
                         "fp32": "",
                         "bf16": ""
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@@ -21624,18 +24174,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® Silver 4316 CPU-only",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
+        "Platform": "Intel® Xeon® Platinum 8580 CPU-only",
+        "Model": "phi-3-mini-4k-instruct",
+        "Checked": "true",
         "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
-                        "int4": "",
-                        "int8": "",
-                        "fp16": "",
-                        "fp32": 10.56,
+                        "int4": 52.2,
+                        "int8": 39.0,
+                        "fp16": 25.9,
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                         "bf16": ""
                     }
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@@ -21645,9 +24195,9 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": "",
-                        "int8": "",
-                        "fp16": "",
+                        "int4": 19.15,
+                        "int8": 25.66,
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                         "fp32": "",
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                     }
@@ -21658,18 +24208,18 @@
         }
     },
     {
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-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ Ultra 7 processor 165H CPU-only",
+        "Model": "phi-3-mini-4k-instruct",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
-                        "int4": "",
-                        "int8": 4708.99,
-                        "fp16": "",
-                        "fp32": 1235.24,
+                        "int4": 11.6,
+                        "int8": 8.6,
+                        "fp16": 6.9,
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                         "bf16": ""
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@@ -21679,9 +24229,9 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": "",
-                        "int8": 1.01,
-                        "fp16": "",
+                        "int4": 86.42,
+                        "int8": 116.68,
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                         "fp32": "",
                         "bf16": ""
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@@ -21692,18 +24242,18 @@
         }
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     {
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-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Data Center GPU Flex 170 dGPU",
+        "Model": "phi-3-mini-4k-instruct",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
-                        "int4": "",
-                        "int8": 70.37,
-                        "fp16": "",
-                        "fp32": 16.53,
+                        "int4": 55.6,
+                        "int8": 46.6,
+                        "fp16": 31.6,
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                         "bf16": ""
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@@ -21713,9 +24263,9 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": "",
-                        "int8": 29.89,
-                        "fp16": "",
+                        "int4": 17.98,
+                        "int8": 21.47,
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@@ -21726,18 +24276,18 @@
         }
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     {
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-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
+        "Model": "phi-3-mini-4k-instruct",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
-                        "int4": "",
-                        "int8": 2232.73,
+                        "int4": 41.9,
+                        "int8": "",
                         "fp16": "",
-                        "fp32": 696.62,
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                         "bf16": ""
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@@ -21747,8 +24297,8 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": "",
-                        "int8": 1.39,
+                        "int4": 23.88,
+                        "int8": "",
                         "fp16": "",
                         "fp32": "",
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@@ -21760,18 +24310,18 @@
         }
     },
     {
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-        "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
+        "Model": "phi-3-mini-4k-instruct",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
-                        "int4": "",
-                        "int8": 868.1,
+                        "int4": 18.8,
+                        "int8": 10.7,
                         "fp16": "",
-                        "fp32": 341.49,
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                         "bf16": ""
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                 ],
@@ -21781,8 +24331,8 @@
             "latency": {
                 "Precisions": [
                     {
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-                        "int8": 3.33,
+                        "int4": 53.33,
+                        "int8": 93.53,
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@@ -21794,18 +24344,18 @@
         }
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     {
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-        "Model": "bert-base-cased",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i7-1360P iGPU-only",
+        "Model": "phi-3-mini-4k-instruct",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
-                        "int4": "",
-                        "int8": 52.43,
+                        "int4": 9.7,
+                        "int8": 6.1,
                         "fp16": "",
-                        "fp32": 36.18,
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                 ],
@@ -21815,8 +24365,8 @@
             "latency": {
                 "Precisions": [
                     {
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-                        "int8": 27.41,
+                        "int4": 102.87,
+                        "int8": 165.24,
                         "fp16": "",
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@@ -21828,18 +24378,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® W1290P CPU-only",
-        "Model": "bert-large-uncased-whole-word-masking-squad-0001",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ Ultra 7 processor 165H iGPU-only",
+        "Model": "phi-3-mini-4k-instruct",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
-                        "int4": "",
-                        "int8": 5.15,
-                        "fp16": "",
-                        "fp32": 3.32,
+                        "int4": 23.1,
+                        "int8": 15.7,
+                        "fp16": 9.1,
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                         "bf16": ""
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@@ -21849,9 +24399,9 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": "",
-                        "int8": 257.26,
-                        "fp16": "",
+                        "int4": 43.28,
+                        "int8": 63.82,
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                         "fp32": "",
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@@ -21862,18 +24412,18 @@
         }
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     {
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-        "Model": "efficientdet-d0",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ Ultra 9 processor 288V iGPU-only",
+        "Model": "phi-3-mini-4k-instruct",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
-                        "int4": "",
-                        "int8": 96.11,
-                        "fp16": "",
-                        "fp32": 46.96,
+                        "int4": 25.6,
+                        "int8": 16.7,
+                        "fp16": 10.3,
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                         "bf16": ""
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                 ],
@@ -21883,9 +24433,9 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": "",
-                        "int8": 13.55,
-                        "fp16": "",
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+                        "int8": 59.98,
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                         "fp32": "",
                         "bf16": ""
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@@ -21896,18 +24446,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® W1290P CPU-only",
-        "Model": "mask_rcnn_resnet50_atrous_coco",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i9-13900K CPU-only",
+        "Model": "stable-diffusion-v1-5",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 0.75,
+                        "int8": "",
                         "fp16": "",
-                        "fp32": 0.28,
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                 ],
@@ -21918,8 +24468,8 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1441.58,
-                        "fp16": "",
+                        "int8": 42.87,
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@@ -21930,18 +24480,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® W1290P CPU-only",
-        "Model": "mobilenet-v2",
-        "Checked": true,
+        "Platform": "Intel® Xeon® Platinum 8380 CPU-only",
+        "Model": "stable-diffusion-v1-5",
+        "Checked": "true",
         "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1446.3,
+                        "int8": "",
                         "fp16": "",
-                        "fp32": 663.43,
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                 ],
@@ -21952,8 +24502,8 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 1.28,
-                        "fp16": "",
+                        "int8": 14.24,
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                         "fp32": "",
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@@ -21964,18 +24514,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® W1290P CPU-only",
-        "Model": "resnet-50",
-        "Checked": true,
+        "Platform": "Intel® Xeon® Platinum 8480+ CPU-only",
+        "Model": "stable-diffusion-v1-5",
+        "Checked": "true",
         "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 244.84,
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                         "fp16": "",
-                        "fp32": 123.21,
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                     }
                 ],
@@ -21986,8 +24536,8 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 5.34,
-                        "fp16": "",
+                        "int8": 4.67,
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                         "fp32": "",
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@@ -21998,9 +24548,9 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® W1290P CPU-only",
-        "Model": "ssd-resnet34-1200",
-        "Checked": true,
+        "Platform": "Intel® Xeon® Platinum 8580 CPU-only",
+        "Model": "stable-diffusion-v1-5",
+        "Checked": "true",
         "PlatformType": "Server Platforms (Intel® Xeon®)",
         "Parameters": {
             "throughput": {
@@ -22009,7 +24559,7 @@
                         "int4": "",
                         "int8": "",
                         "fp16": "",
-                        "fp32": 2.3,
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                         "bf16": ""
                     }
                 ],
@@ -22020,8 +24570,8 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": "",
-                        "fp16": "",
+                        "int8": 4.13,
+                        "fp16": 4.04,
                         "fp32": "",
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@@ -22032,18 +24582,18 @@
         }
     },
     {
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-        "Model": "ssd_mobilenet_v1_coco",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Arc™ A-Series Graphics dGPU",
+        "Model": "stable-diffusion-v1-5",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 577.33,
+                        "int8": "",
                         "fp16": "",
-                        "fp32": 271.36,
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                     }
                 ],
@@ -22054,8 +24604,8 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 2.36,
-                        "fp16": "",
+                        "int8": 2.55,
+                        "fp16": 2.47,
                         "fp32": "",
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@@ -22066,18 +24616,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® W1290P CPU-only",
-        "Model": "unet-camvid-onnx-0001",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Data Center GPU Flex 140 dGPU",
+        "Model": "stable-diffusion-v1-5",
+        "Checked": "true",
+        "PlatformType": "Accelerator Platforms",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 6.16,
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                         "fp16": "",
-                        "fp32": 3.31,
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                     }
                 ],
@@ -22088,8 +24638,8 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 176.77,
-                        "fp16": "",
+                        "int8": 10.8,
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                         "fp32": "",
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@@ -22100,18 +24650,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® W1290P CPU-only",
-        "Model": "yolo_v3_tiny",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ i7-1360P iGPU-only",
+        "Model": "stable-diffusion-v1-5",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 298.95,
+                        "int8": "",
                         "fp16": "",
-                        "fp32": 120.89,
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                     }
                 ],
@@ -22122,8 +24672,8 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 3.96,
-                        "fp16": "",
+                        "int8": 33.5,
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@@ -22134,18 +24684,18 @@
         }
     },
     {
-        "Platform": "Intel® Xeon® W1290P CPU-only",
-        "Model": "yolo_v8n",
-        "Checked": true,
-        "PlatformType": "Server Platforms (Intel® Xeon®)",
+        "Platform": "Intel® Core™ Ultra 9 processor 288V iGPU-only",
+        "Model": "stable-diffusion-v1-5",
+        "Checked": "true",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 134.9,
+                        "int8": "",
                         "fp16": "",
-                        "fp32": 72.23,
+                        "fp32": "",
                         "bf16": ""
                     }
                 ],
@@ -22156,8 +24706,8 @@
                 "Precisions": [
                     {
                         "int4": "",
-                        "int8": 9.02,
-                        "fp16": "",
+                        "int8": 5.67,
+                        "fp16": 5.33,
                         "fp32": "",
                         "bf16": ""
                     }
@@ -22168,10 +24718,10 @@
         }
     },
     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V CPU-only",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V CPU-only",
         "Model": "bert-base-cased",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
@@ -22202,10 +24752,10 @@
         }
     },
     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V CPU-only",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V CPU-only",
         "Model": "bert-large-uncased",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
@@ -22236,10 +24786,10 @@
         }
     },
     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V CPU-only",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V CPU-only",
         "Model": "efficientdet-d0",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
@@ -22270,10 +24820,10 @@
         }
     },
     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V CPU-only",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V CPU-only",
         "Model": "mask_rcnn_resnet50_atrous_coco",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
@@ -22304,10 +24854,10 @@
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     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V CPU-only",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V CPU-only",
         "Model": "mobilenet-v2",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -22338,10 +24888,10 @@
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     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V CPU-only",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V CPU-only",
         "Model": "resnet-50",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -22372,10 +24922,10 @@
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     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V CPU-only",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V CPU-only",
         "Model": "ssd_mobilenet_v1_coco",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -22406,10 +24956,10 @@
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     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V CPU-only",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V CPU-only",
         "Model": "ssd-resnet34-1200",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -22440,10 +24990,10 @@
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     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V CPU-only",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V CPU-only",
         "Model": "unet-camvid-onnx-0001",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -22474,10 +25024,10 @@
         }
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     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V CPU-only",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V CPU-only",
         "Model": "yolo_v5m",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -22508,10 +25058,10 @@
         }
     },
     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V CPU-only",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V CPU-only",
         "Model": "yolo_v8n",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -22542,10 +25092,10 @@
         }
     },
     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V iGPU",
         "Model": "bert-base-cased",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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@@ -22576,10 +25126,10 @@
         }
     },
     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V iGPU",
         "Model": "bert-large-uncased",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -22610,10 +25160,10 @@
         }
     },
     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V iGPU",
         "Model": "efficientdet-d0",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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@@ -22644,10 +25194,10 @@
         }
     },
     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V iGPU",
         "Model": "mask_rcnn_resnet50_atrous_coco",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -22678,10 +25228,10 @@
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     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V iGPU",
         "Model": "mobilenet-v2",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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@@ -22712,10 +25262,10 @@
         }
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     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V iGPU",
         "Model": "resnet-50",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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@@ -22746,10 +25296,10 @@
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     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V iGPU",
         "Model": "ssd_mobilenet_v1_coco",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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@@ -22780,10 +25330,10 @@
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     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V iGPU",
         "Model": "ssd-resnet34-1200",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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@@ -22814,10 +25364,10 @@
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     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V iGPU",
         "Model": "unet-camvid-onnx-0001",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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@@ -22848,10 +25398,10 @@
         }
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     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V iGPU",
         "Model": "yolo_v5m",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -22882,10 +25432,10 @@
         }
     },
     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V iGPU",
         "Model": "yolo_v8n",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
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@@ -22916,10 +25466,10 @@
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     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V CPU+iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V CPU+iGPU",
         "Model": "bert-base-cased",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -22950,10 +25500,10 @@
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     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V CPU+iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V CPU+iGPU",
         "Model": "bert-large-uncased",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -22984,10 +25534,10 @@
         }
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     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V CPU+iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V CPU+iGPU",
         "Model": "efficientdet-d0",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -23018,10 +25568,10 @@
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     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V CPU+iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V CPU+iGPU",
         "Model": "mask_rcnn_resnet50_atrous_coco",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -23052,10 +25602,10 @@
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     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V CPU+iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V CPU+iGPU",
         "Model": "mobilenet-v2",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -23086,10 +25636,10 @@
         }
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     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V CPU+iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V CPU+iGPU",
         "Model": "resnet-50",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -23120,10 +25670,10 @@
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     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V CPU+iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V CPU+iGPU",
         "Model": "ssd_mobilenet_v1_coco",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -23154,10 +25704,10 @@
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     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V CPU+iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V CPU+iGPU",
         "Model": "ssd-resnet34-1200",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -23188,10 +25738,10 @@
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     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V CPU+iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V CPU+iGPU",
         "Model": "unet-camvid-onnx-0001",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
@@ -23222,10 +25772,10 @@
         }
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     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V CPU+iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V CPU+iGPU",
         "Model": "yolo_v5m",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -23256,10 +25806,10 @@
         }
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     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V CPU+iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V CPU+iGPU",
         "Model": "yolo_v8n",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
@@ -23290,10 +25840,10 @@
         }
     },
     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V NPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V NPU",
         "Model": "bert-base-cased",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
@@ -23324,10 +25874,10 @@
         }
     },
     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V NPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V NPU",
         "Model": "bert-large-uncased",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
@@ -23358,10 +25908,10 @@
         }
     },
     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V NPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V NPU",
         "Model": "efficientdet-d0",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -23392,10 +25942,10 @@
         }
     },
     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V NPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V NPU",
         "Model": "mask_rcnn_resnet50_atrous_coco",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
@@ -23426,10 +25976,10 @@
         }
     },
     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V NPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V NPU",
         "Model": "mobilenet-v2",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
@@ -23460,10 +26010,10 @@
         }
     },
     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V NPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V NPU",
         "Model": "resnet-50",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -23494,10 +26044,10 @@
         }
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     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V NPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V NPU",
         "Model": "ssd_mobilenet_v1_coco",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
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@@ -23528,10 +26078,10 @@
         }
     },
     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V NPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V NPU",
         "Model": "ssd-resnet34-1200",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
@@ -23562,10 +26112,10 @@
         }
     },
     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V NPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V NPU",
         "Model": "unet-camvid-onnx-0001",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
@@ -23596,10 +26146,10 @@
         }
     },
     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V NPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V NPU",
         "Model": "yolo_v5m",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
@@ -23630,10 +26180,10 @@
         }
     },
     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V NPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V NPU",
         "Model": "yolo_v8n",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
@@ -23664,15 +26214,15 @@
         }
     },
     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V iGPU",
         "Model": "chatglm2-6b",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
-                        "int4": 14.8,
+                        "int4": 20.6,
                         "int8": "",
                         "fp16": "",
                         "fp32": "",
@@ -23685,7 +26235,7 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": 67,
+                        "int4": 48,
                         "int8": "",
                         "fp16": "",
                         "fp32": "",
@@ -23698,15 +26248,15 @@
         }
     },
     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V iGPU",
         "Model": "falcon-7b-instruct",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
-                        "int4": 13.4,
+                        "int4": 18.8,
                         "int8": "",
                         "fp16": "",
                         "fp32": "",
@@ -23719,7 +26269,7 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": 74,
+                        "int4": 53,
                         "int8": "",
                         "fp16": "",
                         "fp32": "",
@@ -23732,15 +26282,15 @@
         }
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     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V iGPU",
         "Model": "llama-2-7b-chat-hf",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
-                        "int4": 11.8,
+                        "int4": 17.5,
                         "int8": "",
                         "fp16": "",
                         "fp32": "",
@@ -23753,7 +26303,7 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": 84,
+                        "int4": 57,
                         "int8": "",
                         "fp16": "",
                         "fp32": "",
@@ -23766,15 +26316,15 @@
         }
     },
     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V iGPU",
         "Model": "llama-3-8b",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
-                        "int4": 12.9,
+                        "int4": 19.2,
                         "int8": "",
                         "fp16": "",
                         "fp32": "",
@@ -23787,7 +26337,7 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": 77,
+                        "int4": 52,
                         "int8": "",
                         "fp16": "",
                         "fp32": "",
@@ -23800,15 +26350,15 @@
         }
     },
     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V iGPU",
         "Model": "mistral-7b-v0.1",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
-                        "int4": 14.3,
+                        "int4": 18.2,
                         "int8": "",
                         "fp16": "",
                         "fp32": "",
@@ -23821,7 +26371,7 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": 69,
+                        "int4": 54,
                         "int8": "",
                         "fp16": "",
                         "fp32": "",
@@ -23834,15 +26384,15 @@
         }
     },
     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V iGPU",
         "Model": "phi-3-mini-4k-instruct",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [
                     {
-                        "int4": 23.5,
+                        "int4": 29.5,
                         "int8": "",
                         "fp16": "",
                         "fp32": "",
@@ -23855,7 +26405,7 @@
             "latency": {
                 "Precisions": [
                     {
-                        "int4": 42,
+                        "int4": 34,
                         "int8": "",
                         "fp16": "",
                         "fp32": "",
@@ -23868,10 +26418,10 @@
         }
     },
     {
-        "Platform": "Intel(R) Core(TM) Ultra 7 processor 268V iGPU",
+        "Platform": "Intel® Core™ Ultra 7 processor 268V iGPU",
         "Model": "stable-diffusion-v1-5",
         "Checked": "true",
-        "PlatformType": "Client Platforms (Intel(R) Core(TM))",
+        "PlatformType": "Client Platforms (Intel® Core™)",
         "Parameters": {
             "throughput": {
                 "Precisions": [

From 42b7322d3575677193309600e35691f4671e3244 Mon Sep 17 00:00:00 2001
From: Andrei Kashchikhin <andrey.kashchikhin@intel.com>
Date: Thu, 19 Sep 2024 16:52:01 +0100
Subject: [PATCH 17/49] [CI] [GHA] Add `lost communication with the server` to
 errors to look for in rerunner (#26691)

### Tickets:
 - *152565*
---
 .github/scripts/workflow_rerun/errors_to_look_for.json | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/.github/scripts/workflow_rerun/errors_to_look_for.json b/.github/scripts/workflow_rerun/errors_to_look_for.json
index bfde22bb70c6df..aecef0c49c71b3 100644
--- a/.github/scripts/workflow_rerun/errors_to_look_for.json
+++ b/.github/scripts/workflow_rerun/errors_to_look_for.json
@@ -74,5 +74,9 @@
     {
         "error_text": "json.decoder.JSONDecodeError: Unterminated string starting at",
         "ticket": 151796
+    },
+    {
+        "error_text": "lost communication with the server",
+        "ticket": 152565
     }
 ]
\ No newline at end of file

From b4af3d53291aaf37dc6cb1b211556654621b348c Mon Sep 17 00:00:00 2001
From: Alicja Miloszewska <alicja.miloszewska@intel.com>
Date: Fri, 20 Sep 2024 06:42:34 +0200
Subject: [PATCH 18/49] [OV JS] npm run lint on .js files (#26543)

### Details:
 - include *.js files
 - Fix codestyle errors about max-len, snake-case etc.

### Tickets:
 - *ticket-id*
---
 src/bindings/js/.eslintrc-global.js           |   3 +-
 src/bindings/js/.eslintrc.js                  |   2 +-
 src/bindings/js/node/.eslintrc.js             |   3 +
 src/bindings/js/node/lib/addon.ts             |  98 ++++----
 .../js/node/scripts/download_runtime.js       |  58 +++--
 .../node/tests/e2e/demo-electron-app/index.js |   1 +
 .../js/node/tests/e2e/electron-app.test.js    |  25 +-
 src/bindings/js/node/tests/unit/basic.test.js | 144 ++++++-----
 .../js/node/tests/unit/compiled_model.test.js |  34 +--
 src/bindings/js/node/tests/unit/core.test.js  |  17 +-
 .../js/node/tests/unit/infer_request.test.js  | 197 +++++++--------
 src/bindings/js/node/tests/unit/model.test.js |  98 ++++----
 .../tests/unit/pre_post_processor.test.js     | 226 ++++++++++++------
 .../js/node/tests/unit/read_model.test.js     |  22 +-
 .../js/node/tests/unit/tensor.test.js         | 176 ++++++++------
 src/bindings/js/node/tests/unit/utils.js      |  12 +-
 16 files changed, 632 insertions(+), 484 deletions(-)

diff --git a/src/bindings/js/.eslintrc-global.js b/src/bindings/js/.eslintrc-global.js
index 623b8ba549a00a..7f4abc54561495 100644
--- a/src/bindings/js/.eslintrc-global.js
+++ b/src/bindings/js/.eslintrc-global.js
@@ -2,7 +2,7 @@ module.exports = {
   rules: {
     'semi': ['error'],
     'no-var': ['error'],
-    'max-len': ['error'],
+    'max-len': ['error', { 'ignoreUrls': true }],
     'eol-last': ['error'],
     'indent': ['error', 2],
     'camelcase': ['error'],
@@ -23,5 +23,6 @@ module.exports = {
     'key-spacing': ['error', { beforeColon: false }],
     'no-multiple-empty-lines': ['error', { max: 1, maxBOF: 0, maxEOF: 0 }],
     'keyword-spacing': ['error', { overrides: { catch: { after: false } } }],
+    '@typescript-eslint/no-var-requires': 0,
   }
 };
diff --git a/src/bindings/js/.eslintrc.js b/src/bindings/js/.eslintrc.js
index 8cb6464d27ddcc..5958104aa92946 100644
--- a/src/bindings/js/.eslintrc.js
+++ b/src/bindings/js/.eslintrc.js
@@ -4,6 +4,6 @@ module.exports = {
     'plugin:@typescript-eslint/recommended',
     './.eslintrc-global.js',
   ],
-  ignorePatterns: ['**/*.js', 'node_modules/', 'types/', 'dist/', 'bin/'],
+  ignorePatterns: ['node_modules/', 'types/', 'dist/', 'bin/', '.eslintrc.js'],
   root: true,
 };
diff --git a/src/bindings/js/node/.eslintrc.js b/src/bindings/js/node/.eslintrc.js
index 992f409c909343..24ba06e7821d43 100644
--- a/src/bindings/js/node/.eslintrc.js
+++ b/src/bindings/js/node/.eslintrc.js
@@ -1,5 +1,8 @@
 module.exports = {
   extends: ['../.eslintrc.js'],
+  env: {
+    node: true,
+  },
   parser: '@typescript-eslint/parser',
   plugins: ['@typescript-eslint'],
 };
diff --git a/src/bindings/js/node/lib/addon.ts b/src/bindings/js/node/lib/addon.ts
index c2ba1d9c61f1f9..060af2cfec92e8 100644
--- a/src/bindings/js/node/lib/addon.ts
+++ b/src/bindings/js/node/lib/addon.ts
@@ -48,7 +48,7 @@ interface Core {
   compileModel(
     model: Model,
     deviceName: string,
-    config?: { [propertyName: string]: string }
+    config?: { [propertyName: string]: string },
   ): Promise<CompiledModel>;
   /**
    * Asynchronously reads a model and creates a compiled model
@@ -67,7 +67,7 @@ interface Core {
   compileModel(
     modelPath: string,
     deviceName: string,
-    config?: { [propertyName: string]: string }
+    config?: { [propertyName: string]: string },
   ): Promise<CompiledModel>;
   /**
    * A synchronous version of {@link Core.compileModel}.
@@ -76,7 +76,7 @@ interface Core {
   compileModelSync(
     model: Model,
     deviceName: string,
-    config?: { [propertyName: string]: string }
+    config?: { [propertyName: string]: string },
   ): CompiledModel;
   /**
    * A synchronous version of {@link Core.compileModel}.
@@ -85,7 +85,7 @@ interface Core {
   compileModelSync(
     modelPath: string,
     deviceName: string,
-    config?: { [propertyName: string]: string }
+    config?: { [propertyName: string]: string },
   ): CompiledModel;
   /**
    * It returns a list of available inference devices.
@@ -118,9 +118,9 @@ interface Core {
    */
   getVersions(deviceName: string): {
     [deviceName: string]: {
-      buildNumber: string,
-      description: string,
-    },
+      buildNumber: string;
+      description: string;
+    };
   };
   /**
    * Asynchronously imports a previously exported compiled model.
@@ -135,7 +135,7 @@ interface Core {
   importModel(
     modelStream: Buffer,
     device: string,
-    config?: { [key: string]: string | number | boolean }
+    config?: { [key: string]: string | number | boolean },
   ): Promise<CompiledModel>;
   /**
    * A synchronous version of {@link Core.importModel}.
@@ -144,7 +144,7 @@ interface Core {
   importModelSync(
     modelStream: Buffer,
     device: string,
-    config?: { [key: string]: string | number | boolean }
+    config?: { [key: string]: string | number | boolean },
   ): CompiledModel;
   /**
    * It reads models from the IR / ONNX / PDPD / TF and TFLite formats.
@@ -175,7 +175,9 @@ interface Core {
    * @param weightsBuffer Binary data with tensor data.
    */
   readModel(
-    modelBuffer: Uint8Array, weightsBuffer?: Uint8Array): Promise<Model>;
+    modelBuffer: Uint8Array,
+    weightsBuffer?: Uint8Array,
+  ): Promise<Model>;
   /**
    * A synchronous version of {@link Core.readModel}.
    * It reads models from the IR / ONNX / PDPD / TF and TFLite formats.
@@ -220,7 +222,7 @@ interface Core {
   ): {[key: string]: string | number | boolean};
 }
 interface CoreConstructor {
-  new(): Core;
+  new (): Core;
 }
 
 /**
@@ -277,9 +279,9 @@ interface Model {
    */
   isDynamic(): boolean;
   /**
- * It gets the output of the model.
- * If a model has more than one output, this method throws an exception.
- */
+   * It gets the output of the model.
+   * If a model has more than one output, this method throws an exception.
+   */
   output(): Output;
   /**
    * It gets the output of the model identified by the tensor name.
@@ -372,15 +374,15 @@ interface CompiledModel {
    * @returns {Output} A compiled model input.
    */
   input(name: string): Output;
-   /**
+  /**
    * It sets properties for the current compiled model. Properties
    * can be retrieved via {@link CompiledModel.getProperty}.
    * @param property An object with the key-value pairs.
    * (property name, property value)
    */
-   setProperty(properties: {
-      [propertyName: string]: string | number | boolean
-    }): void;
+  setProperty(properties: {
+    [propertyName: string]: string | number | boolean;
+  }): void;
 }
 
 /**
@@ -438,7 +440,7 @@ interface TensorConstructor {
    * @param type The element type of the new tensor.
    * @param shape The shape of the new tensor.
    */
-  new(type: element | elementTypeString, shape: number[]): Tensor;
+  new (type: element | elementTypeString, shape: number[]): Tensor;
   /**
    * It constructs a tensor using the element type and shape. The new tensor
    * wraps allocated host memory.
@@ -447,14 +449,17 @@ interface TensorConstructor {
    * @param tensorData A subclass of TypedArray that will be wrapped
    * by a {@link Tensor}.
    */
-  new(type: element | elementTypeString, shape: number[],
-    tensorData: SupportedTypedArray): Tensor;
+  new (
+    type: element | elementTypeString,
+    shape: number[],
+    tensorData: SupportedTypedArray,
+  ): Tensor;
   /**
    * It constructs a tensor using the element type and shape. The strings from
    * the array are used to fill the new tensor. Each element of a string tensor
    * is a string of arbitrary length, including an empty string.
    */
-  new(tensorData: string[]): Tensor;
+  new (tensorData: string[]): Tensor;
 }
 
 /**
@@ -478,8 +483,9 @@ interface InferRequest {
    * TypedArray will be wrapped into Tensor underneath using the input shape
    * and element type of the deployed model.
    */
-  infer(inputData: { [inputName: string]: Tensor | SupportedTypedArray })
-    : { [outputName: string]: Tensor };
+  infer(inputData: { [inputName: string]: Tensor | SupportedTypedArray }): {
+    [outputName: string]: Tensor;
+  };
   /**
    * It infers specified input(s) in the synchronous mode.
    * @param inputData An array with tensors or TypedArrays. TypedArrays will be
@@ -487,16 +493,18 @@ interface InferRequest {
    * of the deployed model. If the model has multiple inputs, the Tensors
    * and TypedArrays must be passed in the correct order.
    */
-  infer(inputData: Tensor[] | SupportedTypedArray[])
-    : { [outputName: string]: Tensor };
+  infer(inputData: Tensor[] | SupportedTypedArray[]): {
+    [outputName: string]: Tensor;
+  };
   /**
    * It infers specified input(s) in the asynchronous mode.
    * @param inputData An object with the key-value pairs where the key is the
    * input name and value is a tensor or an array with tensors. If the model has
    * multiple inputs, the Tensors must be passed in the correct order.
    */
-  inferAsync(inputData: { [inputName: string]: Tensor }
-    | Tensor[]): Promise<{ [outputName: string]: Tensor }>;
+  inferAsync(
+    inputData: { [inputName: string]: Tensor } | Tensor[],
+  ): Promise<{ [outputName: string]: Tensor }>;
   /**
    * It gets the compiled model used by the InferRequest object.
    */
@@ -521,11 +529,11 @@ interface InferRequest {
    */
   getOutputTensor(): Tensor;
   /**
-    * It gets the output tensor for inference.
-    * @param idx An index of the tensor to get.
-    * @returns A tensor at the specified index. If the tensor with the specified
-    * idx is not found, an exception is thrown.
-    */
+   * It gets the output tensor for inference.
+   * @param idx An index of the tensor to get.
+   * @returns A tensor at the specified index. If the tensor with the specified
+   * idx is not found, an exception is thrown.
+   */
   getOutputTensor(idx?: number): Tensor;
   /**
    * It gets an input/output tensor for inference.
@@ -619,7 +627,7 @@ interface PrePostProcessor {
   output(idxOrTensorName?: number | string): OutputInfo;
 }
 interface PrePostProcessorConstructor {
-  new(model: Model): PrePostProcessor;
+  new (model: Model): PrePostProcessor;
 }
 
 interface PartialShape {
@@ -638,7 +646,7 @@ interface PartialShapeConstructor {
    * Omit parameter to create empty shape.
    * @param [shape] String representation of the shape.
    */
-  new(shape?: string): PartialShape;
+  new (shape?: string): PartialShape;
 }
 
 declare enum element {
@@ -662,18 +670,16 @@ declare enum resizeAlgorithm {
 }
 
 export interface NodeAddon {
-  Core: CoreConstructor,
-  Tensor: TensorConstructor,
-  PartialShape: PartialShapeConstructor,
+  Core: CoreConstructor;
+  Tensor: TensorConstructor;
+  PartialShape: PartialShapeConstructor;
 
   preprocess: {
-    resizeAlgorithm: typeof resizeAlgorithm,
-    PrePostProcessor: PrePostProcessorConstructor,
-  },
-  element: typeof element,
+    resizeAlgorithm: typeof resizeAlgorithm;
+    PrePostProcessor: PrePostProcessorConstructor;
+  };
+  element: typeof element;
 }
 
-export default
-  // eslint-disable-next-line @typescript-eslint/no-var-requires
-  require('../bin/ov_node_addon.node') as
-  NodeAddon;
+export default // eslint-disable-next-line @typescript-eslint/no-var-requires
+require('../bin/ov_node_addon.node') as NodeAddon;
diff --git a/src/bindings/js/node/scripts/download_runtime.js b/src/bindings/js/node/scripts/download_runtime.js
index 775b4aa6c754c9..321eb4b125bc6c 100644
--- a/src/bindings/js/node/scripts/download_runtime.js
+++ b/src/bindings/js/node/scripts/download_runtime.js
@@ -25,10 +25,11 @@ async function main() {
 
   try {
     await downloadRuntime(destinationPath, { force, ignoreIfExists, proxy });
-  } catch (error) {
+  } catch(error) {
     if (error instanceof RuntimeExistsError) {
       console.error(
-        `Directory '${destinationPath}' already exists. To force runtime downloading run 'npm run download_runtime -- -f'`
+        `Directory '${destinationPath}' already exists. ` +
+          'To force runtime downloading run \'npm run download_runtime -- -f\'',
       );
     } else {
       throw error;
@@ -52,13 +53,18 @@ class RuntimeExistsError extends Error {
  * @function downloadRuntime
  * @param {string} destinationPath - The destination directory path.
  * @param {Object} [config] - The configuration object.
- * @param {boolean} [config.force=false] - The flag to force install and replace runtime if it exists. Default is `false`.
- * @param {boolean} [config.ignoreIfExists=true] - The flag to skip installation if it exists Default is `true`.
+ * @param {boolean} [config.force=false] - The flag
+ * to force install and replace runtime if it exists. Default is `false`.
+ * @param {boolean} [config.ignoreIfExists=true] - The flag
+ * to skip installation if it exists Default is `true`.
  * @param {string|null} [config.proxy=null] - The proxy URL. Default is `null`.
  * @returns {Promise<void>}
  * @throws {RuntimeExistsError}
  */
-async function downloadRuntime(destinationPath, config = { force: false, ignoreIfExists: true, proxy: null }) {
+async function downloadRuntime(
+  destinationPath,
+  config = { force: false, ignoreIfExists: true, proxy: null },
+) {
   const { version } = packageJson;
   const osInfo = await getOsInfo();
   const isRuntimeDirectoryExists = await checkIfPathExists(destinationPath);
@@ -66,13 +72,16 @@ async function downloadRuntime(destinationPath, config = { force: false, ignoreI
   if (isRuntimeDirectoryExists && !config.force) {
     if (config.ignoreIfExists) {
       console.warn(
-        `Directory '${destinationPath}' already exists. Skipping runtime downloading because 'ignoreIfExists' flag is passed.`
+        `Directory '${destinationPath}' already exists. Skipping ` +
+          'runtime downloading because \'ignoreIfExists\' flag is passed.',
       );
+
       return;
     }
 
     throw new RuntimeExistsError(
-      `Directory '${destinationPath}' already exists. To force runtime downloading use 'force' flag.`
+      `Directory '${destinationPath}' already exists. ` +
+        'To force runtime downloading use \'force\' flag.',
     );
   }
 
@@ -87,7 +96,12 @@ async function downloadRuntime(destinationPath, config = { force: false, ignoreI
     await fs.mkdir(tempDirectoryPath);
 
     console.log('Downloading OpenVINO runtime archive...');
-    await downloadFile(runtimeArchiveUrl, tempDirectoryPath, filename, config.proxy);
+    await downloadFile(
+      runtimeArchiveUrl,
+      tempDirectoryPath,
+      filename,
+      config.proxy,
+    );
     console.log('OpenVINO runtime archive downloaded.');
 
     await removeDirectory(destinationPath);
@@ -96,7 +110,7 @@ async function downloadRuntime(destinationPath, config = { force: false, ignoreI
     await unarchive(archiveFilePath, destinationPath);
 
     console.log('The archive was successfully extracted.');
-  } catch (error) {    
+  } catch(error) {
     console.error(`Failed to download OpenVINO runtime: ${error}.`);
     throw error;
   } finally {
@@ -149,8 +163,9 @@ async function getOsInfo() {
 async function checkIfPathExists(path) {
   try {
     await fs.access(path);
+
     return true;
-  } catch (error) {
+  } catch(error) {
     if (error.code === codeENOENT) {
       return false;
     }
@@ -167,12 +182,12 @@ async function checkIfPathExists(path) {
  * @returns {string}
  */
 function getRuntimeArchiveUrl(version, osInfo) {
-  const { 
+  const {
     host,
     package_name: packageNameTemplate,
     remote_path: remotePathTemplate,
   } = packageJson.binary;
-  const fullPathTemplate = `${remotePathTemplate}${packageNameTemplate}`
+  const fullPathTemplate = `${remotePathTemplate}${packageNameTemplate}`;
   const fullPath = fullPathTemplate
     .replace(new RegExp('{version}', 'g'), version)
     .replace(new RegExp('{platform}', 'g'), osInfo.platform)
@@ -193,7 +208,7 @@ async function removeDirectory(path) {
   try {
     console.log(`Removing ${path}`);
     await fs.rm(path, { recursive: true, force: true });
-  } catch (error) {
+  } catch(error) {
     if (error.code === codeENOENT) console.log(`Path: ${path} doesn't exist`);
 
     throw error;
@@ -215,7 +230,7 @@ function downloadFile(url, destination, filename, proxy = null) {
   const fullPath = path.resolve(destination, filename);
   const file = createWriteStream(fullPath);
 
-  if (new URL(url).protocol === 'http') 
+  if (new URL(url).protocol === 'http')
     throw new Error('Http link doesn\'t support');
 
   let agent;
@@ -271,12 +286,15 @@ function unarchive(tarFilePath, dest) {
   return new Promise((resolve, reject) => {
     createReadStream(tarFilePath)
       .pipe(gunzip())
-      .pipe(tar.extract(dest)
-        .on('finish', () => {
-          resolve();
-        }).on('error', (err) => {
-          reject(err);
-        }),
+      .pipe(
+        tar
+          .extract(dest)
+          .on('finish', () => {
+            resolve();
+          })
+          .on('error', (err) => {
+            reject(err);
+          }),
       );
   });
 }
diff --git a/src/bindings/js/node/tests/e2e/demo-electron-app/index.js b/src/bindings/js/node/tests/e2e/demo-electron-app/index.js
index 9f5fcb8511b868..cfa5fd27b0fa4e 100644
--- a/src/bindings/js/node/tests/e2e/demo-electron-app/index.js
+++ b/src/bindings/js/node/tests/e2e/demo-electron-app/index.js
@@ -3,6 +3,7 @@ const { addon: ov } = require('openvino-node');
 
 app.whenReady().then(() => {
   console.log('Creating OpenVINO Runtime Core');
+  // eslint-disable-next-line @typescript-eslint/no-unused-vars
   const core = new ov.Core();
   console.log('Created OpenVINO Runtime Core');
 
diff --git a/src/bindings/js/node/tests/e2e/electron-app.test.js b/src/bindings/js/node/tests/e2e/electron-app.test.js
index 948146b98225f8..01e84dea884502 100644
--- a/src/bindings/js/node/tests/e2e/electron-app.test.js
+++ b/src/bindings/js/node/tests/e2e/electron-app.test.js
@@ -1,29 +1,36 @@
+/* global describe, it, before, after */
 const fs = require('node:fs');
 const assert = require('node:assert');
 const { exec } = require('child_process');
 
-describe('E2E test of installation openvino as electron dependency', function() {
+describe('E2E testing for OpenVINO as an Electron dependency.', function() {
   this.timeout(50000);
 
   before((done) => {
-    exec('cp -r ./tests/e2e/demo-electron-app/ demo-electron-app-project', (error) => {
-      if (error) {
-        console.error(`exec error: ${error}`);
+    exec(
+      'cp -r ./tests/e2e/demo-electron-app/ demo-electron-app-project',
+      (error) => {
+        if (error) {
+          console.error(`exec error: ${error}`);
 
-        return done(error);
-      }
+          return done(error);
+        }
 
-      done();
-    });
+        done();
+      },
+    );
   });
 
   it('should install dependencies', (done) => {
     exec('cd demo-electron-app-project && npm install', (error) => {
       if (error) {
         console.error(`exec error: ${error}`);
+
         return done(error);
       }
-      const packageJson = JSON.parse(fs.readFileSync('demo-electron-app-project/package-lock.json', 'utf8'));
+      const packageJson = JSON.parse(
+        fs.readFileSync('demo-electron-app-project/package-lock.json', 'utf8'),
+      );
       assert.equal(packageJson.name, 'demo-electron-app');
       done();
     });
diff --git a/src/bindings/js/node/tests/unit/basic.test.js b/src/bindings/js/node/tests/unit/basic.test.js
index d84aabeb91267c..0717b430ae002d 100644
--- a/src/bindings/js/node/tests/unit/basic.test.js
+++ b/src/bindings/js/node/tests/unit/basic.test.js
@@ -9,8 +9,7 @@ const { testModels, getModelPath, isModelAvailable } = require('./utils.js');
 const epsilon = 0.5;
 
 describe('ov basic tests.', () => {
-
-  let testXml = null; 
+  let testXml = null;
   let core = null;
   let model = null;
   let compiledModel = null;
@@ -35,7 +34,6 @@ describe('ov basic tests.', () => {
   });
 
   describe('Core.getVersions()', () => {
-
     it('getVersions(validDeviceName: string)', () => {
       const deviceVersion = core.getVersions('CPU');
       assert.strictEqual(typeof deviceVersion, 'object');
@@ -44,20 +42,15 @@ describe('ov basic tests.', () => {
       assert.strictEqual(typeof deviceVersion.CPU.description, 'string');
     });
 
-    it('getVersions() throws if no arguments are passed into the function', () => {
-      assert.throws(
-        () => core.getVersions(),
-        { message: 'getVersions() method expects 1 argument of string type.' }
-      );
+    it('getVersions() throws if no arguments are passed', () => {
+      assert.throws(() => core.getVersions(), {
+        message: 'getVersions() method expects 1 argument of string type.',
+      });
     });
 
-    it('getVersions() throws if non string coercable arguments are passed into the function', () => {
-      assert.throws(
-        () => core.getVersions({ deviceName: 'CPU' }),
-        { message: 'The argument in getVersions() method must be a string or convertible to a string.' }
-      );
+    it('getVersions() throws with non string coercable arg.', () => {
+      assert.throws(() => core.getVersions({ deviceName: 'CPU' }));
     });
-
   });
 
   it('CompiledModel type', () => {
@@ -70,14 +63,14 @@ describe('ov basic tests.', () => {
   });
 
   describe('Core.compileModelSync()', () => {
-    const tput = { 'PERFORMANCE_HINT': 'THROUGHPUT' };
+    const tput = { PERFORMANCE_HINT: 'THROUGHPUT' };
 
     it('compileModelSync(model:Model, deviceName: string, config: {}) ', () => {
       const cm = core.compileModelSync(model, 'CPU', tput);
       assert.deepStrictEqual(cm.output(0).shape, [1, 10]);
     });
 
-    it('compileModelSync(model:model_path, deviceName: string, config: {}) ', () => {
+    it('compileModelSync(model_path, deviceName, config: {}) ', () => {
       const cm = core.compileModelSync(testXml, 'CPU', tput);
       assert.equal(cm.inputs.length, 1);
     });
@@ -87,14 +80,16 @@ describe('ov basic tests.', () => {
       assert.deepStrictEqual(cm.output(0).shape, [1, 10]);
     });
 
-    it('compileModelSync(model, device, config) throws when config is a string', () => {
-      const expectedMsg = ('\'compileModelSync\' method called with incorrect parameters.\n' +
-      'Provided signature: (object, string, string) \n' +
-      'Allowed signatures:\n' +
-      '- (string, string)\n' +
-      '- (Model, string)\n' +
-      '- (string, string, object)\n' +
-      '- (Model, string, object)\n').replace(/[()]/g, '\\$&');
+    it('compileModelSync(model, device, configOfTypeString) throws', () => {
+      const expectedMsg = (
+        '\'compileModelSync\' method called with incorrect parameters.\n' +
+        'Provided signature: (object, string, string) \n' +
+        'Allowed signatures:\n' +
+        '- (string, string)\n' +
+        '- (Model, string)\n' +
+        '- (string, string, object)\n' +
+        '- (Model, string, object)\n'
+      ).replace(/[()]/g, '\\$&');
 
       assert.throws(
         () => core.compileModelSync(model, 'CPU', 'string'),
@@ -102,21 +97,23 @@ describe('ov basic tests.', () => {
       );
     });
 
-    it('compileModelSync(model, device, config) throws when config value is not a string', () => {
+    it('compileModelSync(model, device, invalidConfig) throws', () => {
       assert.throws(
-        () => core.compileModelSync(model, 'CPU', { 'PERFORMANCE_HINT': tput }),
-        /Cannot convert to ov::Any/
+        () => core.compileModelSync(model, 'CPU', { PERFORMANCE_HINT: tput }),
+        /Cannot convert to ov::Any/,
       );
     });
 
-    it('compileModelSync(model) throws if the number of arguments is invalid', () => {
-      const expectedMsg = ('\'compileModelSync\' method called with incorrect parameters.\n' +
-      'Provided signature: (object) \n' +
-      'Allowed signatures:\n' +
-      '- (string, string)\n' +
-      '- (Model, string)\n' +
-      '- (string, string, object)\n' +
-      '- (Model, string, object)\n').replace(/[()]/g, '\\$&');
+    it('compileModelSync(model) throws with invalid number of args', () => {
+      const expectedMsg = (
+        '\'compileModelSync\' method called with incorrect parameters.\n' +
+        'Provided signature: (object) \n' +
+        'Allowed signatures:\n' +
+        '- (string, string)\n' +
+        '- (Model, string)\n' +
+        '- (string, string, object)\n' +
+        '- (Model, string, object)\n'
+      ).replace(/[()]/g, '\\$&');
 
       assert.throws(
         () => core.compileModelSync(model),
@@ -126,54 +123,50 @@ describe('ov basic tests.', () => {
   });
 
   describe('Core.compileModel()', () => {
-    const tput = { 'PERFORMANCE_HINT': 'THROUGHPUT' };
+    const tput = { PERFORMANCE_HINT: 'THROUGHPUT' };
 
     it('compileModel(model:Model, deviceName: string, config: {}) ', () => {
-      core.compileModel(model, 'CPU', tput).then(cm => {
+      core.compileModel(model, 'CPU', tput).then((cm) => {
         assert.deepStrictEqual(cm.output(0).shape, [1, 10]);
       });
-
     });
 
-    it('compileModel(model:model_path, deviceName: string, config: {}) ', () => {
-      core.compileModel(testXml, 'CPU', tput).then(cm => {
+    it('compileModel(model_path, deviceName, config: {}) ', () => {
+      core.compileModel(testXml, 'CPU', tput).then((cm) => {
         assert.equal(cm.inputs.length, 1);
       });
-
     });
 
     it('compileModel(model:model_path, deviceName: string) ', () => {
-      core.compileModel(testXml, 'CPU').then(cm => {
+      core.compileModel(testXml, 'CPU').then((cm) => {
         assert.deepStrictEqual(cm.output(0).shape, [1, 10]);
       });
-
     });
 
-    it('compileModel(model, device, config) throws when config isn\'t an object', () => {
+    it('compileModel(model, device, invalidconfig) throws', () => {
       assert.throws(
         () => core.compileModel(model, 'CPU', 'string').then(),
-        'Argument #3 must be an Object.'
+        'Argument #3 must be an Object.',
       );
     });
 
-    it('compileModel(model, device, config) throws when config value is not a string', () => {
+    it('compileModel(model, device, invalidConfig) throws', () => {
       assert.throws(
-        () => core.compileModel(model, 'CPU', { 'PERFORMANCE_HINT': tput }).then(),
-        /Cannot convert to ov::Any/
+        () =>
+          core.compileModel(model, 'CPU', { PERFORMANCE_HINT: tput }).then(),
+        /Cannot convert to ov::Any/,
       );
     });
 
-    it('compileModel(model) throws if the number of arguments is invalid', () => {
+    it('compileModel(model) throws with invalid number of args', () => {
       assert.throws(
         () => core.compileModel(model).then(),
-        /Invalid number of arguments/
+        /Invalid number of arguments/,
       );
     });
-
   });
 
   describe('ov.Model/ov.CompiledModel output()', () => {
-
     it('model.output() type', () => {
       assert.ok(model.output() instanceof ov.Output);
     });
@@ -183,7 +176,6 @@ describe('ov basic tests.', () => {
     });
 
     it('output() methods/properties', () => {
-
       modelLike.forEach((obj) => {
         assert.strictEqual(obj.outputs.length, 1);
         // tests for an obj with one output
@@ -195,12 +187,10 @@ describe('ov basic tests.', () => {
         assert.strictEqual(obj.output().getAnyName(), 'fc_out');
         assert.strictEqual(obj.output().anyName, 'fc_out');
       });
-
     });
   });
 
   describe('ov.Model/ov.CompiledModel input()', () => {
-
     it('ov.Model.input() type', () => {
       assert.ok(model.input() instanceof ov.Output);
     });
@@ -211,7 +201,6 @@ describe('ov basic tests.', () => {
 
     it('input() methods/properties', () => {
       modelLike.forEach((obj) => {
-
         assert.strictEqual(typeof obj.input(), 'object');
         assert.strictEqual(obj.inputs.length, 1);
         assert.strictEqual(obj.input().toString(), 'data');
@@ -223,9 +212,7 @@ describe('ov basic tests.', () => {
         assert.deepStrictEqual(obj.input(0).shape, [1, 3, 32, 32]);
         assert.deepStrictEqual(obj.input(0).getShape(), [1, 3, 32, 32]);
       });
-
     });
-
   });
 
   describe('Test exportModel()/importModel()', () => {
@@ -234,7 +221,10 @@ describe('ov basic tests.', () => {
     let res1 = null;
 
     before(() => {
-      tensor = Float32Array.from({ length: 3072 }, () => (Math.random() + epsilon));
+      tensor = Float32Array.from(
+        { length: 3072 },
+        () => Math.random() + epsilon,
+      );
       const core = new ov.Core();
       const model = core.readModelSync(testXml);
       const compiledModel = core.compileModelSync(model, 'CPU');
@@ -252,7 +242,9 @@ describe('ov basic tests.', () => {
     });
 
     it('Test importModelSync(stream, device, config)', () => {
-      const newCompiled = core.importModelSync(userStream, 'CPU', { 'NUM_STREAMS': 1 });
+      const newCompiled = core.importModelSync(userStream, 'CPU', {
+        NUM_STREAMS: 1,
+      });
       const newInferRequest = newCompiled.createInferRequest();
       const res2 = newInferRequest.infer([tensor]);
 
@@ -262,20 +254,20 @@ describe('ov basic tests.', () => {
     it('Test importModelSync(stream, device) throws', () => {
       assert.throws(
         () => core.importModelSync(epsilon, 'CPU'),
-        /The first argument must be of type Buffer./
+        /The first argument must be of type Buffer./,
       );
     });
 
     it('Test importModelSync(stream, device) throws', () => {
       assert.throws(
         () => core.importModelSync(userStream, tensor),
-        /The second argument must be of type String./
+        /The second argument must be of type String./,
       );
     });
     it('Test importModelSync(stream, device, config: tensor) throws', () => {
       assert.throws(
         () => core.importModelSync(userStream, 'CPU', tensor),
-        /NotFound: Unsupported property 0 by CPU plugin./
+        /NotFound: Unsupported property 0 by CPU plugin./,
       );
     });
 
@@ -283,7 +275,7 @@ describe('ov basic tests.', () => {
       const testString = 'test';
       assert.throws(
         () => core.importModelSync(userStream, 'CPU', testString),
-        /Passed Napi::Value must be an object./
+        /Passed Napi::Value must be an object./,
       );
     });
 
@@ -291,13 +283,13 @@ describe('ov basic tests.', () => {
     throws', () => {
       const tmpDir = '/tmp';
       assert.throws(
-        () => core.importModelSync(userStream, 'CPU', { 'CACHE_DIR': tmpDir }),
-        /Unsupported property CACHE_DIR by CPU plugin./
+        () => core.importModelSync(userStream, 'CPU', { CACHE_DIR: tmpDir }),
+        /Unsupported property CACHE_DIR by CPU plugin./,
       );
     });
 
     it('Test importModel(stream, device)', () => {
-      core.importModel(userStream, 'CPU').then(newCompiled => {
+      core.importModel(userStream, 'CPU').then((newCompiled) => {
         const newInferRequest = newCompiled.createInferRequest();
         const res2 = newInferRequest.infer([tensor]);
         assert.deepStrictEqual(res1['fc_out'].data[0], res2['fc_out'].data[0]);
@@ -305,26 +297,30 @@ describe('ov basic tests.', () => {
     });
 
     it('Test importModel(stream, device, config)', () => {
-      core.importModel(userStream, 'CPU', { 'NUM_STREAMS': 1 }).then(
-        newCompiled => {
+      core
+        .importModel(userStream, 'CPU', { NUM_STREAMS: 1 })
+        .then((newCompiled) => {
           const newInferRequest = newCompiled.createInferRequest();
           const res2 = newInferRequest.infer([tensor]);
 
-          assert.deepStrictEqual(res1['fc_out'].data[0], res2['fc_out'].data[0]);
+          assert.deepStrictEqual(
+            res1['fc_out'].data[0],
+            res2['fc_out'].data[0],
+          );
         });
     });
 
     it('Test importModel(stream, device) throws', () => {
       assert.throws(
         () => core.importModel(epsilon, 'CPU').then(),
-        /'importModel' method called with incorrect parameters./
+        /'importModel' method called with incorrect parameters./,
       );
     });
 
     it('Test importModel(stream, device) throws', () => {
       assert.throws(
         () => core.importModel(userStream, tensor).then(),
-        /'importModel' method called with incorrect parameters./
+        /'importModel' method called with incorrect parameters./,
       );
     });
 
@@ -332,10 +328,8 @@ describe('ov basic tests.', () => {
       const testString = 'test';
       assert.throws(
         () => core.importModel(userStream, 'CPU', testString).then(),
-        /'importModel' method called with incorrect parameters./
+        /'importModel' method called with incorrect parameters./,
       );
     });
-
   });
-
 });
diff --git a/src/bindings/js/node/tests/unit/compiled_model.test.js b/src/bindings/js/node/tests/unit/compiled_model.test.js
index d433cf3b8e96b1..86c7bf4b7d1b3f 100644
--- a/src/bindings/js/node/tests/unit/compiled_model.test.js
+++ b/src/bindings/js/node/tests/unit/compiled_model.test.js
@@ -8,8 +8,6 @@ const { describe, it, before, beforeEach } = require('node:test');
 const { testModels, getModelPath, isModelAvailable } = require('./utils.js');
 
 describe('ov.CompiledModel tests', () => {
-
-
   let testXml = null;
   let core = null;
   let compiledModel = null;
@@ -19,10 +17,10 @@ describe('ov.CompiledModel tests', () => {
     testXml = getModelPath().xml;
     core = new ov.Core();
   });
-  
+
   beforeEach(() => {
     const properties = {
-      'AUTO_BATCH_TIMEOUT': '1',
+      AUTO_BATCH_TIMEOUT: '1',
     };
     compiledModel = core.compileModelSync(testXml, 'BATCH:CPU', properties);
   });
@@ -34,51 +32,55 @@ describe('ov.CompiledModel tests', () => {
     it('throws an error when called without arguments', () => {
       assert.throws(
         () => compiledModel.getProperty(),
-        /'getProperty' method called with incorrect parameters/
+        /'getProperty' method called with incorrect parameters/,
       );
     });
-    it('throws an error when called with property name that does not exists', () => {
-      assert.throws(
-        () => compiledModel.getProperty('PROPERTY_THAT_DOES_NOT_EXIST')
+    it('throws when called with property name that does not exists', () => {
+      assert.throws(() =>
+        compiledModel.getProperty('PROPERTY_THAT_DOES_NOT_EXIST'),
       );
     });
   });
 
   describe('setProperty()', () => {
     it('sets a properties for compiled model', () => {
-      const properties = {'AUTO_BATCH_TIMEOUT': '1000'};
+      const properties = { AUTO_BATCH_TIMEOUT: '1000' };
       assert.doesNotThrow(() => compiledModel.setProperty(properties));
     });
 
     it('throws an error when called without an object argument', () => {
       assert.throws(
         () => compiledModel.setProperty(),
-        /'setProperty' method called with incorrect parameters/
+        /'setProperty' method called with incorrect parameters/,
       );
     });
     it('throws an error when called with wrong argument', () => {
       assert.throws(
         () => compiledModel.setProperty(123),
-        /'setProperty' method called with incorrect parameters/
+        /'setProperty' method called with incorrect parameters/,
       );
     });
 
     it('throws an error when called with multiple arguments', () => {
       assert.throws(
-        () => compiledModel.setProperty({'PERFORMANCE_HINT': 'THROUGHPUT'}, {'NUM_STREAMS': 'AUTO'}),
-        /'setProperty' method called with incorrect parameters/
+        () =>
+          compiledModel.setProperty(
+            { PERFORMANCE_HINT: 'THROUGHPUT' },
+            { NUM_STREAMS: 'AUTO' },
+          ),
+        /'setProperty' method called with incorrect parameters/,
       );
     });
 
     it('returns the set property of the compiled model', () => {
-      const properties = {'AUTO_BATCH_TIMEOUT': '123'};
+      const properties = { AUTO_BATCH_TIMEOUT: '123' };
       compiledModel.setProperty(properties);
       assert.strictEqual(compiledModel.getProperty('AUTO_BATCH_TIMEOUT'), 123);
     });
 
     it('retains the last set property when set multiple times', () => {
-      compiledModel.setProperty({'AUTO_BATCH_TIMEOUT': '321'});
-      compiledModel.setProperty({'AUTO_BATCH_TIMEOUT': '132'});
+      compiledModel.setProperty({ AUTO_BATCH_TIMEOUT: '321' });
+      compiledModel.setProperty({ AUTO_BATCH_TIMEOUT: '132' });
       assert.strictEqual(compiledModel.getProperty('AUTO_BATCH_TIMEOUT'), 132);
     });
 
diff --git a/src/bindings/js/node/tests/unit/core.test.js b/src/bindings/js/node/tests/unit/core.test.js
index fac64e489273b5..6cf431a38b5030 100644
--- a/src/bindings/js/node/tests/unit/core.test.js
+++ b/src/bindings/js/node/tests/unit/core.test.js
@@ -20,7 +20,7 @@ describe('ov.Core tests', () => {
   it('Core.setProperty()', () => {
     const tmpDir = '/tmp';
 
-    core.setProperty({ 'CACHE_DIR': tmpDir });
+    core.setProperty({ CACHE_DIR: tmpDir });
 
     const cacheDir = core.getProperty('CACHE_DIR');
 
@@ -30,7 +30,7 @@ describe('ov.Core tests', () => {
   it('Core.setProperty(\'CPU\')', () => {
     const tmpDir = '/tmp';
 
-    core.setProperty('CPU', { 'CACHE_DIR': tmpDir });
+    core.setProperty('CPU', { CACHE_DIR: tmpDir });
 
     const cacheDir = core.getProperty('CPU', 'CACHE_DIR');
 
@@ -38,7 +38,10 @@ describe('ov.Core tests', () => {
   });
 
   it('Core.getProperty(\'CPU\', \'SUPPORTED_PROPERTIES\') is Array', () => {
-    const supportedPropertiesArray = core.getProperty('CPU', 'SUPPORTED_PROPERTIES');
+    const supportedPropertiesArray = core.getProperty(
+      'CPU',
+      'SUPPORTED_PROPERTIES',
+    );
 
     assert.ok(Array.isArray(supportedPropertiesArray));
   });
@@ -46,7 +49,7 @@ describe('ov.Core tests', () => {
   it('Core.setProperty(\'CPU\', { \'NUM_STREAMS\': 5 })', () => {
     const streams = 5;
 
-    core.setProperty('CPU', { 'NUM_STREAMS': streams });
+    core.setProperty('CPU', { NUM_STREAMS: streams });
     const result = core.getProperty('CPU', 'NUM_STREAMS');
 
     assert.equal(result, streams);
@@ -55,7 +58,7 @@ describe('ov.Core tests', () => {
   it('Core.setProperty(\'CPU\', { \'INFERENCE_NUM_THREADS\': 3 })', () => {
     const threads = 3;
 
-    core.setProperty('CPU', { 'INFERENCE_NUM_THREADS': threads });
+    core.setProperty('CPU', { INFERENCE_NUM_THREADS: threads });
     const result = core.getProperty('CPU', 'INFERENCE_NUM_THREADS');
 
     assert.equal(result, threads);
@@ -64,7 +67,7 @@ describe('ov.Core tests', () => {
   it('Core.addExtension() with empty parameters', () => {
     assert.throws(
       () => core.addExtension(),
-      /addExtension method applies one argument of string type/
+      /addExtension method applies one argument of string type/,
     );
   });
 
@@ -73,7 +76,7 @@ describe('ov.Core tests', () => {
 
     assert.throws(
       () => core.addExtension(notExistsExt),
-      /Cannot load library 'not_exists'/
+      /Cannot load library 'not_exists'/,
     );
   });
 
diff --git a/src/bindings/js/node/tests/unit/infer_request.test.js b/src/bindings/js/node/tests/unit/infer_request.test.js
index d1ee057cd77e0e..3f993ef70f25db 100644
--- a/src/bindings/js/node/tests/unit/infer_request.test.js
+++ b/src/bindings/js/node/tests/unit/infer_request.test.js
@@ -11,7 +11,6 @@ const epsilon = 0.5; // To avoid very small numbers
 const testXml = getModelPath().xml;
 
 describe('ov.InferRequest tests', () => {
-
   let compiledModel = null;
   let tensorData = null;
   let tensor = null;
@@ -25,17 +24,12 @@ describe('ov.InferRequest tests', () => {
     const model = core.readModelSync(testXml);
     compiledModel = core.compileModelSync(model, 'CPU');
 
-    tensorData = Float32Array.from({ length: 3072 }, () => (Math.random() + epsilon));
-    tensor = new ov.Tensor(
-      ov.element.f32,
-      [1, 3, 32, 32],
-      tensorData,
-    );
-    resTensor = new ov.Tensor(
-      ov.element.f32,
-      [1, 10],
-      tensorData.slice(-10),
+    tensorData = Float32Array.from(
+      { length: 3072 },
+      () => Math.random() + epsilon,
     );
+    tensor = new ov.Tensor(ov.element.f32, [1, 3, 32, 32], tensorData);
+    resTensor = new ov.Tensor(ov.element.f32, [1, 10], tensorData.slice(-10));
     tensorLike = [tensor, tensorData];
   });
 
@@ -45,7 +39,7 @@ describe('ov.InferRequest tests', () => {
       inferRequest = compiledModel.createInferRequest();
     });
 
-    it('Test infer(inputData: {inputName: string]: Tensor[]/TypedArray[]}', () => {
+    it('infer(inputData: {inputName: string]: Tensor[]/TypedArray[]}', () => {
       tensorLike.forEach((tl) => {
         const result = inferRequest.infer({ data: tl });
         assert.deepStrictEqual(Object.keys(result), ['fc_out']);
@@ -62,72 +56,77 @@ describe('ov.InferRequest tests', () => {
     });
 
     it('Test infer(TypedArray) throws', () => {
-      assert.throws(
-        () => inferRequest.infer(tensorData),
-        {message: /TypedArray cannot be passed directly into infer\(\) method./});
+      assert.throws(() => inferRequest.infer(tensorData), {
+        message: /TypedArray cannot be passed directly into infer\(\) method./,
+      });
     });
 
     it('Test for invalid input data', () => {
       const buffer = new ArrayBuffer(tensorData.length);
       const inputMessagePairs = [
         ['string', 'Cannot create a tensor from the passed Napi::Value.'],
-        [tensorData.slice(-10), 'Memory allocated using shape and element::type mismatch passed data\'s size'],
-        [new Float32Array(buffer, 4), 'TypedArray.byteOffset has to be equal to zero.'],
+        [
+          tensorData.slice(-10),
+          /Memory allocated using shape and element::type mismatch/,
+        ],
+        [
+          new Float32Array(buffer, 4),
+          'TypedArray.byteOffset has to be equal to zero.',
+        ],
         [{}, /Invalid argument/], // Test for object that is not Tensor
       ];
 
-      inputMessagePairs.forEach( ([tl, msg]) => {
-
+      inputMessagePairs.forEach(([tl, msg]) => {
         assert.throws(
           () => inferRequest.infer([tl]),
-          { message: new RegExp(msg) }, 'infer([data]) throws');
+          { message: new RegExp(msg) },
+          'infer([data]) throws',
+        );
 
         assert.throws(
-          () => inferRequest.infer({data: tl}),
-          { message: new RegExp(msg) }, 'infer({ data: tl}) throws');
-
+          () => inferRequest.infer({ data: tl }),
+          { message: new RegExp(msg) },
+          'infer({ data: tl}) throws',
+        );
       });
-
     });
-
   });
 
   describe('inferAsync() method', () => {
     let inferRequest = null;
-    before( () => {
+    before(() => {
       inferRequest = compiledModel.createInferRequest();
     });
 
     it('Test inferAsync(inputData: { [inputName: string]: Tensor })', () => {
-      inferRequest.inferAsync({ data: tensor }).then(result => {
+      inferRequest.inferAsync({ data: tensor }).then((result) => {
         assert.ok(result['fc_out'] instanceof ov.Tensor);
         assert.deepStrictEqual(Object.keys(result), ['fc_out']);
-        assert.deepStrictEqual(result['fc_out'].data.length, 10);}
-      );
+        assert.deepStrictEqual(result['fc_out'].data.length, 10);
+      });
     });
 
     it('Test inferAsync(inputData: Tensor[])', () => {
-      inferRequest.inferAsync([ tensor ]).then(result => {
+      inferRequest.inferAsync([tensor]).then((result) => {
         assert.ok(result['fc_out'] instanceof ov.Tensor);
         assert.deepStrictEqual(Object.keys(result), ['fc_out']);
         assert.deepStrictEqual(result['fc_out'].data.length, 10);
       });
     });
 
-    it('Test inferAsync([data]) throws: Cannot create a tensor from the passed Napi::Value.', () => {
+    it('Test inferAsync([data]) throws', () => {
       assert.throws(
         () => inferRequest.inferAsync(['string']).then(),
-        /Cannot create a tensor from the passed Napi::Value./
+        /Cannot create a tensor from the passed Napi::Value./,
       );
     });
 
-    it('Test inferAsync({ data: "string"}) throws: Cannot create a tensor from the passed Napi::Value.', () => {
+    it('Test inferAsync({ data: "string"}) throws', () => {
       assert.throws(
-        () => inferRequest.inferAsync({data: 'string'}).then(),
-        /Cannot create a tensor from the passed Napi::Value./
+        () => inferRequest.inferAsync({ data: 'string' }).then(),
+        /Cannot create a tensor from the passed Napi::Value./,
       );
     });
-
   });
 
   describe('setters', () => {
@@ -136,128 +135,117 @@ describe('ov.InferRequest tests', () => {
       inferRequest = compiledModel.createInferRequest();
     });
 
-    it('Test setInputTensor(tensor)', () => {
+    it('setInputTensor(tensor)', () => {
       inferRequest.setInputTensor(tensor);
       const t1 = inferRequest.getInputTensor();
       assert.deepStrictEqual(tensor.data[0], t1.data[0]);
     });
 
-    it('Test setInputTensor(object) throws when passed object is not a Tensor.', () => {
-      assert.throws(
-        () => inferRequest.setInputTensor({}),
-        {message: /Argument #[0-9]+ must be a Tensor./}
-      );
+    it('setInputTensor(object) throws if object is not a Tensor.', () => {
+      assert.throws(() => inferRequest.setInputTensor({}), {
+        message: /Argument #[0-9]+ must be a Tensor./,
+      });
     });
 
-    it('Test setInputTensor(idx, tensor)', () => {
+    it('setInputTensor(idx, tensor)', () => {
       inferRequest.setInputTensor(0, tensor);
       const t1 = inferRequest.getInputTensor();
       assert.deepStrictEqual(tensor.data[0], t1.data[0]);
     });
 
-    it('Test setInputTensor(idx, tensor) throws', () => {
+    it('setInputTensor(idx, tensor) throws', () => {
       const testIdx = 10;
-      assert.throws (
-        () => inferRequest.setInputTensor(testIdx, tensor),
-        {message: /Input port for index [0-9]+ was not found!/}
-      );
+      assert.throws(() => inferRequest.setInputTensor(testIdx, tensor), {
+        message: /Input port for index [0-9]+ was not found!/,
+      });
     });
 
-    it('Test setInputTensor(idx, object) throws when passed object is not a Tensor.', () => {
-      assert.throws(
-        () => inferRequest.setInputTensor(0, {}),
-        {message: /Argument #[0-9]+ must be a Tensor./}
-      );
+    it('setInputTensor(idx, object) throws if object is not a Tensor.', () => {
+      assert.throws(() => inferRequest.setInputTensor(0, {}), {
+        message: /Argument #[0-9]+ must be a Tensor./,
+      });
     });
 
-    it('Test setInputTensor(tensor, tensor) throws', () => {
-      assert.throws(
-        () => inferRequest.setInputTensor(resTensor, tensor),
-        {message: / invalid argument./}
-      );
+    it('setInputTensor(tensor, tensor) throws', () => {
+      assert.throws(() => inferRequest.setInputTensor(resTensor, tensor), {
+        message: / invalid argument./,
+      });
     });
 
-    it('Test setOutputTensor(tensor)', () => {
+    it('setOutputTensor(tensor)', () => {
       inferRequest.setOutputTensor(resTensor);
       const res2 = inferRequest.getOutputTensor();
       assert.deepStrictEqual(resTensor.data[0], res2.data[0]);
     });
 
-    it('Test setOutputTensor(object) throws when passed object is not a Tensor.', () => {
-      assert.throws(
-        () => inferRequest.setOutputTensor({}),
-        {message: /Argument #[0-9]+ must be a Tensor./}
-      );
+    it('setOutputTensor(object) throws if object is not a Tensor.', () => {
+      assert.throws(() => inferRequest.setOutputTensor({}), {
+        message: /Argument #[0-9]+ must be a Tensor./,
+      });
     });
 
-    it('Test setOutputTensor(idx, tensor) throws', () => {
+    it('setOutputTensor(idx, tensor) throws', () => {
       const testIdx = 10;
-      assert.throws (
-        () => inferRequest.setOutputTensor(testIdx, tensor),
-        {message: /Output port for index [0-9]+ was not found!/}
-      );
+      assert.throws(() => inferRequest.setOutputTensor(testIdx, tensor), {
+        message: /Output port for index [0-9]+ was not found!/,
+      });
     });
 
-    it('Test setOutputTensor(idx, tensor)', () => {
+    it('setOutputTensor(idx, tensor)', () => {
       inferRequest.setOutputTensor(0, resTensor);
       const res2 = inferRequest.getOutputTensor();
       assert.deepStrictEqual(resTensor.data[0], res2.data[0]);
     });
 
-    it('Test setOutputTensor(idx, tensor) throws when passed object is not a Tensor.', () => {
-      assert.throws(
-        () => inferRequest.setOutputTensor(0, {}),
-        {message: /Argument #[0-9]+ must be a Tensor./}
-      );
+    it('setOutputTensor(idx, object) throws if object is not a Tensor.', () => {
+      assert.throws(() => inferRequest.setOutputTensor(0, {}), {
+        message: /Argument #[0-9]+ must be a Tensor./,
+      });
     });
 
-    it('Test setOutputTensor() - pass two tensors', () => {
-      assert.throws(
-        () => inferRequest.setOutputTensor(resTensor, tensor),
-        {message: / invalid argument./});
+    it('setOutputTensor() - pass two tensors', () => {
+      assert.throws(() => inferRequest.setOutputTensor(resTensor, tensor), {
+        message: / invalid argument./,
+      });
     });
 
-    it('Test setTensor(string, tensor)', () => {
+    it('setTensor(string, tensor)', () => {
       inferRequest.setTensor('fc_out', resTensor);
       const res2 = inferRequest.getTensor('fc_out');
       assert.ok(res2 instanceof ov.Tensor);
       assert.deepStrictEqual(resTensor.data[0], res2.data[0]);
     });
 
-    it('Test setTensor(string, object) - throws', () => {
+    it('setTensor(string, object) - throws', () => {
       const testName = 'testName';
-      assert.throws(
-        () => inferRequest.setTensor(testName, tensor),
-        {message: /Port for tensor name testName was not found./});
+      assert.throws(() => inferRequest.setTensor(testName, tensor), {
+        message: /Port for tensor name testName was not found./,
+      });
     });
 
-    it('Test setTensor(string, object) - throws', () => {
-      assert.throws(
-        () => inferRequest.setTensor('fc_out', {}),
-        {message: /Argument #[0-9]+ must be a Tensor./});
+    it('setTensor(string, object) - throws', () => {
+      assert.throws(() => inferRequest.setTensor('fc_out', {}), {
+        message: /Argument #[0-9]+ must be a Tensor./,
+      });
     });
 
-    it('Test setTensor(string, tensor) - pass one arg', () => {
-      assert.throws(
-        () => inferRequest.setTensor('fc_out'),
-        {message: / invalid argument./}
-      );
+    it('setTensor(string, tensor) - pass one arg', () => {
+      assert.throws(() => inferRequest.setTensor('fc_out'), {
+        message: / invalid argument./,
+      });
     });
 
-    it('Test setTensor(string, tensor) - pass args in wrong order', () => {
-      assert.throws(
-        () => inferRequest.setTensor(resTensor, 'fc_out'),
-        {message: / invalid argument./}
-      );
+    it('setTensor(string, tensor) - pass args in wrong order', () => {
+      assert.throws(() => inferRequest.setTensor(resTensor, 'fc_out'), {
+        message: / invalid argument./,
+      });
     });
 
-    it('Test setTensor(string, tensor) - pass number as first arg', () => {
-      assert.throws(
-        () => inferRequest.setTensor(123, 'fc_out'),
-        {message: / invalid argument/}
-      );
+    it('setTensor(string, tensor) - pass number as first arg', () => {
+      assert.throws(() => inferRequest.setTensor(123, 'fc_out'), {
+        message: / invalid argument/,
+      });
     });
-
   });
 
   describe('getters', () => {
@@ -310,6 +298,5 @@ describe('ov.InferRequest tests', () => {
       const res1 = ir.infer([tensorData]);
       assert.deepStrictEqual(res1['fc_out'].data[0], res2['fc_out'].data[0]);
     });
-
   });
 });
diff --git a/src/bindings/js/node/tests/unit/model.test.js b/src/bindings/js/node/tests/unit/model.test.js
index e71226e756ea44..f66c119578f305 100644
--- a/src/bindings/js/node/tests/unit/model.test.js
+++ b/src/bindings/js/node/tests/unit/model.test.js
@@ -8,7 +8,6 @@ const { describe, it, before, beforeEach } = require('node:test');
 const { testModels, getModelPath, isModelAvailable } = require('./utils.js');
 
 describe('ov.Model tests', () => {
-
   let testXml = null;
   let core = null;
   let model = null;
@@ -24,12 +23,12 @@ describe('ov.Model tests', () => {
   });
 
   describe('Model.isDynamic()', () => {
-    it('should return a boolean value indicating if the model is dynamic', () => {
+    it('should return a boolean value type', () => {
       const result = model.isDynamic();
       assert.strictEqual(
         typeof result,
         'boolean',
-        'isDynamic() should return a boolean value'
+        'isDynamic() should return a boolean value',
       );
     });
 
@@ -39,7 +38,7 @@ describe('ov.Model tests', () => {
           model.isDynamic('unexpected argument');
         },
         /^Error: isDynamic\(\) does not accept any arguments\.$/,
-        'Expected isDynamic to throw an error when called with arguments'
+        'Expected isDynamic to throw an error when called with arguments',
       );
     });
 
@@ -48,20 +47,20 @@ describe('ov.Model tests', () => {
       assert.strictEqual(
         model.isDynamic(),
         expectedStatus,
-        'Expected isDynamic to return false for a static model'
+        'Expected isDynamic to return false for a static model',
       );
     });
   });
 
   describe('Model.getFriendlyName()', () => {
-    it('returns the unique name of the model if no friendly name is set', () => {
+    it('returns model\'s unique name if no friendly name is set', () => {
       const expectedName = 'test_model';
       assert.strictEqual(model.getFriendlyName(), expectedName);
     });
     it('throws an error when called with arguments', () => {
       assert.throws(
         () => model.getFriendlyName('unexpected argument'),
-        /getFriendlyName\(\) does not take any arguments/
+        /getFriendlyName\(\) does not take any arguments/,
       );
     });
   });
@@ -69,18 +68,18 @@ describe('ov.Model tests', () => {
     it('throws an error when called without a string argument', () => {
       assert.throws(
         () => model.setFriendlyName(),
-        /Expected a single string argument for the friendly name/
+        /Expected a single string argument for the friendly name/,
       );
       assert.throws(
         () => model.setFriendlyName(123),
-        /Expected a single string argument for the friendly name/
+        /Expected a single string argument for the friendly name/,
       );
     });
 
     it('throws an error when called with multiple arguments', () => {
       assert.throws(
         () => model.setFriendlyName('Name1', 'Name2'),
-        /Expected a single string argument for the friendly name/
+        /Expected a single string argument for the friendly name/,
       );
     });
 
@@ -103,85 +102,94 @@ describe('ov.Model tests', () => {
   });
 
   describe('Model.getOutputSize()', () => {
-
-    it('should return a number indicating number of outputs for the model', () => {
+    it('getOutputSize() should return a number', () => {
       const result = model.getOutputSize();
-      assert.strictEqual(typeof result, 'number', 'getOutputSize() should return a number');
+      assert.strictEqual(typeof result, 'number');
     });
 
     it('should not accept any arguments', () => {
       assert.throws(() => {
         model.getOutputSize('unexpected argument');
-      }, /^Error: getOutputSize\(\) does not accept any arguments\.$/, 'Expected getOutputSize to throw an error when called with arguments');
+      }, /^Error: getOutputSize\(\) does not accept any arguments\.$/);
     });
 
     it('should return 1 for the default model', () => {
-      assert.strictEqual(model.getOutputSize(), 1, 'Expected getOutputSize to return 1 for the default model');
+      assert.strictEqual(model.getOutputSize(), 1);
     });
   });
 
   describe('Model.getOutputElementType()', () => {
-    it('should return a string for the element type ', () => {
+    it('should return a string', () => {
       const result = model.getOutputElementType(0);
-      assert.strictEqual(typeof result, 'string',
-        'getOutputElementType() should return a string');
+      assert.strictEqual(typeof result, 'string');
     });
 
     it('should accept a single integer argument', () => {
-      assert.throws(() => {
-        model.getOutputElementType();
-      }, /'getOutputElementType' method called with incorrect parameters/,
-      'Should throw when called without arguments');
+      assert.throws(
+        () => {
+          model.getOutputElementType();
+        },
+        /'getOutputElementType' method called with incorrect parameters/,
+        'Should throw when called without arguments',
+      );
 
-      assert.throws(() => {
-        model.getOutputElementType('unexpected argument');
-      }, /'getOutputElementType' method called with incorrect parameters/,
-      'Should throw on non-number argument');
+      assert.throws(
+        () => {
+          model.getOutputElementType('unexpected argument');
+        },
+        /'getOutputElementType' method called with incorrect parameters/,
+        'Should throw on non-number argument',
+      );
 
-      assert.throws(() => {
-        model.getOutputElementType(0, 1);
-      }, /'getOutputElementType' method called with incorrect parameters/,
-      'Should throw on multiple arguments');
+      assert.throws(
+        () => {
+          model.getOutputElementType(0, 1);
+        },
+        /'getOutputElementType' method called with incorrect parameters/,
+        'Should throw on multiple arguments',
+      );
 
-      assert.throws(() => {
-        model.getOutputElementType(3.14);
-      }, /'getOutputElementType' method called with incorrect parameters/,
-      'Should throw on non-integer number');
+      assert.throws(
+        () => {
+          model.getOutputElementType(3.14);
+        },
+        /'getOutputElementType' method called with incorrect parameters/,
+        'Should throw on non-integer number',
+      );
     });
 
     it('should return a valid element type for the default model', () => {
       const elementType = model.getOutputElementType(0);
-      assert.ok(typeof elementType === 'string' && elementType.length > 0,
-        `Expected a non-empty string, got ${elementType}`);
+      assert.ok(
+        typeof elementType === 'string' && elementType.length > 0,
+        `Expected a non-empty string, got ${elementType}`,
+      );
     });
 
     it('should throw an error for out-of-range index', () => {
       const outputSize = model.getOutputSize();
-      assert.throws(
-        () => { model.getOutputElementType(outputSize); },
-        /^Error: /,
-        'Should throw for out-of-range index'
-      );
+      assert.throws(() => {
+        model.getOutputElementType(outputSize);
+      }, /^Error: /);
     });
   });
 
   describe('Model.clone()', () => {
     it('should return an object of type model', () => {
       const clonedModel = model.clone();
-      assert.ok(clonedModel instanceof ov.Model, 'clone() should return a model');
+      assert.ok(clonedModel instanceof ov.Model);
     });
 
     it('should return a model that is a clone of the calling model', () => {
       const clonedModel = model.clone();
-      assert.deepStrictEqual(clonedModel, model, 'Cloned Model should be exactly equal to the calling model');
+      assert.deepStrictEqual(clonedModel, model);
     });
 
     it('should not accept any arguments', () => {
       assert.throws(
         () => model.clone('Unexpected argument').then(),
-        /'clone' method called with incorrect parameters./
+        /'clone' method called with incorrect parameters./,
       );
     });
   });
-
 });
diff --git a/src/bindings/js/node/tests/unit/pre_post_processor.test.js b/src/bindings/js/node/tests/unit/pre_post_processor.test.js
index adb02dc082e797..04bef1fead6dac 100644
--- a/src/bindings/js/node/tests/unit/pre_post_processor.test.js
+++ b/src/bindings/js/node/tests/unit/pre_post_processor.test.js
@@ -8,7 +8,6 @@ const { describe, it, before, beforeEach } = require('node:test');
 const { testModels, getModelPath, isModelAvailable } = require('./utils.js');
 
 describe('ov.preprocess.PrePostProcessor tests', () => {
-
   let testXml = null;
   let core = null;
   let model = null;
@@ -17,7 +16,6 @@ describe('ov.preprocess.PrePostProcessor tests', () => {
     await isModelAvailable(testModels.testModelFP32);
     testXml = getModelPath().xml;
     core = new ov.Core();
-
   });
 
   beforeEach(() => {
@@ -25,192 +23,278 @@ describe('ov.preprocess.PrePostProcessor tests', () => {
   });
 
   describe('PrePostProcess', () => {
-
     it('input() ', () => {
       assert.doesNotThrow(() =>
-        new ov.preprocess.PrePostProcessor(model).input());
+        new ov.preprocess.PrePostProcessor(model).input(),
+      );
     });
 
     it('input(size_t input_index)', () => {
       assert.doesNotThrow(() =>
-        new ov.preprocess.PrePostProcessor(model).input(0));
+        new ov.preprocess.PrePostProcessor(model).input(0),
+      );
     });
 
     it('input(const std::string& tensor_name)', () => {
       assert.doesNotThrow(() =>
-        new ov.preprocess.PrePostProcessor(model).input('data'));
+        new ov.preprocess.PrePostProcessor(model).input('data'),
+      );
     });
 
     it('output() ', () => {
       assert.doesNotThrow(() =>
-        new ov.preprocess.PrePostProcessor(model).output());
+        new ov.preprocess.PrePostProcessor(model).output(),
+      );
     });
 
     it('output(size_t output_index)', () => {
       assert.doesNotThrow(() =>
-        new ov.preprocess.PrePostProcessor(model).output(0));
+        new ov.preprocess.PrePostProcessor(model).output(0),
+      );
     });
 
     it('output(const std::string& tensor_name)', () => {
       assert.doesNotThrow(() =>
-        new ov.preprocess.PrePostProcessor(model).output('fc_out'));
+        new ov.preprocess.PrePostProcessor(model).output('fc_out'),
+      );
     });
-
   });
 
   describe('InputInfo', () => {
     it('tensor()', () => {
       assert.doesNotThrow(() =>
-        new ov.preprocess.PrePostProcessor(model).input(0).tensor());
+        new ov.preprocess.PrePostProcessor(model).input(0).tensor(),
+      );
     });
 
     it('preprocess()', () => {
       assert.doesNotThrow(() =>
-        new ov.preprocess.PrePostProcessor(model).input(0).preprocess());
+        new ov.preprocess.PrePostProcessor(model).input(0).preprocess(),
+      );
     });
 
     it('model()', () => {
       assert.doesNotThrow(() =>
-        new ov.preprocess.PrePostProcessor(model).input(0).model());
+        new ov.preprocess.PrePostProcessor(model).input(0).model(),
+      );
     });
 
     it('tensor(param) throws', () => {
-      assert.throws(() =>
-        new ov.preprocess.PrePostProcessor(model).input(0).tensor(0),
-      /Function does not take any parameters./);
+      assert.throws(
+        () => new ov.preprocess.PrePostProcessor(model).input(0).tensor(0),
+        /Function does not take any parameters./,
+      );
     });
 
     it('preprocess(param) throws', () => {
-      assert.throws(() =>
-        new ov.preprocess.PrePostProcessor(model).input(0).preprocess(0),
-      /Function does not take any parameters./);
+      assert.throws(
+        () => new ov.preprocess.PrePostProcessor(model).input(0).preprocess(0),
+        /Function does not take any parameters./,
+      );
     });
 
     it('model(param) throws', () => {
-      assert.throws(() =>
-        new ov.preprocess.PrePostProcessor(model).input(0).model(0),
-      /Function does not take any parameters./);
+      assert.throws(
+        () => new ov.preprocess.PrePostProcessor(model).input(0).model(0),
+        /Function does not take any parameters./,
+      );
     });
 
     it('tensor().setElementType()', () => {
       assert.doesNotThrow(() =>
-        new ov.preprocess.PrePostProcessor(model).input(0).tensor().setElementType(ov.element.u8));
+        new ov.preprocess.PrePostProcessor(model)
+          .input(0)
+          .tensor()
+          .setElementType(ov.element.u8),
+      );
     });
 
     it('tensor().setElementType() throws', () => {
-      assert.throws(() =>
-        new ov.preprocess.PrePostProcessor(model).input(0).tensor().setElementType(),
-      /Wrong number of parameters./);
+      assert.throws(
+        () =>
+          new ov.preprocess.PrePostProcessor(model)
+            .input(0)
+            .tensor()
+            .setElementType(),
+        /Wrong number of parameters./,
+      );
     });
 
     it('tensor().setElementType() throws', () => {
-      assert.throws(() =>
-        new ov.preprocess.PrePostProcessor(model).input(0).tensor().setElementType('invalidType'),
-      /Cannot create ov::element::Type/);
+      assert.throws(
+        () =>
+          new ov.preprocess.PrePostProcessor(model)
+            .input(0)
+            .tensor()
+            .setElementType('invalidType'),
+        /Cannot create ov::element::Type/,
+      );
     });
 
     it('tensor().SetShape()', () => {
       assert.doesNotThrow(() =>
-        new ov.preprocess.PrePostProcessor(model).input(0).tensor().setShape([1, 10]));
+        new ov.preprocess.PrePostProcessor(model)
+          .input(0)
+          .tensor()
+          .setShape([1, 10]),
+      );
     });
 
     it('tensor().SetShape() throws', () => {
-      assert.throws(() =>
-        new ov.preprocess.PrePostProcessor(model).input(0).tensor().setShape(),
-      /Wrong number of parameters./);
+      assert.throws(
+        () =>
+          new ov.preprocess.PrePostProcessor(model)
+            .input(0)
+            .tensor()
+            .setShape(),
+        /Wrong number of parameters./,
+      );
     });
 
     it('tensor().setLayout()', () => {
       assert.doesNotThrow(() =>
-        new ov.preprocess.PrePostProcessor(model).input(0).tensor().setLayout('NHWC'));
+        new ov.preprocess.PrePostProcessor(model)
+          .input(0)
+          .tensor()
+          .setLayout('NHWC'),
+      );
     });
 
     it('tensor().setLayout() throws', () => {
-      assert.throws(() =>
-        new ov.preprocess.PrePostProcessor(model).input(0).tensor().setLayout(),
-      /Wrong number of parameters./);
+      assert.throws(
+        () =>
+          new ov.preprocess.PrePostProcessor(model)
+            .input(0)
+            .tensor()
+            .setLayout(),
+        /Wrong number of parameters./,
+      );
     });
 
     it('preprocess().resize()', () => {
       assert.doesNotThrow(() =>
-        new ov.preprocess.PrePostProcessor(model).input(0).preprocess().resize(
-          ov.preprocess.resizeAlgorithm.RESIZE_LINEAR,
-        ));
+        new ov.preprocess.PrePostProcessor(model)
+          .input(0)
+          .preprocess()
+          .resize(ov.preprocess.resizeAlgorithm.RESIZE_LINEAR),
+      );
     });
 
     it('preprocess().resize() throws', () => {
-      assert.throws(() =>
-        new ov.preprocess.PrePostProcessor(model).input(0).preprocess().resize(
-          ov.preprocess.resizeAlgorithm.RESIZE_LINEAR, 'extraArg',
-        ),
-      /Wrong number of parameters./);
+      assert.throws(
+        () =>
+          new ov.preprocess.PrePostProcessor(model)
+            .input(0)
+            .preprocess()
+            .resize(ov.preprocess.resizeAlgorithm.RESIZE_LINEAR, 'extraArg'),
+        /Wrong number of parameters./,
+      );
     });
 
     it('preprocess().resize() no arg throws', () => {
-      assert.throws(() =>
-        new ov.preprocess.PrePostProcessor(model).input(0).preprocess().resize(),
-      /Wrong number of parameters./);
+      assert.throws(
+        () =>
+          new ov.preprocess.PrePostProcessor(model)
+            .input(0)
+            .preprocess()
+            .resize(),
+        /Wrong number of parameters./,
+      );
     });
 
     it('model().setLayout()', () => {
       assert.doesNotThrow(() =>
-        new ov.preprocess.PrePostProcessor(model).input(0).model().setLayout('NCHW'));
+        new ov.preprocess.PrePostProcessor(model)
+          .input(0)
+          .model()
+          .setLayout('NCHW'),
+      );
     });
 
     it('model().setLayout() throws', () => {
-      assert.throws(() =>
-        new ov.preprocess.PrePostProcessor(model).input(0).model().setLayout('NCHW', 'extraArg'),
-      /Wrong number of parameters./);
+      assert.throws(
+        () =>
+          new ov.preprocess.PrePostProcessor(model)
+            .input(0)
+            .model()
+            .setLayout('NCHW', 'extraArg'),
+        /Wrong number of parameters./,
+      );
     });
 
     it('model().setLayout() throws', () => {
       assert.throws(() =>
-        new ov.preprocess.PrePostProcessor(model).input(0).model().setLayout('invalidLayout')
+        new ov.preprocess.PrePostProcessor(model)
+          .input(0)
+          .model()
+          .setLayout('invalidLayout'),
       );
     });
-
   });
 
   describe('OutputInfo', () => {
     it('tensor()', () => {
       assert.doesNotThrow(() =>
-        new ov.preprocess.PrePostProcessor(model).output(0).tensor());
+        new ov.preprocess.PrePostProcessor(model).output(0).tensor(),
+      );
     });
 
     it('tensor(param) throws', () => {
-      assert.throws(() =>
-        new ov.preprocess.PrePostProcessor(model).output(0).tensor(0),
-      /Function does not take any parameters./);
+      assert.throws(
+        () => new ov.preprocess.PrePostProcessor(model).output(0).tensor(0),
+        /Function does not take any parameters./,
+      );
     });
 
     it('tensor().setElementType()', () => {
       assert.doesNotThrow(() =>
-        new ov.preprocess.PrePostProcessor(model).output(0).tensor().setElementType(ov.element.u8));
+        new ov.preprocess.PrePostProcessor(model)
+          .output(0)
+          .tensor()
+          .setElementType(ov.element.u8),
+      );
     });
 
     it('tensor().setElementType() throws', () => {
-      assert.throws(() =>
-        new ov.preprocess.PrePostProcessor(model).output(0).tensor().setElementType(),
-      /Wrong number of parameters./);
+      assert.throws(
+        () =>
+          new ov.preprocess.PrePostProcessor(model)
+            .output(0)
+            .tensor()
+            .setElementType(),
+        /Wrong number of parameters./,
+      );
     });
 
     it('tensor().setElementType() throws', () => {
-      assert.throws(() =>
-        new ov.preprocess.PrePostProcessor(model).output(0).tensor().setElementType('invalidType'),
-      /Cannot create ov::element::Type/);
+      assert.throws(
+        () =>
+          new ov.preprocess.PrePostProcessor(model)
+            .output(0)
+            .tensor()
+            .setElementType('invalidType'),
+        /Cannot create ov::element::Type/,
+      );
     });
 
     it('tensor().setLayout()', () => {
       assert.doesNotThrow(() =>
-        new ov.preprocess.PrePostProcessor(model).output(0).tensor().setLayout('NHWC'));
+        new ov.preprocess.PrePostProcessor(model)
+          .output(0)
+          .tensor()
+          .setLayout('NHWC'),
+      );
     });
 
     it('tensor().setLayout() throws', () => {
-      assert.throws(() =>
-        new ov.preprocess.PrePostProcessor(model).output(0).tensor().setLayout(),
-      /Wrong number of parameters./);
+      assert.throws(
+        () =>
+          new ov.preprocess.PrePostProcessor(model)
+            .output(0)
+            .tensor()
+            .setLayout(),
+        /Wrong number of parameters./,
+      );
     });
-
   });
-
 });
diff --git a/src/bindings/js/node/tests/unit/read_model.test.js b/src/bindings/js/node/tests/unit/read_model.test.js
index ab0add15d59ce5..902ee2d963d161 100644
--- a/src/bindings/js/node/tests/unit/read_model.test.js
+++ b/src/bindings/js/node/tests/unit/read_model.test.js
@@ -11,11 +11,10 @@ const { testModels, isModelAvailable, getModelPath } = require('./utils.js');
 const { xml: modelPath, bin: weightsPath } = getModelPath();
 
 describe('Tests for reading model.', () => {
-
   let modelFile = null;
   let modelStr = null;
   let weightsFile = null;
-  let weightsTensor= null;
+  let weightsTensor = null;
   let core = null;
 
   before(async () => {
@@ -27,7 +26,11 @@ describe('Tests for reading model.', () => {
 
   beforeEach(() => {
     core = new ov.Core();
-    weightsTensor = new ov.Tensor(ov.element.u8, [weightsFile.buffer.byteLength], new Uint8Array(weightsFile.buffer));
+    weightsTensor = new ov.Tensor(
+      ov.element.u8,
+      [weightsFile.buffer.byteLength],
+      new Uint8Array(weightsFile.buffer),
+    );
   });
 
   describe('Core.readModeSync', () => {
@@ -51,10 +54,7 @@ describe('Tests for reading model.', () => {
     });
 
     it('readModelSync(modelString, weightsTensor) ', () => {
-      const model = core.readModelSync(
-        modelStr,
-        weightsTensor,
-      );
+      const model = core.readModelSync(modelStr, weightsTensor);
       assert.ok(model instanceof ov.Model);
       assert.equal(model.inputs.length, 1);
     });
@@ -81,15 +81,12 @@ describe('Tests for reading model.', () => {
     });
 
     it('readModel(modelString, weightsTensor) ', async () => {
-      const model = await core.readModel(
-        modelStr,
-        weightsTensor,
-      );
+      const model = await core.readModel(modelStr, weightsTensor);
       assert.ok(model instanceof ov.Model);
       assert.equal(model.inputs.length, 1);
     });
 
-    it('readModel(modelUint8ArrayBuffer, weightsUint8ArrayBuffer) ', async () => {
+    it('readModel(Uint8ArrayBuffer, Uint8ArrayBuffer) ', async () => {
       const model = await core.readModel(
         new Uint8Array(modelFile.buffer),
         new Uint8Array(weightsFile.buffer),
@@ -97,5 +94,4 @@ describe('Tests for reading model.', () => {
       assert.equal(model.inputs.length, 1);
     });
   });
-
 });
diff --git a/src/bindings/js/node/tests/unit/tensor.test.js b/src/bindings/js/node/tests/unit/tensor.test.js
index 5f03030d98cc02..656bdf9b122e58 100644
--- a/src/bindings/js/node/tests/unit/tensor.test.js
+++ b/src/bindings/js/node/tests/unit/tensor.test.js
@@ -8,74 +8,112 @@ const { test, describe, it, before } = require('node:test');
 const getRandomBigInt = require('random-bigint');
 
 describe('ov.Tensor tests', () => {
-
   let shape = null;
   let elemNum = null;
   let data = null;
-  let params =null;
+  let params = null;
 
   before(() => {
     shape = [1, 3, 224, 224];
     elemNum = 1 * 3 * 224 * 224;
-    data = Float32Array.from({ length: elemNum }, () => Math.random() );
+    data = Float32Array.from({ length: elemNum }, () => Math.random());
     params = [
-      [ov.element.i8, 'i8', Int8Array.from({ length: elemNum }, () => Math.random() )],
-      [ov.element.u8, 'u8', Uint8Array.from({ length: elemNum }, () => Math.random() )],
-      [ov.element.i16, 'i16', Int16Array.from({ length: elemNum }, () => Math.random() )],
-      [ov.element.u16, 'u16', Uint16Array.from({ length: elemNum }, () => Math.random() )],
-      [ov.element.i32, 'i32', Int32Array.from({ length: elemNum }, () => Math.random() )],
-      [ov.element.u32, 'u32', Uint32Array.from({ length: elemNum }, () => Math.random() )],
-      [ov.element.f32, 'f32', Float32Array.from({ length: elemNum }, () => Math.random() )],
-      [ov.element.f64, 'f64', Float64Array.from({ length: elemNum }, () => Math.random() )],
-      [ov.element.i64, 'i64', BigInt64Array.from({ length: elemNum }, () => getRandomBigInt(10) )],
-      [ov.element.u64, 'u64', BigUint64Array.from({ length: elemNum }, () => getRandomBigInt(10) )],
+      [
+        ov.element.i8,
+        'i8',
+        Int8Array.from({ length: elemNum }, () => Math.random()),
+      ],
+      [
+        ov.element.u8,
+        'u8',
+        Uint8Array.from({ length: elemNum }, () => Math.random()),
+      ],
+      [
+        ov.element.i16,
+        'i16',
+        Int16Array.from({ length: elemNum }, () => Math.random()),
+      ],
+      [
+        ov.element.u16,
+        'u16',
+        Uint16Array.from({ length: elemNum }, () => Math.random()),
+      ],
+      [
+        ov.element.i32,
+        'i32',
+        Int32Array.from({ length: elemNum }, () => Math.random()),
+      ],
+      [
+        ov.element.u32,
+        'u32',
+        Uint32Array.from({ length: elemNum }, () => Math.random()),
+      ],
+      [
+        ov.element.f32,
+        'f32',
+        Float32Array.from({ length: elemNum }, () => Math.random()),
+      ],
+      [
+        ov.element.f64,
+        'f64',
+        Float64Array.from({ length: elemNum }, () => Math.random()),
+      ],
+      [
+        ov.element.i64,
+        'i64',
+        BigInt64Array.from({ length: elemNum }, () => getRandomBigInt(10)),
+      ],
+      [
+        ov.element.u64,
+        'u64',
+        BigUint64Array.from({ length: elemNum }, () => getRandomBigInt(10)),
+      ],
     ];
   });
 
   test('Test for number of arguments in tensor', () => {
-    assert.throws( () => new ov.Tensor(ov.element.f32, shape, data, params),
-      {message: /Invalid number of arguments for Tensor constructor./});
+    assert.throws(() => new ov.Tensor(ov.element.f32, shape, data, params), {
+      message: /Invalid number of arguments for Tensor constructor./,
+    });
   });
 
-  describe('Tensor without data parameters', () => {
-    it('Tensor should have array with zeros and numbers of elements according to the shape', () => {
-      const tensor = new ov.Tensor(ov.element.f32, shape);
-      assert.strictEqual(tensor.data.length, elemNum);
-    });
+  test('Tensor without data parameters', () => {
+    const tensor = new ov.Tensor(ov.element.f32, shape);
+    assert.strictEqual(tensor.data.length, elemNum);
   });
 
   describe('Tensor data', () => {
     it('set tensor data with element type', () => {
-      params.forEach(([type, stringType, data]) => {
+      params.forEach(([type, , data]) => {
         const tensor = new ov.Tensor(type, shape, data);
         assert.deepStrictEqual(tensor.data, data);
       });
     });
 
     it('create string tensor', () => {
-      const str_arr = ['text', 'more text', 'even more text'];
-      const tensor = new ov.Tensor(str_arr);
-      assert.deepStrictEqual(tensor.data, str_arr);
+      const strArray = ['text', 'more text', 'even more text'];
+      const tensor = new ov.Tensor(strArray);
+      assert.deepStrictEqual(tensor.data, strArray);
     });
 
     it('create string tensor', () => {
-      const str_arr = ['text', 'more text', 'even more text'];
-      const tensor = new ov.Tensor(str_arr);
-      assert.deepStrictEqual(tensor.data, str_arr);
+      const strArray = ['text', 'more text', 'even more text'];
+      const tensor = new ov.Tensor(strArray);
+      assert.deepStrictEqual(tensor.data, strArray);
     });
 
     it('string tensor - passed array does not contain string elements', () => {
-      const str_arr = ['text', true];
-      assert.throws(() => { new ov.Tensor(str_arr);},
-        /The array passed to create string tensor must contain only strings./
-      );
+      const strArray = ['text', true];
+      assert.throws(() => {
+        new ov.Tensor(strArray);
+      }, /The array passed to create string tensor must contain only strings./);
     });
 
     it('set string tensor data', () => {
-      const str_arr = ['H', 'e', 'l', 'l', 'o'];
+      const strArray = ['H', 'e', 'l', 'l', 'o'];
       const tensor = new ov.Tensor(ov.element.string, [1, 1, 1, 5]);
-      tensor.data = str_arr;
-      assert.deepStrictEqual(tensor.data, str_arr);
+      tensor.data = strArray;
+      assert.deepStrictEqual(tensor.data, strArray);
     });
 
     it('test tensor getData()', () => {
@@ -84,30 +122,27 @@ describe('ov.Tensor tests', () => {
     });
 
     it('test tensor.data setter - different element type throws', () => {
-      const float64_data = Float64Array.from([1, 2, 3] );
+      const float64Data = Float64Array.from([1, 2, 3]);
       const tensor = new ov.Tensor(ov.element.f32, [1, 3]);
       assert.throws(() => {
-        tensor.data = float64_data;},
-      /Passed array must have the same size as the Tensor!/
-      );
+        tensor.data = float64Data;
+      }, /Passed array must have the same size as the Tensor!/);
     });
 
     it('test tensor.data setter - different element length throws', () => {
-      const float64_data = Float64Array.from([1, 2, 3] );
+      const float64Data = Float64Array.from([1, 2, 3]);
       const tensor = new ov.Tensor(ov.element.f64, [1, 2]);
       assert.throws(() => {
-        tensor.data = float64_data;},
-      /Passed array must have the same size as the Tensor!/
-      );
+        tensor.data = float64Data;
+      }, /Passed array must have the same size as the Tensor!/);
     });
 
     it('test tensor.data setter', () => {
       const testString = 'test';
       const tensor = new ov.Tensor(ov.element.f64, [1, 2]);
       assert.throws(() => {
-        tensor.data = testString;},
-      /Passed argument must be TypedArray, or Array if the tensor type is string./
-      );
+        tensor.data = testString;
+      }, /Passed argument must be TypedArray, or Array/);
     });
 
     it('test tensor.data setter', () => {
@@ -130,40 +165,45 @@ describe('ov.Tensor tests', () => {
       const buffer = new ArrayBuffer(size);
       const view = new Float32Array(buffer);
       view.set(data);
-      assert.throws( () => new ov.Tensor(ov.element.f32, shape, view),
-        {message: /Memory allocated using shape and element::type mismatch/});
+      assert.throws(() => new ov.Tensor(ov.element.f32, shape, view), {
+        message: /Memory allocated using shape and element::type mismatch/,
+      });
     });
 
     it('third argument of a tensor cannot be an ArrayBuffer', () => {
       assert.throws(
         () => new ov.Tensor(ov.element.f32, shape, new ArrayBuffer(1234)),
-        {message: /Third argument of a tensor must be TypedArray./});
+        { message: /Third argument of a tensor must be TypedArray./ },
+      );
     });
 
     it('third argument of a tensor cannot be an array object', () => {
-      assert.throws(
-        () => new ov.Tensor(ov.element.f32, shape, [1, 2, 3, 4]),
-        {message: /Third argument of a tensor must be TypedArray./});
+      assert.throws(() => new ov.Tensor(ov.element.f32, shape, [1, 2, 3, 4]), {
+        message: /Third argument of a tensor must be TypedArray./,
+      });
     });
   });
 
   describe('Tensor shape', () => {
-
     it('ov::Shape from an array object', () => {
       const tensor = new ov.Tensor(ov.element.f32, [1, 3, 224, 224], data);
       assert.deepStrictEqual(tensor.getShape(), [1, 3, 224, 224]);
     });
 
     it('ov::Shape from an array object with floating point numbers', () => {
-      const tensor =
-    new ov.Tensor(ov.element.f32, [1, 3.0, 224.8, 224.4], data);
+      const tensor = new ov.Tensor(
+        ov.element.f32,
+        [1, 3.0, 224.8, 224.4],
+        data,
+      );
       assert.deepStrictEqual(tensor.getShape(), [1, 3, 224, 224]);
     });
 
     it('array argument to create ov::Shape can only contain numbers', () => {
       assert.throws(
         () => new ov.Tensor(ov.element.f32, ['1', 3, 224, 224], data),
-        {message: /Passed array must contain only numbers/});
+        { message: /Passed array must contain only numbers/ },
+      );
     });
 
     it('ov::Shape from TypedArray -> Int32Array', () => {
@@ -176,7 +216,7 @@ describe('ov.Tensor tests', () => {
       const shape = Float32Array.from([1, 224, 224, 3]);
       assert.throws(
         () => new ov.Tensor(ov.element.f32, shape, data),
-        /Passed argument must be an Int32Array or a Uint32Array./
+        /Passed argument must be an Int32Array or a Uint32Array./,
       );
     });
 
@@ -184,30 +224,27 @@ describe('ov.Tensor tests', () => {
       const shape = Int32Array.from([1, 224, 224, 3]);
       assert.throws(
         () => new ov.Tensor(ov.element.f32, shape.buffer, data),
-        /Passed argument must be of type Array or TypedArray./
+        /Passed argument must be of type Array or TypedArray./,
       );
     });
 
     it('getShape() method does not accept parameters', () => {
       const tensor = new ov.Tensor(ov.element.f32, [1, 3, 224, 224], data);
-      assert.throws(
-        () => tensor.getShape(1, 2, 3),
-        { message: 'No parameters are allowed for the getShape() method.'}
-      );
+      assert.throws(() => tensor.getShape(1, 2, 3), {
+        message: 'No parameters are allowed for the getShape() method.',
+      });
     });
   });
 
   describe('Tensor element type', () => {
     it('comparisons of ov.element to string', () => {
       params.forEach(([elemType, val]) => {
-
         assert.strictEqual(elemType, val);
       });
     });
 
     it('comparisons of ov.element got from Tensor object', () => {
       params.forEach(([elemType, , data]) => {
-
         const tensor = new ov.Tensor(elemType, shape, data);
         assert.strictEqual(tensor.getElementType(), elemType);
       });
@@ -215,7 +252,6 @@ describe('ov.Tensor tests', () => {
   });
 
   describe('Tensor getSize', () => {
-
     it('getSize returns the correct total number of elements', () => {
       const tensor = new ov.Tensor(ov.element.f32, shape, data);
       const expectedSize = shape.reduce((acc, dim) => acc * dim, 1);
@@ -224,18 +260,16 @@ describe('ov.Tensor tests', () => {
 
     it('getSize should throw an error if arguments are provided', () => {
       const tensor = new ov.Tensor(ov.element.f32, shape, data);
-      assert.throws(
-        () => tensor.getSize(1),
-        { message: 'getSize() does not accept any arguments.' }
-      );
+      assert.throws(() => tensor.getSize(1), {
+        message: 'getSize() does not accept any arguments.',
+      });
     });
   });
 
   describe('Tensor getSize for various shapes', () => {
-
-    it('calculates size correctly for a common image data shape [3, 224, 224]', () => {
+    it('calculates size for a common image data shape [3, 224, 224]', () => {
       const shape = [3, 224, 224];
-      const expectedSize = 3*224*224;
+      const expectedSize = 3 * 224 * 224;
       const tensorData = new Float32Array(expectedSize).fill(0);
       const tensor = new ov.Tensor(ov.element.f32, shape, tensorData);
       assert.strictEqual(tensor.getSize(), expectedSize);
diff --git a/src/bindings/js/node/tests/unit/utils.js b/src/bindings/js/node/tests/unit/utils.js
index cf541a332b103f..c2089e30b4cdc8 100644
--- a/src/bindings/js/node/tests/unit/utils.js
+++ b/src/bindings/js/node/tests/unit/utils.js
@@ -4,7 +4,10 @@
 
 const path = require('path');
 const fs = require('node:fs/promises');
-const { downloadFile, checkIfPathExists } = require('../../scripts/download_runtime');
+const {
+  downloadFile,
+  checkIfPathExists,
+} = require('../../scripts/download_runtime');
 
 const modelDir = 'tests/unit/test_models/';
 const testModels = {
@@ -25,7 +28,7 @@ module.exports = {
   testModels,
 };
 
-function getModelPath(isFP16=false) {
+function getModelPath(isFP16 = false) {
   const modelName = `test_model_fp${isFP16 ? 16 : 32}`;
 
   return {
@@ -63,7 +66,8 @@ async function isModelAvailable(model) {
   const modelExists = await checkIfPathExists(modelPath);
   if (modelExists) return;
 
-  console.log('\n\nTestModel cannot be found.\nPlease run `npm run test_setup`.\n\n');
+  console.log(
+    '\n\nTestModel cannot be found.\nPlease run `npm run test_setup`.\n\n',
+  );
   process.exit(1);
-
 }

From fa918602c5e2f4403b7849a7d0a5afc2cfab16bc Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Fri, 20 Sep 2024 11:05:58 +0400
Subject: [PATCH 19/49] Bump actions/setup-node from 4.0.3 to 4.0.4 (#26701)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Bumps [actions/setup-node](https://github.com/actions/setup-node) from
4.0.3 to 4.0.4.
<details>
<summary>Release notes</summary>
<p><em>Sourced from <a
href="https://github.com/actions/setup-node/releases">actions/setup-node's
releases</a>.</em></p>
<blockquote>
<h2>v4.0.4</h2>
<h2>What's Changed</h2>
<ul>
<li>Add workflow file for publishing releases to immutable action
package by <a
href="https://github.com/Jcambass"><code>@​Jcambass</code></a> in <a
href="https://redirect.github.com/actions/setup-node/pull/1125">actions/setup-node#1125</a></li>
<li>Enhance Windows ARM64 Setup and Update micromatch Dependency by <a
href="https://github.com/priyagupta108"><code>@​priyagupta108</code></a>
in <a
href="https://redirect.github.com/actions/setup-node/pull/1126">actions/setup-node#1126</a></li>
</ul>
<h3>Documentation changes:</h3>
<ul>
<li>Documentation update in the README file by <a
href="https://github.com/suyashgaonkar"><code>@​suyashgaonkar</code></a>
in <a
href="https://redirect.github.com/actions/setup-node/pull/1106">actions/setup-node#1106</a></li>
<li>Correct invalid 'lts' version string reference by <a
href="https://github.com/fulldecent"><code>@​fulldecent</code></a> in <a
href="https://redirect.github.com/actions/setup-node/pull/1124">actions/setup-node#1124</a></li>
</ul>
<h2>New Contributors</h2>
<ul>
<li><a
href="https://github.com/suyashgaonkar"><code>@​suyashgaonkar</code></a>
made their first contribution in <a
href="https://redirect.github.com/actions/setup-node/pull/1106">actions/setup-node#1106</a></li>
<li><a
href="https://github.com/priyagupta108"><code>@​priyagupta108</code></a>
made their first contribution in <a
href="https://redirect.github.com/actions/setup-node/pull/1126">actions/setup-node#1126</a></li>
<li><a href="https://github.com/Jcambass"><code>@​Jcambass</code></a>
made their first contribution in <a
href="https://redirect.github.com/actions/setup-node/pull/1125">actions/setup-node#1125</a></li>
<li><a
href="https://github.com/fulldecent"><code>@​fulldecent</code></a> made
their first contribution in <a
href="https://redirect.github.com/actions/setup-node/pull/1124">actions/setup-node#1124</a></li>
</ul>
<p><strong>Full Changelog</strong>: <a
href="https://github.com/actions/setup-node/compare/v4...v4.0.4">https://github.com/actions/setup-node/compare/v4...v4.0.4</a></p>
</blockquote>
</details>
<details>
<summary>Commits</summary>
<ul>
<li><a
href="https://github.com/actions/setup-node/commit/0a44ba7841725637a19e28fa30b79a866c81b0a6"><code>0a44ba7</code></a>
Correct version string (<a
href="https://redirect.github.com/actions/setup-node/issues/1124">#1124</a>)</li>
<li><a
href="https://github.com/actions/setup-node/commit/97ca147735c170fb35096b39ef17a0fc5d9270ac"><code>97ca147</code></a>
Merge pull request <a
href="https://redirect.github.com/actions/setup-node/issues/1125">#1125</a>
from actions/add-is-release-workflow</li>
<li><a
href="https://github.com/actions/setup-node/commit/aa363ded8fefb1e43b7a6cb669a222a98c09eb57"><code>aa363de</code></a>
Create publish-immutable-action.yml</li>
<li><a
href="https://github.com/actions/setup-node/commit/1c7b2db92075f828bee89d7e19d33a911d15e7b3"><code>1c7b2db</code></a>
Fix: windows arm64 setup (<a
href="https://redirect.github.com/actions/setup-node/issues/1126">#1126</a>)</li>
<li><a
href="https://github.com/actions/setup-node/commit/26961cf329f22f6837d5f54c3efd76b480300ace"><code>26961cf</code></a>
Documentation update in the README file (<a
href="https://redirect.github.com/actions/setup-node/issues/1106">#1106</a>)</li>
<li>See full diff in <a
href="https://github.com/actions/setup-node/compare/1e60f620b9541d16bece96c5465dc8ee9832be0b...0a44ba7841725637a19e28fa30b79a866c81b0a6">compare
view</a></li>
</ul>
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---
 .github/workflows/job_openvino_js.yml        | 2 +-
 .github/workflows/windows_vs2019_release.yml | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/job_openvino_js.yml b/.github/workflows/job_openvino_js.yml
index 5fd603923752a6..3b1911c28cea4e 100644
--- a/.github/workflows/job_openvino_js.yml
+++ b/.github/workflows/job_openvino_js.yml
@@ -52,7 +52,7 @@ jobs:
 
       - name: Setup Node ${{ env.NODE_VERSION }}
         if: runner.os != 'Linux' # Node is already installed in the Docker image
-        uses: actions/setup-node@1e60f620b9541d16bece96c5465dc8ee9832be0b # v4.0.3
+        uses: actions/setup-node@0a44ba7841725637a19e28fa30b79a866c81b0a6 # v4.0.4
         with:
           node-version: ${{ env.NODE_VERSION }}
 
diff --git a/.github/workflows/windows_vs2019_release.yml b/.github/workflows/windows_vs2019_release.yml
index b9d5d1687f773e..43c3879ba4ae8d 100644
--- a/.github/workflows/windows_vs2019_release.yml
+++ b/.github/workflows/windows_vs2019_release.yml
@@ -169,7 +169,7 @@ jobs:
           path: ${{ env.OPENVINO_JS_LIBS_DIR }}
 
       - name: Setup Node ${{ env.NODE_VERSION }}
-        uses: actions/setup-node@1e60f620b9541d16bece96c5465dc8ee9832be0b # v4.0.3
+        uses: actions/setup-node@0a44ba7841725637a19e28fa30b79a866c81b0a6 # v4.0.4
         with:
           node-version: ${{ env.NODE_VERSION }}
 

From 9c4bd4b5292185500a7a5a276aef4321fab87d48 Mon Sep 17 00:00:00 2001
From: Eddy Kim <eddy.kim@intel.com>
Date: Fri, 20 Sep 2024 16:08:10 +0900
Subject: [PATCH 20/49] [GPU] update unfusing logic to rename additional deps
 of fc (#26563)

### Details:
- `fully_connected` has additional dependencies (such as weights, bias,
and so on) that cannot be found in the `input`.
- This PR updates the unfusing logic to rename these additional input
nodes for `fully_connected` layers.

### Tickets:
 - 152248
 - 152867
---
 .../intel_gpu/src/graph/primitive_inst.cpp    | 16 +++++---
 .../fusions/fully_connected_fusion_test.cpp   | 40 +++++++++++++++++++
 2 files changed, 51 insertions(+), 5 deletions(-)

diff --git a/src/plugins/intel_gpu/src/graph/primitive_inst.cpp b/src/plugins/intel_gpu/src/graph/primitive_inst.cpp
index af99500afb6225..00c321e1f1db51 100644
--- a/src/plugins/intel_gpu/src/graph/primitive_inst.cpp
+++ b/src/plugins/intel_gpu/src/graph/primitive_inst.cpp
@@ -2291,18 +2291,25 @@ cldnn::network::ptr primitive_inst::get_unfused_subgraph() {
         std::vector<primitive_id> outer_dep_ids;
         // Add input primitives: constants are moved as is
         // Any other primitive types are replaced with input_layout
+        auto prim_of_fused_node = std::const_pointer_cast<primitive>(_impl_params->desc);
+        size_t dep_idx = 0;
         for (auto& dep : _node->get_dependencies()) {
+            cldnn::primitive_id dep_id = dep.first->id();
+
             if (dep.first->is_type<data>()) {
                 auto& data_node = dep.first->as<data>();
-                auto data_prim = *data_node.get_primitive();
+                // need to rename primitive ids of dependent data of the current fused nodes with those in the original primitive
+                if (dep_idx >= prim_of_fused_node->input_size() && dep_idx < prim_of_fused_node->dependencies().size())
+                    dep_id = prim_of_fused_node->dependencies()[dep_idx].pid;
                 // mem field of original primitive can be nullified during transfer_memory_to_device pass, thus use mem from program_node
-                data_prim.mem = data_node.get_attached_memory_ptr();
+                data data_prim(dep_id, data_node.get_attached_memory_ptr());
                 t.add(data_prim);
             } else {
-                input_layout in_prim(dep.first->id(), dep.first->get_output_layout());
+                input_layout in_prim(dep_id, dep.first->get_output_layout());
                 t.add(in_prim);
             }
-            outer_dep_ids.push_back(dep.first->id());
+            outer_dep_ids.push_back(dep_id);
+            dep_idx += 1;
         }
 
         // Create the primitive itself
@@ -2346,7 +2353,6 @@ cldnn::network::ptr primitive_inst::get_unfused_subgraph() {
             outer_dep_ids.push_back(prim->id);
         }
         // Samely, need to update dependency of the current fused nodes' input primitive ids with those in the current program
-        auto prim_of_fused_node = std::const_pointer_cast<primitive>(_impl_params->desc);
         for (size_t i = 0; i < prim_of_fused_node->input.size(); ++i) {
             auto& in = prim_of_fused_node->input[i];
             if (std::find_if(outer_dep_ids.begin(), outer_dep_ids.end(),
diff --git a/src/plugins/intel_gpu/tests/unit/fusions/fully_connected_fusion_test.cpp b/src/plugins/intel_gpu/tests/unit/fusions/fully_connected_fusion_test.cpp
index 5e1b381328301b..4a1133ab00507f 100644
--- a/src/plugins/intel_gpu/tests/unit/fusions/fully_connected_fusion_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/fusions/fully_connected_fusion_test.cpp
@@ -197,6 +197,7 @@ class FullyConnectedFusingTestOneDNN : public BaseFusingTest<fully_connected_tes
 
 
 #define CASE_FC_FP16_INT4_COMP_1 { 1, 128 }, { 1, 128 }, { 128, 128 }, data_types::f16, format::bfyx, data_types::u4, format::oiyx, data_types::f16, format::bfyx
+#define CASE_FC_FP16_INT4_COMP_2 { 2, 128 }, { 2, 128 }, { 128, 128 }, data_types::f16, format::bfyx, data_types::u4, format::oiyx, data_types::f16, format::bfyx
 
 /* ----------------------------------------------------------------------------------------------------- */
 /* ---------------------------------------- FC cases --------------------------------------------------- */
@@ -672,6 +673,45 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, fc_compressed_int8_bias_dynamic_onednn, ::
     fully_connected_test_params{ CASE_FC_FP16_INT4_COMP_1, 2, 3 },
 }));
 
+class fc_compressed_int8_bias_prod_unfused_dynamic_onednn : public FullyConnectedFusingTestOneDNN {};
+TEST_P(fc_compressed_int8_bias_prod_unfused_dynamic_onednn, basic) {
+    auto p = GetParam();
+    auto test_input_layout = get_input_layout(p);
+    auto dynamic_input_layout = layout{ov::PartialShape::dynamic(test_input_layout.get_partial_shape().rank()), test_input_layout.data_type, test_input_layout.format};
+    auto data_layout = layout{ ov::PartialShape{p.out_shape[0], 1}, p.default_type, p.default_format };
+
+    auto supports_immad = engine.get_device_info().supports_immad;
+    auto dcomp_zp_name = supports_immad ? "dcomp_zp" : "";
+
+    auto fc_prim = fully_connected("fc_prim", input_info("input"), "weights", "", "scale", dcomp_zp_name, data_types::f16, get_output_dim_size(p), get_input_weights_rank(p));
+    fc_prim.decompression_zero_point_scalar = 8.0f;
+
+    // onednn FC supports scalar ZP for int4 compressed weight.
+    auto dcomp_zp_layout = layout{ {1, 1, 1, 1}, data_types::u8, format::bfyx };
+
+    create_topologies(
+        input_layout("input", dynamic_input_layout),
+        data("weights", get_mem(get_weights_layout(p))),
+        data("scale", get_mem(get_scale_layout(p))),
+        data("bias", get_mem(get_bias_layout(p))),
+        data("dcomp_zp", get_mem(dcomp_zp_layout, 8.0f)),
+        data("mul_data", get_mem(data_layout, -10, 10)),
+        fc_prim,
+        eltwise("bias_add", { input_info("fc_prim"), input_info("bias") }, eltwise_mode::sum),
+        eltwise("mul", { input_info("bias_add"), input_info("mul_data") }, eltwise_mode::prod),
+        reorder("reorder_bfyx", input_info("mul"), p.default_format, data_types::f32)
+    );
+
+    bool is_dynamic = true;
+    cfg_not_fused.set_property(ov::intel_gpu::allow_new_shape_infer(is_dynamic));
+    tolerance = 1.0f;
+    execute(p, false, is_dynamic);
+}
+
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, fc_compressed_int8_bias_prod_unfused_dynamic_onednn, ::testing::ValuesIn(std::vector<fully_connected_test_params>{
+    fully_connected_test_params{ CASE_FC_FP16_INT4_COMP_2, 2, 4 },
+}));
+
 class fc_fp16_eltwise_sub : public FullyConnectedFusingTestOneDNN {
 public:
     void run_test(bool is_caching_test = false) {

From 248028ea81b128dd461d01434ba8fe1013219334 Mon Sep 17 00:00:00 2001
From: Tomasz Jankowski <tomasz1.jankowski@intel.com>
Date: Fri, 20 Sep 2024 09:45:17 +0200
Subject: [PATCH 21/49] [Core] Resolve coverity issues (#26684)

### Tickets:
 - CVS-148594
---
 src/core/src/pass/manager.cpp   | 25 ++++++++++++-------------
 src/core/src/pass/serialize.cpp |  4 ++--
 2 files changed, 14 insertions(+), 15 deletions(-)

diff --git a/src/core/src/pass/manager.cpp b/src/core/src/pass/manager.cpp
index 86c2e57e03599a..9168292f5284c0 100644
--- a/src/core/src/pass/manager.cpp
+++ b/src/core/src/pass/manager.cpp
@@ -21,8 +21,6 @@
 #include "openvino/util/log.hpp"
 #include "perf_counters.hpp"
 
-using namespace std;
-
 #ifdef ENABLE_PROFILING_ITT
 
 namespace ov {
@@ -196,8 +194,9 @@ class Profiler {
 
             bool is_pass_manager = name == m_manager_name;
             if (is_pass_manager) {
-                std::cout << std::setw(25) << left;
+                std::cout << std::setw(25) << std::left;
                 std::cout << "PassManager started: " << m_manager_name << std::endl;
+                std::cout << std::right;
             }
         }
     }
@@ -209,15 +208,15 @@ class Profiler {
 
             bool is_pass_manager = name == m_manager_name;
             if (m_profile_pass.is_bool()) {
-                std::cout << std::setw(25) << left;
+                std::cout << std::setw(25) << std::left;
                 if (is_pass_manager) {
                     std::cout << "PassManager finished: ";
                 } else {
                     std::cout << "  ";
                 }
-                std::cout << std::setw(60) << left << name;
-                std::cout << std::setw(5) << right << stopwatch.get_milliseconds() << "ms " << (applied ? "+" : "-")
-                          << std::endl;
+                std::cout << std::setw(60) << std::left << name;
+                std::cout << std::setw(5) << std::right << stopwatch.get_milliseconds() << "ms "
+                          << (applied ? "+" : "-") << std::endl;
             } else if (m_file.is_open()) {
                 if (is_pass_manager) {
                     m_file << "m;" << name << ";" << stopwatch.get_timer_value().count() << ";" << (applied ? "1" : "0")
@@ -233,7 +232,7 @@ class Profiler {
         }
     }
 
-    void visualize(const shared_ptr<ov::Model>& model, const std::string& pass_name) const {
+    void visualize(const std::shared_ptr<ov::Model>& model, const std::string& pass_name) const {
         static size_t viz_index = 0;
         if (m_visualize.is_enabled()) {
             const auto& _visualize = [&]() {
@@ -256,7 +255,7 @@ class Profiler {
         }
     }
 
-    void serialize(const shared_ptr<ov::Model>& model, const std::string& pass_name) const {
+    void serialize(const std::shared_ptr<ov::Model>& model, const std::string& pass_name) const {
         static size_t serialize_index = 0;
         if (m_serialize.is_enabled()) {
             const auto& _serialize = [&]() {
@@ -330,7 +329,7 @@ void ov::pass::Manager::set_per_pass_validation(bool new_state) {
     m_per_pass_validation = new_state;
 }
 
-bool ov::pass::Manager::run_passes(const shared_ptr<ov::Model>& model) {
+bool ov::pass::Manager::run_passes(const std::shared_ptr<ov::Model>& model) {
     OV_ITT_SCOPED_TASK(ov::itt::domains::core, "pass::Manager::run_passes");
     Profiler profiler(m_name);
 
@@ -375,11 +374,11 @@ bool ov::pass::Manager::run_pass(const std::shared_ptr<PassBase>& pass,
 
     OV_ITT_SCOPE(FIRST_INFERENCE, ov::itt::domains::ov_pass, ov::pass::perf_counters()[pass->get_type_info()]);
 
-    if (auto matcher_pass = dynamic_pointer_cast<MatcherPass>(pass)) {
+    if (auto matcher_pass = std::dynamic_pointer_cast<MatcherPass>(pass)) {
         // GraphRewrite is a temporary container for MatcherPass to make execution on entire ov::Model
         return GraphRewrite(matcher_pass).run_on_model(model);
-    } else if (auto model_pass = dynamic_pointer_cast<ModelPass>(pass)) {
-        if (dynamic_pointer_cast<ov::pass::Validate>(model_pass) && !needs_validate) {
+    } else if (auto model_pass = std::dynamic_pointer_cast<ModelPass>(pass)) {
+        if (std::dynamic_pointer_cast<ov::pass::Validate>(model_pass) && !needs_validate) {
             return false;
         }
         return model_pass->run_on_model(model);
diff --git a/src/core/src/pass/serialize.cpp b/src/core/src/pass/serialize.cpp
index 55acbda9b41a66..409dcad066d7a6 100644
--- a/src/core/src/pass/serialize.cpp
+++ b/src/core/src/pass/serialize.cpp
@@ -1278,8 +1278,8 @@ bool pass::Serialize::run_on_model(const std::shared_ptr<ov::Model>& model) {
             // hence we need to delete it here in case of failure
             xml_file.close();
             bin_file.close();
-            std::remove(m_xmlPath.c_str());
-            std::remove(m_binPath.c_str());
+            std::ignore = std::remove(m_xmlPath.c_str());
+            std::ignore = std::remove(m_binPath.c_str());
             throw;
         }
     }

From 36b6e3c80f59f034a30a953d021131e3ec05fc7a Mon Sep 17 00:00:00 2001
From: Sebastian Golebiewski <sebastianx.golebiewski@intel.com>
Date: Fri, 20 Sep 2024 10:22:28 +0200
Subject: [PATCH 22/49] [DOCS] Adding snippets for remote tensor creation
 (#26632)

Adding code snippets for D3D and VA handles in Remote Tensor API of GPU
Plugin. This PR addresses the following JIRA ticket: 120077

---------

Co-authored-by: Karol Blaszczak <karol.blaszczak@intel.com>
---
 .../remote-tensor-api-gpu-plugin.rst          | 104 +++++++++++++++++-
 1 file changed, 103 insertions(+), 1 deletion(-)

diff --git a/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device/remote-tensor-api-gpu-plugin.rst b/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device/remote-tensor-api-gpu-plugin.rst
index d58a2ea16b0b21..fd303d2754d6c8 100644
--- a/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device/remote-tensor-api-gpu-plugin.rst
+++ b/docs/articles_en/openvino-workflow/running-inference/inference-devices-and-modes/gpu-device/remote-tensor-api-gpu-plugin.rst
@@ -350,7 +350,109 @@ For more details, see the code snippets below:
 
 The ``ov::intel_gpu::ocl::D3DContext`` and ``ov::intel_gpu::ocl::VAContext`` classes are derived from ``ov::intel_gpu::ocl::ClContext``.
 Therefore, they provide the functionality described above and extend it
-to allow creation of ``ov::RemoteTensor`` objects from ``ID3D11Buffer``, ``ID3D11Texture2D`` pointers or the ``VASurfaceID`` handle respectively.
+to enable creation of ``ov::RemoteTensor`` objects from ``ID3D11Buffer``, ``ID3D11Texture2D``
+pointers or the ``VASurfaceID`` handle, as shown in the examples below:
+
+
+.. tab-set::
+
+   .. tab-item:: ID3D11Buffer
+      :sync: id3d11-buffer
+
+      .. code-block:: cpp
+
+         // ...
+
+         // initialize the core and load the network
+         ov::Core core;
+         auto model = core.read_model("model.xml");
+         auto compiled_model = core.compile_model(model, "GPU");
+         auto infer_request = compiled_model.create_infer_request();
+
+
+         // obtain the RemoteContext from the compiled model object and cast it to D3DContext
+         auto gpu_context = compiled_model.get_context().as<ov::intel_gpu::ocl::D3DContext>();
+
+         auto input = model->get_parameters().at(0);
+         ID3D11Buffer* d3d_handle = get_d3d_buffer();
+         auto tensor = gpu_context.create_tensor(input->get_element_type(), input->get_shape(), d3d_handle);
+         infer_request.set_tensor(input, tensor);
+
+   .. tab-item:: ID3D11Texture2D
+      :sync: id3d11-texture
+
+      .. code-block:: cpp
+
+         using namespace ov::preprocess;
+         auto p = PrePostProcessor(model);
+         p.input().tensor().set_element_type(ov::element::u8)
+                           .set_color_format(ov::preprocess::ColorFormat::NV12_TWO_PLANES, {"y", "uv"})
+                           .set_memory_type(ov::intel_gpu::memory_type::surface);
+         p.input().preprocess().convert_color(ov::preprocess::ColorFormat::BGR);
+         p.input().model().set_layout("NCHW");
+         model = p.build();
+
+         CComPtr<ID3D11Device> device_ptr = get_d3d_device_ptr()
+         // create the shared context object
+         auto shared_d3d_context = ov::intel_gpu::ocl::D3DContext(core, device_ptr);
+         // compile model within a shared context
+         auto compiled_model = core.compile_model(model, shared_d3d_context);
+
+         auto param_input_y = model->get_parameters().at(0);
+         auto param_input_uv = model->get_parameters().at(1);
+
+         D3D11_TEXTURE2D_DESC texture_description = get_texture_desc();
+         CComPtr<ID3D11Texture2D> dx11_texture = get_texture();
+         //     ...
+         //wrap decoder output into RemoteBlobs and set it as inference input
+         auto nv12_blob = shared_d3d_context.create_tensor_nv12(texture_description.Heights, texture_description.Width, dx11_texture);
+
+         auto infer_request = compiled_model.create_infer_request();
+         infer_request.set_tensor(param_input_y->get_friendly_name(), nv12_blob.first);
+         infer_request.set_tensor(param_input_uv->get_friendly_name(), nv12_blob.second);
+         infer_request.start_async();
+         infer_request.wait();
+
+   .. tab-item:: VASurfaceID
+      :sync: vasurfaceid
+
+      .. code-block:: cpp
+
+         using namespace ov::preprocess;
+         auto p = PrePostProcessor(model);
+         p.input().tensor().set_element_type(ov::element::u8)
+                           .set_color_format(ov::preprocess::ColorFormat::NV12_TWO_PLANES, {"y", "uv"})
+                           .set_memory_type(ov::intel_gpu::memory_type::surface);
+         p.input().preprocess().convert_color(ov::preprocess::ColorFormat::BGR);
+         p.input().model().set_layout("NCHW");
+         model = p.build();
+
+         CComPtr<ID3D11Device> device_ptr = get_d3d_device_ptr()
+         // create the shared context object
+         auto shared_va_context = ov::intel_gpu::ocl::VAContext(core, device_ptr);
+         // compile model within a shared context
+         auto compiled_model = core.compile_model(model, shared_va_context);
+
+         auto param_input_y = model->get_parameters().at(0);
+         auto param_input_uv = model->get_parameters().at(1);
+
+         auto shape = param_input_y->get_shape();
+         auto width = shape[1];
+         auto height = shape[2];
+
+         VASurfaceID va_surface = decode_va_surface();
+         //     ...
+         //wrap decoder output into RemoteBlobs and set it as inference input
+         auto nv12_blob = shared_va_context.create_tensor_nv12(height, width, va_surface);
+
+         auto infer_request = compiled_model.create_infer_request();
+         infer_request.set_tensor(param_input_y->get_friendly_name(), nv12_blob.first);
+         infer_request.set_tensor(param_input_uv->get_friendly_name(), nv12_blob.second);
+         infer_request.start_async();
+         infer_request.wait();
+
+
+
 
 Direct NV12 Video Surface Input
 ###########################################################

From 22869259a79110ba9611d8ae28127a686470811d Mon Sep 17 00:00:00 2001
From: Andrzej Kopytko <andrzejx.kopytko@intel.com>
Date: Fri, 20 Sep 2024 11:01:10 +0200
Subject: [PATCH 23/49] [DOCS] Fixed problems with console (#26704)

### Details:
 - *item1*
 - *...*

### Tickets:
 - *ticket-id*
---
 docs/sphinx_setup/_static/js/custom.js | 14 ++++++++------
 docs/sphinx_setup/_static/js/graphs.js |  2 +-
 2 files changed, 9 insertions(+), 7 deletions(-)

diff --git a/docs/sphinx_setup/_static/js/custom.js b/docs/sphinx_setup/_static/js/custom.js
index e67a09e84f7549..df8bc9131bf42e 100644
--- a/docs/sphinx_setup/_static/js/custom.js
+++ b/docs/sphinx_setup/_static/js/custom.js
@@ -125,13 +125,15 @@ function handleSidebar() {
 
 function initSidebar() {
     const sidebar = document.querySelector("#bd-sidebar");
-    var size;
-    if(localStorage['resizeSidebarX'] == null){
-        size = "350px";
-    }else{
-        size = localStorage['resizeSidebarX'];
+    if (sidebar) {
+        var size;
+        if (localStorage['resizeSidebarX'] == null) {
+            size = "350px";
+        } else {
+            size = localStorage['resizeSidebarX'];
+        }
+        sidebar.style.flexBasis = size;
     }
-    sidebar.style.flexBasis = size;
 }
 
 // Determine where we'd go if clicking on a version selector option
diff --git a/docs/sphinx_setup/_static/js/graphs.js b/docs/sphinx_setup/_static/js/graphs.js
index 62224cedd3b3fb..f8146c1580c58e 100644
--- a/docs/sphinx_setup/_static/js/graphs.js
+++ b/docs/sphinx_setup/_static/js/graphs.js
@@ -637,7 +637,7 @@ $(document).ready(function () {
                 }
             }
         }
-    } Throughput
+    }
 
     function getChartDataNew(labels, datasets) {
         return {

From cf7c24cf0e3ef2658991af80ace629ef4638b667 Mon Sep 17 00:00:00 2001
From: Tatiana Savina <tatiana.savina@intel.com>
Date: Fri, 20 Sep 2024 11:12:06 +0200
Subject: [PATCH 24/49] [Docs] Port release docs (#26689)

### Details:
 - *item1*
 - *...*

### Tickets:
 - *ticket-id*

---------

Co-authored-by: Karol Blaszczak <karol.blaszczak@intel.com>
Co-authored-by: Alexander Suvorov <alexander.suvorov@intel.com>
---
 .../about-openvino/key-features.rst           | 70 +++++++++----------
 .../about-openvino/release-notes-openvino.rst | 24 ++++++-
 .../get-started/install-openvino.rst          |  2 +-
 ...ector-83076730.js => selector-4f9c3c09.js} | 32 ++++-----
 ...tor-9dbd5b1.html => selector-8d4cf1d.html} |  6 +-
 5 files changed, 76 insertions(+), 58 deletions(-)
 rename docs/sphinx_setup/_static/selector-tool/assets/{selector-83076730.js => selector-4f9c3c09.js} (51%)
 rename docs/sphinx_setup/_static/selector-tool/{selector-9dbd5b1.html => selector-8d4cf1d.html} (77%)

diff --git a/docs/articles_en/about-openvino/key-features.rst b/docs/articles_en/about-openvino/key-features.rst
index 128d3947b3b402..6514bdc67a3302 100644
--- a/docs/articles_en/about-openvino/key-features.rst
+++ b/docs/articles_en/about-openvino/key-features.rst
@@ -5,65 +5,65 @@ Easy Integration
 #########################
 
 | :doc:`Support for multiple frameworks <../openvino-workflow/model-preparation/convert-model-to-ir>`
-| Use deep learning models from PyTorch, TensorFlow, TensorFlow Lite, PaddlePaddle, and ONNX
-  directly or convert them to the optimized OpenVINO IR format for improved performance.
+|     Use deep learning models from PyTorch, TensorFlow, TensorFlow Lite, PaddlePaddle, and ONNX
+      directly or convert them to the optimized OpenVINO IR format for improved performance.
 
 | :doc:`Close integration with PyTorch <../openvino-workflow/torch-compile>`
-| For PyTorch-based applications, specify OpenVINO as a backend using
-  :doc:`torch.compile <../openvino-workflow/torch-compile>` to improve model inference. Apply
-  OpenVINO optimizations to your PyTorch models directly with a single line of code.
+|     For PyTorch-based applications, specify OpenVINO as a backend using
+      :doc:`torch.compile <../openvino-workflow/torch-compile>` to improve model inference. Apply
+      OpenVINO optimizations to your PyTorch models directly with a single line of code.
 
 | :doc:`GenAI Out Of The Box <../learn-openvino/llm_inference_guide/genai-guide>`
-| With the genAI flavor of OpenVINO, you can run generative AI with just a couple lines of code.
-  Check out the GenAI guide for instructions on how to do it.
+|     With the genAI flavor of OpenVINO, you can run generative AI with just a couple lines of code.
+      Check out the GenAI guide for instructions on how to do it.
 
 | `Python / C++ / C / NodeJS APIs <https://docs.openvino.ai/2024/api/api_reference.html>`__
-| OpenVINO offers the C++ API as a complete set of available methods. For less resource-critical
-  solutions, the Python API provides almost full coverage, while C and NodeJS ones are limited
-  to the methods most basic for their typical environments. The NodeJS API, is still in its
-  early and active development.
+|     OpenVINO offers the C++ API as a complete set of available methods. For less resource-critical
+      solutions, the Python API provides almost full coverage, while C and NodeJS ones are limited
+      to the methods most basic for their typical environments. The NodeJS API, is still in its
+      early and active development.
 
 | :doc:`Open source and easy to extend <../about-openvino/contributing>`
-| If you need a particular feature or inference accelerator to be supported, you are free to file
-  a feature request or develop new components specific to your projects yourself. As open source,
-  OpenVINO may be used and modified freely. See the extensibility guide for more information on
-  how to adapt it to your needs.
+|     If you need a particular feature or inference accelerator to be supported, you are free to file
+      a feature request or develop new components specific to your projects yourself. As open source,
+      OpenVINO may be used and modified freely. See the extensibility guide for more information on
+      how to adapt it to your needs.
 
 Deployment
 #########################
 
 | :doc:`Local or remote <../openvino-workflow>`
-| Integrate the OpenVINO runtime directly with your application to run inference locally or use
-  `OpenVINO Model Server <https://github.com/openvinotoolkit/model_server>`__ to shift the inference
-  workload to a remote system, a separate server or a Kubernetes environment. For serving,
-  OpenVINO is also integrated with `vLLM <https://docs.vllm.ai/en/stable/getting_started/openvino-installation.html>`__
-  and `Triton <https://github.com/triton-inference-server/openvino_backend>`__ services.
+|     Integrate the OpenVINO runtime directly with your application to run inference locally or use
+      `OpenVINO Model Server <https://github.com/openvinotoolkit/model_server>`__ to shift the inference
+      workload to a remote system, a separate server or a Kubernetes environment. For serving,
+      OpenVINO is also integrated with `vLLM <https://docs.vllm.ai/en/stable/getting_started/openvino-installation.html>`__
+      and `Triton <https://github.com/triton-inference-server/openvino_backend>`__ services.
 
 | :doc:`Scalable and portable <release-notes-openvino/system-requirements>`
-| Write an application once, deploy it anywhere, always making the most out of your hardware setup.
-  The automatic device selection mode gives you the ultimate deployment flexibility on all major
-  operating systems. Check out system requirements.
+|     Write an application once, deploy it anywhere, always making the most out of your hardware setup.
+      The automatic device selection mode gives you the ultimate deployment flexibility on all major
+      operating systems. Check out system requirements.
 
 | **Light-weight**
-| Designed with minimal external dependencies, OpenVINO does not bloat your application
-  and simplifies installation and dependency management. The custom compilation for your specific
-  model(s) may further reduce the final binary size.
+|     Designed with minimal external dependencies, OpenVINO does not bloat your application
+      and simplifies installation and dependency management. The custom compilation for your specific
+      model(s) may further reduce the final binary size.
 
 Performance
 #########################
 
 | :doc:`Model Optimization <../openvino-workflow/model-optimization>`
-| Optimize your deep learning models with NNCF, using various training-time and post-training
-  compression methods, such as pruning, sparsity, quantization, and weight compression. Make
-  your models take less space, run faster, and use less resources.
+|     Optimize your deep learning models with NNCF, using various training-time and post-training
+      compression methods, such as pruning, sparsity, quantization, and weight compression. Make
+      your models take less space, run faster, and use less resources.
 
 | :doc:`Top performance <../about-openvino/performance-benchmarks>`
-| OpenVINO is optimized to work with Intel hardware, delivering confirmed high performance for
-  hundreds of models. Explore OpenVINO Performance Benchmarks to discover the optimal hardware
-  configurations and plan your AI deployment based on verified data.
+|     OpenVINO is optimized to work with Intel hardware, delivering confirmed high performance for
+      hundreds of models. Explore OpenVINO Performance Benchmarks to discover the optimal hardware
+      configurations and plan your AI deployment based on verified data.
 
 | :doc:`Enhanced App Start-Up Time <../openvino-workflow/running-inference/optimize-inference>`
-| If you need your application to launch immediately, OpenVINO will reduce first-inference latency,
-  running inference on CPU until a more suited device is ready to take over. Once a model
-  is compiled for inference, it is also cached, improving the start-up time even more.
+|     If you need your application to launch immediately, OpenVINO will reduce first-inference latency,
+      running inference on CPU until a more suited device is ready to take over. Once a model
+      is compiled for inference, it is also cached, improving the start-up time even more.
 
diff --git a/docs/articles_en/about-openvino/release-notes-openvino.rst b/docs/articles_en/about-openvino/release-notes-openvino.rst
index c60371119de69b..0e3e84cfdc2aad 100644
--- a/docs/articles_en/about-openvino/release-notes-openvino.rst
+++ b/docs/articles_en/about-openvino/release-notes-openvino.rst
@@ -15,7 +15,7 @@ OpenVINO Release Notes
 
 
 
-2024.4 - 18 September 2024
+2024.4 - 19 September 2024
 #############################
 
 :doc:`System Requirements <./release-notes-openvino/system-requirements>` | :doc:`Release policy <./release-notes-openvino/release-policy>` | :doc:`Installation Guides <./../get-started/install-openvino>`
@@ -203,8 +203,7 @@ OpenVINO Model Server
   * Ability to compress the KV Cache to a lower precision, reducing memory consumption without
     a significant loss of accuracy.
   * ``stop`` sampling parameters, to define a sequence that stops text generation.
-  * ``logprobs`` sampling parameter, returning the probabilities to returned tokens, which can
-    be used to calculate the model perplexity metric, among other things.
+  * ``logprobs`` sampling parameter, returning the probabilities to returned tokens.
   * Generic metrics related to execution of the MediaPipe graph that can be used for autoscaling
     based on the current load and the level of concurrency.
   * `Demo of text generation horizontal scalability <https://github.com/openvinotoolkit/model_server/tree/main/demos/continuous_batching/scaling>`__
@@ -212,6 +211,7 @@ OpenVINO Model Server
   * Automatic cancelling of text generation for disconnected clients.
   * Non-UTF-8 responses from the model can be now automatically changed to Unicode replacement
     characters, due to their configurable handling.
+  * Intel GPU with paged attention is now supported.
   * Support for Llama3.1 models.
 
 * The following has been improved:
@@ -301,6 +301,24 @@ Known Issues
 |   OpenVINO.GenAI archive doesn't have debug libraries for OpenVINO Tokenizers and
     OpenVINO.GenAI.
 
+| **Component: ONNX for ARM**
+| ID: n/a
+| Description:
+|   For ARM binaries, the `1.16 ONNX library <https://vcpkg.link/ports/onnx/versions>`__
+    is not yet available, while the current latest has shown two significant vulnerabilities:
+    `CVE-2024-27318 <https://nvd.nist.gov/vuln/detail/CVE-2024-27318>`__ and
+    `CVE-2024-27319 <https://nvd.nist.gov/vuln/detail/CVE-2024-27319>`__.
+    The vulnerabilities are less severe in the context of OpenVINO and will be fixed as soon as
+    the most recent version of the library is available for ARM, expected at the 2024.5 release.
+
+
+
+
+
+
+
+
+
 
 
 
diff --git a/docs/articles_en/get-started/install-openvino.rst b/docs/articles_en/get-started/install-openvino.rst
index b843bc3682f0a9..6dda915af93b69 100644
--- a/docs/articles_en/get-started/install-openvino.rst
+++ b/docs/articles_en/get-started/install-openvino.rst
@@ -21,7 +21,7 @@ Install OpenVINO™ 2024.4
 
    <script type="module" crossorigin src="../_static/selector-tool/assets/index-f34d1fad.js"></script>
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-   <iframe id="selector" src="../_static/selector-tool/selector-9dbd5b1.html" style="width: 100%; border: none" title="Download Intel® Distribution of OpenVINO™ Toolkit"></iframe>
+   <iframe id="selector" src="../_static/selector-tool/selector-8d4cf1d.html" style="width: 100%; border: none" title="Download Intel® Distribution of OpenVINO™ Toolkit"></iframe>
 
 OpenVINO 2024.4, described here, is not a Long-Term-Support version!
 All currently supported versions are:
diff --git a/docs/sphinx_setup/_static/selector-tool/assets/selector-83076730.js b/docs/sphinx_setup/_static/selector-tool/assets/selector-4f9c3c09.js
similarity index 51%
rename from docs/sphinx_setup/_static/selector-tool/assets/selector-83076730.js
rename to docs/sphinx_setup/_static/selector-tool/assets/selector-4f9c3c09.js
index c6a30484013d7a..0836ad1aed41f3 100644
--- a/docs/sphinx_setup/_static/selector-tool/assets/selector-83076730.js
+++ b/docs/sphinx_setup/_static/selector-tool/assets/selector-4f9c3c09.js
@@ -1,4 +1,4 @@
-var Xf=Object.defineProperty;var Jf=(e,t,n)=>t in e?Xf(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var ze=(e,t,n)=>(Jf(e,typeof t!="symbol"?t+"":t,n),n);function uc(e){return e&&e.__esModule&&Object.prototype.hasOwnProperty.call(e,"default")?e.default:e}var cc={exports:{}},po={},dc={exports:{}},D={};/**
+var Xf=Object.defineProperty;var Jf=(e,t,n)=>t in e?Xf(e,t,{enumerable:!0,configurable:!0,writable:!0,value:n}):e[t]=n;var Ve=(e,t,n)=>(Jf(e,typeof t!="symbol"?t+"":t,n),n);function uc(e){return e&&e.__esModule&&Object.prototype.hasOwnProperty.call(e,"default")?e.default:e}var cc={exports:{}},po={},dc={exports:{}},D={};/**
  * @license React
  * react.production.min.js
  *
@@ -6,7 +6,7 @@ var Xf=Object.defineProperty;var Jf=(e,t,n)=>t in e?Xf(e,t,{enumerable:!0,config
  *
  * This source code is licensed under the MIT license found in the
  * LICENSE file in the root directory of this source tree.
- */var qr=Symbol.for("react.element"),Zf=Symbol.for("react.portal"),qf=Symbol.for("react.fragment"),ep=Symbol.for("react.strict_mode"),tp=Symbol.for("react.profiler"),np=Symbol.for("react.provider"),rp=Symbol.for("react.context"),ip=Symbol.for("react.forward_ref"),op=Symbol.for("react.suspense"),lp=Symbol.for("react.memo"),sp=Symbol.for("react.lazy"),Oa=Symbol.iterator;function ap(e){return e===null||typeof e!="object"?null:(e=Oa&&e[Oa]||e["@@iterator"],typeof e=="function"?e:null)}var fc={isMounted:function(){return!1},enqueueForceUpdate:function(){},enqueueReplaceState:function(){},enqueueSetState:function(){}},pc=Object.assign,hc={};function sr(e,t,n){this.props=e,this.context=t,this.refs=hc,this.updater=n||fc}sr.prototype.isReactComponent={};sr.prototype.setState=function(e,t){if(typeof e!="object"&&typeof e!="function"&&e!=null)throw Error("setState(...): takes an object of state variables to update or a function which returns an object of state variables.");this.updater.enqueueSetState(this,e,t,"setState")};sr.prototype.forceUpdate=function(e){this.updater.enqueueForceUpdate(this,e,"forceUpdate")};function mc(){}mc.prototype=sr.prototype;function hs(e,t,n){this.props=e,this.context=t,this.refs=hc,this.updater=n||fc}var ms=hs.prototype=new mc;ms.constructor=hs;pc(ms,sr.prototype);ms.isPureReactComponent=!0;var _a=Array.isArray,gc=Object.prototype.hasOwnProperty,gs={current:null},vc={key:!0,ref:!0,__self:!0,__source:!0};function yc(e,t,n){var r,i={},o=null,l=null;if(t!=null)for(r in t.ref!==void 0&&(l=t.ref),t.key!==void 0&&(o=""+t.key),t)gc.call(t,r)&&!vc.hasOwnProperty(r)&&(i[r]=t[r]);var s=arguments.length-2;if(s===1)i.children=n;else if(1<s){for(var a=Array(s),u=0;u<s;u++)a[u]=arguments[u+2];i.children=a}if(e&&e.defaultProps)for(r in s=e.defaultProps,s)i[r]===void 0&&(i[r]=s[r]);return{$$typeof:qr,type:e,key:o,ref:l,props:i,_owner:gs.current}}function up(e,t){return{$$typeof:qr,type:e.type,key:t,ref:e.ref,props:e.props,_owner:e._owner}}function vs(e){return typeof e=="object"&&e!==null&&e.$$typeof===qr}function cp(e){var t={"=":"=0",":":"=2"};return"$"+e.replace(/[=:]/g,function(n){return t[n]})}var Na=/\/+/g;function Ao(e,t){return typeof e=="object"&&e!==null&&e.key!=null?cp(""+e.key):t.toString(36)}function xi(e,t,n,r,i){var o=typeof e;(o==="undefined"||o==="boolean")&&(e=null);var l=!1;if(e===null)l=!0;else switch(o){case"string":case"number":l=!0;break;case"object":switch(e.$$typeof){case qr:case Zf:l=!0}}if(l)return l=e,i=i(l),e=r===""?"."+Ao(l,0):r,_a(i)?(n="",e!=null&&(n=e.replace(Na,"$&/")+"/"),xi(i,t,n,"",function(u){return u})):i!=null&&(vs(i)&&(i=up(i,n+(!i.key||l&&l.key===i.key?"":(""+i.key).replace(Na,"$&/")+"/")+e)),t.push(i)),1;if(l=0,r=r===""?".":r+":",_a(e))for(var s=0;s<e.length;s++){o=e[s];var a=r+Ao(o,s);l+=xi(o,t,n,a,i)}else if(a=ap(e),typeof a=="function")for(e=a.call(e),s=0;!(o=e.next()).done;)o=o.value,a=r+Ao(o,s++),l+=xi(o,t,n,a,i);else if(o==="object")throw t=String(e),Error("Objects are not valid as a React child (found: "+(t==="[object Object]"?"object with keys {"+Object.keys(e).join(", ")+"}":t)+"). 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d=i.match(this.interpolator.nestingRegexp);if(d&&d.length>0)return{key:i,namespaces:a};var h=i.split(l);(l!==s||l===s&&this.options.ns.indexOf(h[0])>-1)&&(a=h.shift()),i=h.join(s)}return typeof a=="string"&&(a=[a]),{key:i,namespaces:a}}},{key:"translate",value:function(i,o,l){var s=this;if(pe(o)!=="object"&&this.options.overloadTranslationOptionHandler&&(o=this.options.overloadTranslationOptionHandler(arguments)),o||(o={}),i==null)return"";Array.isArray(i)||(i=[String(i)]);var a=o.returnDetails!==void 0?o.returnDetails:this.options.returnDetails,u=o.keySeparator!==void 0?o.keySeparator:this.options.keySeparator,p=this.extractFromKey(i[i.length-1],o),d=p.key,h=p.namespaces,g=h[h.length-1],y=o.lng||this.language,k=o.appendNamespaceToCIMode||this.options.appendNamespaceToCIMode;if(y&&y.toLowerCase()==="cimode"){if(k){var _=o.nsSeparator||this.options.nsSeparator;return a?{res:"".concat(g).concat(_).concat(d),usedKey:d,exactUsedKey:d,usedLng:y,usedNS:g}:"".concat(g).concat(_).concat(d)}return a?{res:d,usedKey:d,exactUsedKey:d,usedLng:y,usedNS:g}:d}var f=this.resolve(i,o),c=f&&f.res,m=f&&f.usedKey||d,w=f&&f.exactUsedKey||d,O=Object.prototype.toString.apply(c),S=["[object Number]","[object Function]","[object RegExp]"],P=o.joinArrays!==void 0?o.joinArrays:this.options.joinArrays,x=!this.i18nFormat||this.i18nFormat.handleAsObject,j=typeof c!="string"&&typeof c!="boolean"&&typeof c!="number";if(x&&c&&j&&S.indexOf(O)<0&&!(typeof P=="string"&&O==="[object Array]")){if(!o.returnObjects&&!this.options.returnObjects){this.options.returnedObjectHandler||this.logger.warn("accessing an object - but returnObjects options is not enabled!");var E=this.options.returnedObjectHandler?this.options.returnedObjectHandler(m,c,ke(ke({},o),{},{ns:h})):"key '".concat(d," (").concat(this.language,")' returned an object instead of string.");return a?(f.res=E,f):E}if(u){var 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Either set keySeparator: false on init or make sure your translations are published in nested format.")}var be=[],Pt=this.languageUtils.getFallbackCodes(this.options.fallbackLng,o.lng||this.language);if(this.options.saveMissingTo==="fallback"&&Pt&&Pt[0])for(var Lo=0;Lo<Pt.length;Lo++)be.push(Pt[Lo]);else this.options.saveMissingTo==="all"?be=this.languageUtils.toResolveHierarchy(o.lng||this.language):be.push(o.lng||this.language);var wa=function(In,Fo,ka){var Sa=L&&ka!==c?ka:Nt;s.options.missingKeyHandler?s.options.missingKeyHandler(In,g,Fo,Sa,Ue,o):s.backendConnector&&s.backendConnector.saveMissing&&s.backendConnector.saveMissing(In,g,Fo,Sa,Ue,o),s.emit("missingKey",In,g,Fo,c)};this.options.saveMissing&&(this.options.saveMissingPlurals&&C?be.forEach(function(jo){s.pluralResolver.getSuffixes(jo,o).forEach(function(In){wa([jo],d+In,o["defaultValue".concat(In)]||V)})}):wa(be,d,V))}c=this.extendTranslation(c,i,o,f,l),rt&&c===d&&this.options.appendNamespaceToMissingKey&&(c="".concat(g,":").concat(d)),(rt||ht)&&this.options.parseMissingKeyHandler&&(this.options.compatibilityAPI!=="v1"?c=this.options.parseMissingKeyHandler(this.options.appendNamespaceToMissingKey?"".concat(g,":").concat(d):d,ht?c:void 0):c=this.options.parseMissingKeyHandler(c))}return a?(f.res=c,f):c}},{key:"extendTranslation",value:function(i,o,l,s,a){var u=this;if(this.i18nFormat&&this.i18nFormat.parse)i=this.i18nFormat.parse(i,ke(ke({},this.options.interpolation.defaultVariables),l),s.usedLng,s.usedNS,s.usedKey,{resolved:s});else if(!l.skipInterpolation){l.interpolation&&this.interpolator.init(ke(ke({},l),{interpolation:ke(ke({},this.options.interpolation),l.interpolation)}));var p=typeof i=="string"&&(l&&l.interpolation&&l.interpolation.skipOnVariables!==void 0?l.interpolation.skipOnVariables:this.options.interpolation.skipOnVariables),d;if(p){var h=i.match(this.interpolator.nestingRegexp);d=h&&h.length}var g=l.replace&&typeof l.replace!="string"?l.replace:l;if(this.options.interpolation.defaultVariables&&(g=ke(ke({},this.options.interpolation.defaultVariables),g)),i=this.interpolator.interpolate(i,g,l.lng||this.language,l),p){var y=i.match(this.interpolator.nestingRegexp),k=y&&y.length;d<k&&(l.nest=!1)}l.nest!==!1&&(i=this.interpolator.nest(i,function(){for(var c=arguments.length,m=new Array(c),w=0;w<c;w++)m[w]=arguments[w];return a&&a[0]===m[0]&&!l.context?(u.logger.warn("It seems you are nesting recursively key: ".concat(m[0]," in key: ").concat(o[0])),null):u.translate.apply(u,m.concat([o]))},l)),l.interpolation&&this.interpolator.reset()}var _=l.postProcess||this.options.postProcess,f=typeof _=="string"?[_]:_;return i!=null&&f&&f.length&&l.applyPostProcessor!==!1&&(i=xc.handle(f,i,o,this.options&&this.options.postProcessPassResolved?ke({i18nResolved:s},l):l,this)),i}},{key:"resolve",value:function(i){var o=this,l=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{},s,a,u,p,d;return typeof i=="string"&&(i=[i]),i.forEach(function(h){if(!o.isValidLookup(s)){var g=o.extractFromKey(h,l),y=g.key;a=y;var k=g.namespaces;o.options.fallbackNS&&(k=k.concat(o.options.fallbackNS));var _=l.count!==void 0&&typeof l.count!="string",f=_&&!l.ordinal&&l.count===0&&o.pluralResolver.shouldUseIntlApi(),c=l.context!==void 0&&(typeof l.context=="string"||typeof l.context=="number")&&l.context!=="",m=l.lngs?l.lngs:o.languageUtils.toResolveHierarchy(l.lng||o.language,l.fallbackLng);k.forEach(function(w){o.isValidLookup(s)||(d=w,!Fa["".concat(m[0],"-").concat(w)]&&o.utils&&o.utils.hasLoadedNamespace&&!o.utils.hasLoadedNamespace(d)&&(Fa["".concat(m[0],"-").concat(w)]=!0,o.logger.warn('key "'.concat(a,'" for languages "').concat(m.join(", "),`" won't get resolved as namespace "`).concat(d,'" was not yet loaded'),"This means something IS WRONG in your setup. You access the t function before i18next.init / i18next.loadNamespace / i18next.changeLanguage was done. Wait for the callback or Promise to resolve before accessing it!!!")),m.forEach(function(O){if(!o.isValidLookup(s)){p=O;var S=[y];if(o.i18nFormat&&o.i18nFormat.addLookupKeys)o.i18nFormat.addLookupKeys(S,y,O,w,l);else{var P;_&&(P=o.pluralResolver.getSuffix(O,l.count,l));var x="".concat(o.options.pluralSeparator,"zero");if(_&&(S.push(y+P),f&&S.push(y+x)),c){var j="".concat(y).concat(o.options.contextSeparator).concat(l.context);S.push(j),_&&(S.push(j+P),f&&S.push(j+x))}}for(var E;E=S.pop();)o.isValidLookup(s)||(u=E,s=o.getResource(O,w,E,l))}}))})}}),{res:s,usedKey:a,exactUsedKey:u,usedLng:p,usedNS:d}}},{key:"isValidLookup",value:function(i){return i!==void 0&&!(!this.options.returnNull&&i===null)&&!(!this.options.returnEmptyString&&i==="")}},{key:"getResource",value:function(i,o,l){var s=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{};return this.i18nFormat&&this.i18nFormat.getResource?this.i18nFormat.getResource(i,o,l,s):this.resourceStore.getResource(i,o,l,s)}}],[{key:"hasDefaultValue",value:function(i){var o="defaultValue";for(var l in i)if(Object.prototype.hasOwnProperty.call(i,l)&&o===l.substring(0,o.length)&&i[l]!==void 0)return!0;return!1}}]),n}(en);function Do(e){return e.charAt(0).toUpperCase()+e.slice(1)}var Da=function(){function e(t){ft(this,e),this.options=t,this.supportedLngs=this.options.supportedLngs||!1,this.logger=wt.create("languageUtils")}return pt(e,[{key:"getScriptPartFromCode",value:function(n){if(!n||n.indexOf("-")<0)return null;var r=n.split("-");return r.length===2||(r.pop(),r[r.length-1].toLowerCase()==="x")?null:this.formatLanguageCode(r.join("-"))}},{key:"getLanguagePartFromCode",value:function(n){if(!n||n.indexOf("-")<0)return n;var r=n.split("-");return this.formatLanguageCode(r[0])}},{key:"formatLanguageCode",value:function(n){if(typeof n=="string"&&n.indexOf("-")>-1){var r=["hans","hant","latn","cyrl","cans","mong","arab"],i=n.split("-");return this.options.lowerCaseLng?i=i.map(function(o){return o.toLowerCase()}):i.length===2?(i[0]=i[0].toLowerCase(),i[1]=i[1].toUpperCase(),r.indexOf(i[1].toLowerCase())>-1&&(i[1]=Do(i[1].toLowerCase()))):i.length===3&&(i[0]=i[0].toLowerCase(),i[1].length===2&&(i[1]=i[1].toUpperCase()),i[0]!=="sgn"&&i[2].length===2&&(i[2]=i[2].toUpperCase()),r.indexOf(i[1].toLowerCase())>-1&&(i[1]=Do(i[1].toLowerCase())),r.indexOf(i[2].toLowerCase())>-1&&(i[2]=Do(i[2].toLowerCase()))),i.join("-")}return this.options.cleanCode||this.options.lowerCaseLng?n.toLowerCase():n}},{key:"isSupportedCode",value:function(n){return(this.options.load==="languageOnly"||this.options.nonExplicitSupportedLngs)&&(n=this.getLanguagePartFromCode(n)),!this.supportedLngs||!this.supportedLngs.length||this.supportedLngs.indexOf(n)>-1}},{key:"getBestMatchFromCodes",value:function(n){var r=this;if(!n)return null;var i;return n.forEach(function(o){if(!i){var l=r.formatLanguageCode(o);(!r.options.supportedLngs||r.isSupportedCode(l))&&(i=l)}}),!i&&this.options.supportedLngs&&n.forEach(function(o){if(!i){var l=r.getLanguagePartFromCode(o);if(r.isSupportedCode(l))return i=l;i=r.options.supportedLngs.find(function(s){if(s.indexOf(l)===0)return s})}}),i||(i=this.getFallbackCodes(this.options.fallbackLng)[0]),i}},{key:"getFallbackCodes",value:function(n,r){if(!n)return[];if(typeof n=="function"&&(n=n(r)),typeof n=="string"&&(n=[n]),Object.prototype.toString.apply(n)==="[object Array]")return n;if(!r)return n.default||[];var i=n[r];return i||(i=n[this.getScriptPartFromCode(r)]),i||(i=n[this.formatLanguageCode(r)]),i||(i=n[this.getLanguagePartFromCode(r)]),i||(i=n.default),i||[]}},{key:"toResolveHierarchy",value:function(n,r){var i=this,o=this.getFallbackCodes(r||this.options.fallbackLng||[],n),l=[],s=function(u){u&&(i.isSupportedCode(u)?l.push(u):i.logger.warn("rejecting language code not found in supportedLngs: ".concat(u)))};return typeof n=="string"&&n.indexOf("-")>-1?(this.options.load!=="languageOnly"&&s(this.formatLanguageCode(n)),this.options.load!=="languageOnly"&&this.options.load!=="currentOnly"&&s(this.getScriptPartFromCode(n)),this.options.load!=="currentOnly"&&s(this.getLanguagePartFromCode(n))):typeof n=="string"&&s(this.formatLanguageCode(n)),o.forEach(function(a){l.indexOf(a)<0&&s(i.formatLanguageCode(a))}),l}}]),e}(),Up=[{lngs:["ach","ak","am","arn","br","fil","gun","ln","mfe","mg","mi","oc","pt","pt-BR","tg","tl","ti","tr","uz","wa"],nr:[1,2],fc:1},{lngs:["af","an","ast","az","bg","bn","ca","da","de","dev","el","en","eo","es","et","eu","fi","fo","fur","fy","gl","gu","ha","hi","hu","hy","ia","it","kk","kn","ku","lb","mai","ml","mn","mr","nah","nap","nb","ne","nl","nn","no","nso","pa","pap","pms","ps","pt-PT","rm","sco","se","si","so","son","sq","sv","sw","ta","te","tk","ur","yo"],nr:[1,2],fc:2},{lngs:["ay","bo","cgg","fa","ht","id","ja","jbo","ka","km","ko","ky","lo","ms","sah","su","th","tt","ug","vi","wo","zh"],nr:[1],fc:3},{lngs:["be","bs","cnr","dz","hr","ru","sr","uk"],nr:[1,2,5],fc:4},{lngs:["ar"],nr:[0,1,2,3,11,100],fc:5},{lngs:["cs","sk"],nr:[1,2,5],fc:6},{lngs:["csb","pl"],nr:[1,2,5],fc:7},{lngs:["cy"],nr:[1,2,3,8],fc:8},{lngs:["fr"],nr:[1,2],fc:9},{lngs:["ga"],nr:[1,2,3,7,11],fc:10},{lngs:["gd"],nr:[1,2,3,20],fc:11},{lngs:["is"],nr:[1,2],fc:12},{lngs:["jv"],nr:[0,1],fc:13},{lngs:["kw"],nr:[1,2,3,4],fc:14},{lngs:["lt"],nr:[1,2,10],fc:15},{lngs:["lv"],nr:[1,2,0],fc:16},{lngs:["mk"],nr:[1,2],fc:17},{lngs:["mnk"],nr:[0,1,2],fc:18},{lngs:["mt"],nr:[1,2,11,20],fc:19},{lngs:["or"],nr:[2,1],fc:2},{lngs:["ro"],nr:[1,2,20],fc:20},{lngs:["sl"],nr:[5,1,2,3],fc:21},{lngs:["he","iw"],nr:[1,2,20,21],fc:22}],bp={1:function(t){return+(t>1)},2:function(t){return+(t!=1)},3:function(t){return 0},4:function(t){return t%10==1&&t%100!=11?0:t%10>=2&&t%10<=4&&(t%100<10||t%100>=20)?1:2},5:function(t){return t==0?0:t==1?1:t==2?2:t%100>=3&&t%100<=10?3:t%100>=11?4:5},6:function(t){return t==1?0:t>=2&&t<=4?1:2},7:function(t){return t==1?0:t%10>=2&&t%10<=4&&(t%100<10||t%100>=20)?1:2},8:function(t){return t==1?0:t==2?1:t!=8&&t!=11?2:3},9:function(t){return+(t>=2)},10:function(t){return t==1?0:t==2?1:t<7?2:t<11?3:4},11:function(t){return t==1||t==11?0:t==2||t==12?1:t>2&&t<20?2:3},12:function(t){return+(t%10!=1||t%100==11)},13:function(t){return+(t!==0)},14:function(t){return t==1?0:t==2?1:t==3?2:3},15:function(t){return t%10==1&&t%100!=11?0:t%10>=2&&(t%100<10||t%100>=20)?1:2},16:function(t){return t%10==1&&t%100!=11?0:t!==0?1:2},17:function(t){return t==1||t%10==1&&t%100!=11?0:1},18:function(t){return t==0?0:t==1?1:2},19:function(t){return t==1?0:t==0||t%100>1&&t%100<11?1:t%100>10&&t%100<20?2:3},20:function(t){return t==1?0:t==0||t%100>0&&t%100<20?1:2},21:function(t){return 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o?this.shouldUseIntlApi()?o.resolvedOptions().pluralCategories.sort(function(l,s){return Ua[l]-Ua[s]}).map(function(l){return"".concat(r.options.prepend).concat(l)}):o.numbers.map(function(l){return r.getSuffix(n,l,i)}):[]}},{key:"getSuffix",value:function(n,r){var i=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{},o=this.getRule(n,i);return o?this.shouldUseIntlApi()?"".concat(this.options.prepend).concat(o.select(r)):this.getSuffixRetroCompatible(o,r):(this.logger.warn("no plural rule found for: ".concat(n)),"")}},{key:"getSuffixRetroCompatible",value:function(n,r){var i=this,o=n.noAbs?n.plurals(r):n.plurals(Math.abs(r)),l=n.numbers[o];this.options.simplifyPluralSuffix&&n.numbers.length===2&&n.numbers[0]===1&&(l===2?l="plural":l===1&&(l=""));var s=function(){return i.options.prepend&&l.toString()?i.options.prepend+l.toString():l.toString()};return this.options.compatibilityJSON==="v1"?l===1?"":typeof l=="number"?"_plural_".concat(l.toString()):s():this.options.compatibilityJSON==="v2"||this.options.simplifyPluralSuffix&&n.numbers.length===2&&n.numbers[0]===1?s():this.options.prepend&&o.toString()?this.options.prepend+o.toString():o.toString()}},{key:"shouldUseIntlApi",value:function(){return!zp.includes(this.options.compatibilityJSON)}}]),e}();function ba(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(i){return Object.getOwnPropertyDescriptor(e,i).enumerable})),n.push.apply(n,r)}return n}function it(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?ba(Object(n),!0).forEach(function(r){tt(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):ba(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}var $p=function(){function e(){var t=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};ft(this,e),this.logger=wt.create("interpolator"),this.options=t,this.format=t.interpolation&&t.interpolation.format||function(n){return n},this.init(t)}return pt(e,[{key:"init",value:function(){var n=arguments.length>0&&arguments[0]!==void 0?arguments[0]:{};n.interpolation||(n.interpolation={escapeValue:!0});var r=n.interpolation;this.escape=r.escape!==void 0?r.escape:Ip,this.escapeValue=r.escapeValue!==void 0?r.escapeValue:!0,this.useRawValueToEscape=r.useRawValueToEscape!==void 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r="".concat(this.prefix).concat(this.unescapePrefix,"(.+?)").concat(this.unescapeSuffix).concat(this.suffix);this.regexpUnescape=new RegExp(r,"g");var i="".concat(this.nestingPrefix,"(.+?)").concat(this.nestingSuffix);this.nestingRegexp=new RegExp(i,"g")}},{key:"interpolate",value:function(n,r,i,o){var l=this,s,a,u,p=this.options&&this.options.interpolation&&this.options.interpolation.defaultVariables||{};function d(_){return _.replace(/\$/g,"$$$$")}var h=function(f){if(f.indexOf(l.formatSeparator)<0){var c=Ra(r,p,f);return l.alwaysFormat?l.format(c,void 0,i,it(it(it({},o),r),{},{interpolationkey:f})):c}var m=f.split(l.formatSeparator),w=m.shift().trim(),O=m.join(l.formatSeparator).trim();return l.format(Ra(r,p,w),O,i,it(it(it({},o),r),{},{interpolationkey:w}))};this.resetRegExp();var g=o&&o.missingInterpolationHandler||this.options.missingInterpolationHandler,y=o&&o.interpolation&&o.interpolation.skipOnVariables!==void 0?o.interpolation.skipOnVariables:this.options.interpolation.skipOnVariables,k=[{regex:this.regexpUnescape,safeValue:function(f){return d(f)}},{regex:this.regexp,safeValue:function(f){return l.escapeValue?d(l.escape(f)):d(f)}}];return k.forEach(function(_){for(u=0;s=_.regex.exec(n);){var f=s[1].trim();if(a=h(f),a===void 0)if(typeof g=="function"){var c=g(n,s,o);a=typeof c=="string"?c:""}else if(o&&Object.prototype.hasOwnProperty.call(o,f))a="";else if(y){a=s[0];continue}else l.logger.warn("missed to pass in variable ".concat(f," for interpolating ").concat(n)),a="";else typeof a!="string"&&!l.useRawValueToEscape&&(a=Ia(a));var m=_.safeValue(a);if(n=n.replace(s[0],m),y?(_.regex.lastIndex+=a.length,_.regex.lastIndex-=s[0].length):_.regex.lastIndex=0,u++,u>=l.maxReplaces)break}}),n}},{key:"nest",value:function(n,r){var i=this,o=arguments.length>2&&arguments[2]!==void 0?arguments[2]:{},l,s,a;function u(g,y){var k=this.nestingOptionsSeparator;if(g.indexOf(k)<0)return g;var _=g.split(new RegExp("".concat(k,"[ ]*{"))),f="{".concat(_[1]);g=_[0],f=this.interpolate(f,a);var c=f.match(/'/g),m=f.match(/"/g);(c&&c.length%2===0&&!m||m.length%2!==0)&&(f=f.replace(/'/g,'"'));try{a=JSON.parse(f),y&&(a=it(it({},y),a))}catch(w){return this.logger.warn("failed parsing options string in nesting for key ".concat(g),w),"".concat(g).concat(k).concat(f)}return delete a.defaultValue,g}for(;l=this.nestingRegexp.exec(n);){var p=[];a=it({},o),a=a.replace&&typeof a.replace!="string"?a.replace:a,a.applyPostProcessor=!1,delete a.defaultValue;var d=!1;if(l[0].indexOf(this.formatSeparator)!==-1&&!/{.*}/.test(l[1])){var h=l[1].split(this.formatSeparator).map(function(g){return g.trim()});l[1]=h.shift(),p=h,d=!0}if(s=r(u.call(this,l[1].trim(),a),a),s&&l[0]===n&&typeof s!="string")return s;typeof s!="string"&&(s=Ia(s)),s||(this.logger.warn("missed to resolve ".concat(l[1]," for nesting ").concat(n)),s=""),d&&(s=p.reduce(function(g,y){return i.format(g,y,o.lng,it(it({},o),{},{interpolationkey:l[1].trim()}))},s.trim())),n=n.replace(l[0],s),this.regexp.lastIndex=0}return n}}]),e}();function za(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(i){return Object.getOwnPropertyDescriptor(e,i).enumerable})),n.push.apply(n,r)}return n}function xt(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?za(Object(n),!0).forEach(function(r){tt(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):za(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}function Bp(e){var t=e.toLowerCase().trim(),n={};if(e.indexOf("(")>-1){var r=e.split("(");t=r[0].toLowerCase().trim();var i=r[1].substring(0,r[1].length-1);if(t==="currency"&&i.indexOf(":")<0)n.currency||(n.currency=i.trim());else 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$a(){return{debug:!1,initImmediate:!0,ns:["translation"],defaultNS:["translation"],fallbackLng:["dev"],fallbackNS:!1,supportedLngs:!1,nonExplicitSupportedLngs:!1,load:"all",preload:!1,simplifyPluralSuffix:!0,keySeparator:".",nsSeparator:":",pluralSeparator:"_",contextSeparator:"_",partialBundledLanguages:!1,saveMissing:!1,updateMissing:!1,saveMissingTo:"fallback",saveMissingPlurals:!0,missingKeyHandler:!1,missingInterpolationHandler:!1,postProcess:!1,postProcessPassResolved:!1,returnNull:!0,returnEmptyString:!0,returnObjects:!1,joinArrays:!1,returnedObjectHandler:!1,parseMissingKeyHandler:!1,appendNamespaceToMissingKey:!1,appendNamespaceToCIMode:!1,overloadTranslationOptionHandler:function(t){var n={};if(pe(t[1])==="object"&&(n=t[1]),typeof t[1]=="string"&&(n.defaultValue=t[1]),typeof t[2]=="string"&&(n.tDescription=t[2]),pe(t[2])==="object"||pe(t[3])==="object"){var r=t[3]||t[2];Object.keys(r).forEach(function(i){n[i]=r[i]})}return 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Yp(a(this.modules.backend),d.resourceStore,d,this.options),d.backendConnector.on("*",function(f){for(var c=arguments.length,m=new Array(c>1?c-1:0),w=1;w<c;w++)m[w-1]=arguments[w];i.emit.apply(i,[f].concat(m))}),this.modules.languageDetector&&(d.languageDetector=a(this.modules.languageDetector),d.languageDetector.init&&d.languageDetector.init(d,this.options.detection,this.options)),this.modules.i18nFormat&&(d.i18nFormat=a(this.modules.i18nFormat),d.i18nFormat.init&&d.i18nFormat.init(this)),this.translator=new Aa(this.services,this.options),this.translator.on("*",function(f){for(var c=arguments.length,m=new Array(c>1?c-1:0),w=1;w<c;w++)m[w-1]=arguments[w];i.emit.apply(i,[f].concat(m))}),this.modules.external.forEach(function(f){f.init&&f.init(i)})}if(this.format=this.options.interpolation.format,l||(l=ai),this.options.fallbackLng&&!this.services.languageDetector&&!this.options.lng){var 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0?arguments[2]:".";if(e){if(e[t])return e[t];for(var r=t.split(n),i=e,o=0;o<r.length;++o){if(!i||typeof i[r[o]]=="string"&&o+1<r.length)return;if(i[r[o]]===void 0){for(var l=2,s=r.slice(o,o+l).join(n),a=i[s];a===void 0&&r.length>o+l;)l++,s=r.slice(o,o+l).join(n),a=i[s];if(a===void 0)return;if(a===null)return null;if(t.endsWith(s)){if(typeof a=="string")return a;if(s&&typeof a[s]=="string")return a[s]}var u=r.slice(o+l).join(n);return u?Pc(a,u,n):void 0}i=i[r[o]]}return i}}var jp=function(e){ho(n,e);var t=Lp(n);function n(r){var i,o=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{ns:["translation"],defaultNS:"translation"};return pt(this,n),i=t.call(this),mo&&nn.call(Gt(i)),i.data=r||{},i.options=o,i.options.keySeparator===void 0&&(i.options.keySeparator="."),i.options.ignoreJSONStructure===void 0&&(i.options.ignoreJSONStructure=!0),i}return ht(n,[{key:"addNamespaces",value:function(i){this.options.ns.indexOf(i)<0&&this.options.ns.push(i)}},{key:"removeNamespaces",value:function(i){var o=this.options.ns.indexOf(i);o>-1&&this.options.ns.splice(o,1)}},{key:"getResource",value:function(i,o,l){var s=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{},a=s.keySeparator!==void 0?s.keySeparator:this.options.keySeparator,u=s.ignoreJSONStructure!==void 0?s.ignoreJSONStructure:this.options.ignoreJSONStructure,p=[i,o];l&&typeof l!="string"&&(p=p.concat(l)),l&&typeof l=="string"&&(p=p.concat(a?l.split(a):l)),i.indexOf(".")>-1&&(p=i.split("."));var d=bi(this.data,p);return d||!u||typeof l!="string"?d:Pc(this.data&&this.data[i]&&this.data[i][o],l,a)}},{key:"addResource",value:function(i,o,l,s){var a=arguments.length>4&&arguments[4]!==void 0?arguments[4]:{silent:!1},u=this.options.keySeparator;u===void 0&&(u=".");var p=[i,o];l&&(p=p.concat(u?l.split(u):l)),i.indexOf(".")>-1&&(p=i.split("."),s=o,o=p[1]),this.addNamespaces(o),Ta(this.data,p,s),a.silent||this.emit("added",i,o,l,s)}},{key:"addResources",value:function(i,o,l){var s=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{silent:!1};for(var a in l)(typeof l[a]=="string"||Object.prototype.toString.apply(l[a])==="[object Array]")&&this.addResource(i,o,a,l[a],{silent:!0});s.silent||this.emit("added",i,o,l)}},{key:"addResourceBundle",value:function(i,o,l,s,a){var u=arguments.length>5&&arguments[5]!==void 0?arguments[5]:{silent:!1},p=[i,o];i.indexOf(".")>-1&&(p=i.split("."),s=l,l=o,o=p[1]),this.addNamespaces(o);var d=bi(this.data,p)||{};s?Nc(d,l,a):d=si(si({},d),l),Ta(this.data,p,d),u.silent||this.emit("added",i,o,l)}},{key:"removeResourceBundle",value:function(i,o){this.hasResourceBundle(i,o)&&delete this.data[i][o],this.removeNamespaces(o),this.emit("removed",i,o)}},{key:"hasResourceBundle",value:function(i,o){return this.getResource(i,o)!==void 0}},{key:"getResourceBundle",value:function(i,o){return o||(o=this.options.defaultNS),this.options.compatibilityAPI==="v1"?si(si({},{}),this.getResource(i,o)):this.getResource(i,o)}},{key:"getDataByLanguage",value:function(i){return this.data[i]}},{key:"hasLanguageSomeTranslations",value:function(i){var o=this.getDataByLanguage(i),l=o&&Object.keys(o)||[];return!!l.find(function(s){return o[s]&&Object.keys(o[s]).length>0})}},{key:"toJSON",value:function(){return this.data}}]),n}(nn),Ec={processors:{},addPostProcessor:function(t){this.processors[t.name]=t},handle:function(t,n,r,i,o){var l=this;return t.forEach(function(s){l.processors[s]&&(n=l.processors[s].process(n,r,i,o))}),n}};function Aa(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(i){return Object.getOwnPropertyDescriptor(e,i).enumerable})),n.push.apply(n,r)}return n}function Oe(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?Aa(Object(n),!0).forEach(function(r){nt(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):Aa(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}function Fp(e){var t=Dp();return function(){var r=_t(e),i;if(t){var o=_t(this).constructor;i=Reflect.construct(r,arguments,o)}else i=r.apply(this,arguments);return ei(this,i)}}function Dp(){if(typeof Reflect>"u"||!Reflect.construct||Reflect.construct.sham)return!1;if(typeof Proxy=="function")return!0;try{return Boolean.prototype.valueOf.call(Reflect.construct(Boolean,[],function(){})),!0}catch{return!1}}var ja={},Fa=function(e){ho(n,e);var t=Fp(n);function n(r){var i,o=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{};return pt(this,n),i=t.call(this),mo&&nn.call(Gt(i)),Ep(["resourceStore","languageUtils","pluralResolver","interpolator","backendConnector","i18nFormat","utils"],r,Gt(i)),i.options=o,i.options.keySeparator===void 0&&(i.options.keySeparator="."),i.logger=kt.create("translator"),i}return ht(n,[{key:"changeLanguage",value:function(i){i&&(this.language=i)}},{key:"exists",value:function(i){var o=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{interpolation:{}};if(i==null)return!1;var l=this.resolve(i,o);return l&&l.res!==void 0}},{key:"extractFromKey",value:function(i,o){var l=o.nsSeparator!==void 0?o.nsSeparator:this.options.nsSeparator;l===void 0&&(l=":");var s=o.keySeparator!==void 0?o.keySeparator:this.options.keySeparator,a=o.ns||this.options.defaultNS||[],u=l&&i.indexOf(l)>-1,p=!this.options.userDefinedKeySeparator&&!o.keySeparator&&!this.options.userDefinedNsSeparator&&!o.nsSeparator&&!Rp(i,l,s);if(u&&!p){var d=i.match(this.interpolator.nestingRegexp);if(d&&d.length>0)return{key:i,namespaces:a};var h=i.split(l);(l!==s||l===s&&this.options.ns.indexOf(h[0])>-1)&&(a=h.shift()),i=h.join(s)}return typeof a=="string"&&(a=[a]),{key:i,namespaces:a}}},{key:"translate",value:function(i,o,l){var s=this;if(me(o)!=="object"&&this.options.overloadTranslationOptionHandler&&(o=this.options.overloadTranslationOptionHandler(arguments)),o||(o={}),i==null)return"";Array.isArray(i)||(i=[String(i)]);var a=o.returnDetails!==void 0?o.returnDetails:this.options.returnDetails,u=o.keySeparator!==void 0?o.keySeparator:this.options.keySeparator,p=this.extractFromKey(i[i.length-1],o),d=p.key,h=p.namespaces,g=h[h.length-1],y=o.lng||this.language,k=o.appendNamespaceToCIMode||this.options.appendNamespaceToCIMode;if(y&&y.toLowerCase()==="cimode"){if(k){var _=o.nsSeparator||this.options.nsSeparator;return a?{res:"".concat(g).concat(_).concat(d),usedKey:d,exactUsedKey:d,usedLng:y,usedNS:g}:"".concat(g).concat(_).concat(d)}return a?{res:d,usedKey:d,exactUsedKey:d,usedLng:y,usedNS:g}:d}var f=this.resolve(i,o),c=f&&f.res,m=f&&f.usedKey||d,w=f&&f.exactUsedKey||d,O=Object.prototype.toString.apply(c),S=["[object Number]","[object Function]","[object RegExp]"],P=o.joinArrays!==void 0?o.joinArrays:this.options.joinArrays,E=!this.i18nFormat||this.i18nFormat.handleAsObject,A=typeof c!="string"&&typeof c!="boolean"&&typeof c!="number";if(E&&c&&A&&S.indexOf(O)<0&&!(typeof P=="string"&&O==="[object Array]")){if(!o.returnObjects&&!this.options.returnObjects){this.options.returnedObjectHandler||this.logger.warn("accessing an object - but returnObjects options is not enabled!");var x=this.options.returnedObjectHandler?this.options.returnedObjectHandler(m,c,Oe(Oe({},o),{},{ns:h})):"key '".concat(d," (").concat(this.language,")' returned an object instead of string.");return a?(f.res=x,f):x}if(u){var H=O==="[object Array]",Te=H?[]:{},Nt=H?w:m;for(var rt in c)if(Object.prototype.hasOwnProperty.call(c,rt)){var xn="".concat(Nt).concat(u).concat(rt);Te[rt]=this.translate(xn,Oe(Oe({},o),{joinArrays:!1,ns:h})),Te[rt]===xn&&(Te[rt]=c[rt])}c=Te}}else if(E&&typeof P=="string"&&O==="[object Array]")c=c.join(P),c&&(c=this.extendTranslation(c,i,o,l));else{var mt=!1,it=!1,C=o.count!==void 0&&typeof o.count!="string",L=n.hasDefaultValue(o),j=C?this.pluralResolver.getSuffix(y,o.count,o):"",V=o["defaultValue".concat(j)]||o.defaultValue;!this.isValidLookup(c)&&L&&(mt=!0,c=V),this.isValidLookup(c)||(it=!0,c=d);var Q=o.missingKeyNoValueFallbackToKey||this.options.missingKeyNoValueFallbackToKey,Pt=Q&&it?void 0:c,ze=L&&V!==c&&this.options.updateMissing;if(it||mt||ze){if(this.logger.log(ze?"updateKey":"missingKey",y,g,d,ze?V:c),u){var Cn=this.resolve(d,Oe(Oe({},o),{},{keySeparator:!1}));Cn&&Cn.res&&this.logger.warn("Seems the loaded translations were in flat JSON format instead of nested. Either set keySeparator: false on init or make sure your translations are published in nested format.")}var be=[],Et=this.languageUtils.getFallbackCodes(this.options.fallbackLng,o.lng||this.language);if(this.options.saveMissingTo==="fallback"&&Et&&Et[0])for(var Lo=0;Lo<Et.length;Lo++)be.push(Et[Lo]);else this.options.saveMissingTo==="all"?be=this.languageUtils.toResolveHierarchy(o.lng||this.language):be.push(o.lng||this.language);var wa=function(In,jo,ka){var Sa=L&&ka!==c?ka:Pt;s.options.missingKeyHandler?s.options.missingKeyHandler(In,g,jo,Sa,ze,o):s.backendConnector&&s.backendConnector.saveMissing&&s.backendConnector.saveMissing(In,g,jo,Sa,ze,o),s.emit("missingKey",In,g,jo,c)};this.options.saveMissing&&(this.options.saveMissingPlurals&&C?be.forEach(function(Ao){s.pluralResolver.getSuffixes(Ao,o).forEach(function(In){wa([Ao],d+In,o["defaultValue".concat(In)]||V)})}):wa(be,d,V))}c=this.extendTranslation(c,i,o,f,l),it&&c===d&&this.options.appendNamespaceToMissingKey&&(c="".concat(g,":").concat(d)),(it||mt)&&this.options.parseMissingKeyHandler&&(this.options.compatibilityAPI!=="v1"?c=this.options.parseMissingKeyHandler(this.options.appendNamespaceToMissingKey?"".concat(g,":").concat(d):d,mt?c:void 0):c=this.options.parseMissingKeyHandler(c))}return a?(f.res=c,f):c}},{key:"extendTranslation",value:function(i,o,l,s,a){var u=this;if(this.i18nFormat&&this.i18nFormat.parse)i=this.i18nFormat.parse(i,Oe(Oe({},this.options.interpolation.defaultVariables),l),s.usedLng,s.usedNS,s.usedKey,{resolved:s});else if(!l.skipInterpolation){l.interpolation&&this.interpolator.init(Oe(Oe({},l),{interpolation:Oe(Oe({},this.options.interpolation),l.interpolation)}));var p=typeof i=="string"&&(l&&l.interpolation&&l.interpolation.skipOnVariables!==void 0?l.interpolation.skipOnVariables:this.options.interpolation.skipOnVariables),d;if(p){var h=i.match(this.interpolator.nestingRegexp);d=h&&h.length}var g=l.replace&&typeof l.replace!="string"?l.replace:l;if(this.options.interpolation.defaultVariables&&(g=Oe(Oe({},this.options.interpolation.defaultVariables),g)),i=this.interpolator.interpolate(i,g,l.lng||this.language,l),p){var y=i.match(this.interpolator.nestingRegexp),k=y&&y.length;d<k&&(l.nest=!1)}l.nest!==!1&&(i=this.interpolator.nest(i,function(){for(var c=arguments.length,m=new Array(c),w=0;w<c;w++)m[w]=arguments[w];return a&&a[0]===m[0]&&!l.context?(u.logger.warn("It seems you are nesting recursively key: ".concat(m[0]," in key: ").concat(o[0])),null):u.translate.apply(u,m.concat([o]))},l)),l.interpolation&&this.interpolator.reset()}var _=l.postProcess||this.options.postProcess,f=typeof _=="string"?[_]:_;return i!=null&&f&&f.length&&l.applyPostProcessor!==!1&&(i=Ec.handle(f,i,o,this.options&&this.options.postProcessPassResolved?Oe({i18nResolved:s},l):l,this)),i}},{key:"resolve",value:function(i){var o=this,l=arguments.length>1&&arguments[1]!==void 0?arguments[1]:{},s,a,u,p,d;return typeof i=="string"&&(i=[i]),i.forEach(function(h){if(!o.isValidLookup(s)){var g=o.extractFromKey(h,l),y=g.key;a=y;var k=g.namespaces;o.options.fallbackNS&&(k=k.concat(o.options.fallbackNS));var _=l.count!==void 0&&typeof l.count!="string",f=_&&!l.ordinal&&l.count===0&&o.pluralResolver.shouldUseIntlApi(),c=l.context!==void 0&&(typeof l.context=="string"||typeof l.context=="number")&&l.context!=="",m=l.lngs?l.lngs:o.languageUtils.toResolveHierarchy(l.lng||o.language,l.fallbackLng);k.forEach(function(w){o.isValidLookup(s)||(d=w,!ja["".concat(m[0],"-").concat(w)]&&o.utils&&o.utils.hasLoadedNamespace&&!o.utils.hasLoadedNamespace(d)&&(ja["".concat(m[0],"-").concat(w)]=!0,o.logger.warn('key "'.concat(a,'" for languages "').concat(m.join(", "),`" won't get resolved as namespace "`).concat(d,'" was not yet loaded'),"This means something IS WRONG in your setup. You access the t function before i18next.init / i18next.loadNamespace / i18next.changeLanguage was done. Wait for the callback or Promise to resolve before accessing it!!!")),m.forEach(function(O){if(!o.isValidLookup(s)){p=O;var S=[y];if(o.i18nFormat&&o.i18nFormat.addLookupKeys)o.i18nFormat.addLookupKeys(S,y,O,w,l);else{var P;_&&(P=o.pluralResolver.getSuffix(O,l.count,l));var E="".concat(o.options.pluralSeparator,"zero");if(_&&(S.push(y+P),f&&S.push(y+E)),c){var A="".concat(y).concat(o.options.contextSeparator).concat(l.context);S.push(A),_&&(S.push(A+P),f&&S.push(A+E))}}for(var x;x=S.pop();)o.isValidLookup(s)||(u=x,s=o.getResource(O,w,x,l))}}))})}}),{res:s,usedKey:a,exactUsedKey:u,usedLng:p,usedNS:d}}},{key:"isValidLookup",value:function(i){return i!==void 0&&!(!this.options.returnNull&&i===null)&&!(!this.options.returnEmptyString&&i==="")}},{key:"getResource",value:function(i,o,l){var s=arguments.length>3&&arguments[3]!==void 0?arguments[3]:{};return this.i18nFormat&&this.i18nFormat.getResource?this.i18nFormat.getResource(i,o,l,s):this.resourceStore.getResource(i,o,l,s)}}],[{key:"hasDefaultValue",value:function(i){var o="defaultValue";for(var l in i)if(Object.prototype.hasOwnProperty.call(i,l)&&o===l.substring(0,o.length)&&i[l]!==void 0)return!0;return!1}}]),n}(nn);function Do(e){return e.charAt(0).toUpperCase()+e.slice(1)}var Da=function(){function e(t){pt(this,e),this.options=t,this.supportedLngs=this.options.supportedLngs||!1,this.logger=kt.create("languageUtils")}return ht(e,[{key:"getScriptPartFromCode",value:function(n){if(!n||n.indexOf("-")<0)return null;var r=n.split("-");return r.length===2||(r.pop(),r[r.length-1].toLowerCase()==="x")?null:this.formatLanguageCode(r.join("-"))}},{key:"getLanguagePartFromCode",value:function(n){if(!n||n.indexOf("-")<0)return n;var r=n.split("-");return this.formatLanguageCode(r[0])}},{key:"formatLanguageCode",value:function(n){if(typeof n=="string"&&n.indexOf("-")>-1){var r=["hans","hant","latn","cyrl","cans","mong","arab"],i=n.split("-");return this.options.lowerCaseLng?i=i.map(function(o){return o.toLowerCase()}):i.length===2?(i[0]=i[0].toLowerCase(),i[1]=i[1].toUpperCase(),r.indexOf(i[1].toLowerCase())>-1&&(i[1]=Do(i[1].toLowerCase()))):i.length===3&&(i[0]=i[0].toLowerCase(),i[1].length===2&&(i[1]=i[1].toUpperCase()),i[0]!=="sgn"&&i[2].length===2&&(i[2]=i[2].toUpperCase()),r.indexOf(i[1].toLowerCase())>-1&&(i[1]=Do(i[1].toLowerCase())),r.indexOf(i[2].toLowerCase())>-1&&(i[2]=Do(i[2].toLowerCase()))),i.join("-")}return this.options.cleanCode||this.options.lowerCaseLng?n.toLowerCase():n}},{key:"isSupportedCode",value:function(n){return(this.options.load==="languageOnly"||this.options.nonExplicitSupportedLngs)&&(n=this.getLanguagePartFromCode(n)),!this.supportedLngs||!this.supportedLngs.length||this.supportedLngs.indexOf(n)>-1}},{key:"getBestMatchFromCodes",value:function(n){var r=this;if(!n)return null;var i;return n.forEach(function(o){if(!i){var l=r.formatLanguageCode(o);(!r.options.supportedLngs||r.isSupportedCode(l))&&(i=l)}}),!i&&this.options.supportedLngs&&n.forEach(function(o){if(!i){var l=r.getLanguagePartFromCode(o);if(r.isSupportedCode(l))return i=l;i=r.options.supportedLngs.find(function(s){if(s.indexOf(l)===0)return s})}}),i||(i=this.getFallbackCodes(this.options.fallbackLng)[0]),i}},{key:"getFallbackCodes",value:function(n,r){if(!n)return[];if(typeof n=="function"&&(n=n(r)),typeof n=="string"&&(n=[n]),Object.prototype.toString.apply(n)==="[object Array]")return n;if(!r)return n.default||[];var i=n[r];return i||(i=n[this.getScriptPartFromCode(r)]),i||(i=n[this.formatLanguageCode(r)]),i||(i=n[this.getLanguagePartFromCode(r)]),i||(i=n.default),i||[]}},{key:"toResolveHierarchy",value:function(n,r){var i=this,o=this.getFallbackCodes(r||this.options.fallbackLng||[],n),l=[],s=function(u){u&&(i.isSupportedCode(u)?l.push(u):i.logger.warn("rejecting language code not found in supportedLngs: ".concat(u)))};return typeof n=="string"&&n.indexOf("-")>-1?(this.options.load!=="languageOnly"&&s(this.formatLanguageCode(n)),this.options.load!=="languageOnly"&&this.options.load!=="currentOnly"&&s(this.getScriptPartFromCode(n)),this.options.load!=="currentOnly"&&s(this.getLanguagePartFromCode(n))):typeof n=="string"&&s(this.formatLanguageCode(n)),o.forEach(function(a){l.indexOf(a)<0&&s(i.formatLanguageCode(a))}),l}}]),e}(),Up=[{lngs:["ach","ak","am","arn","br","fil","gun","ln","mfe","mg","mi","oc","pt","pt-BR","tg","tl","ti","tr","uz","wa"],nr:[1,2],fc:1},{lngs:["af","an","ast","az","bg","bn","ca","da","de","dev","el","en","eo","es","et","eu","fi","fo","fur","fy","gl","gu","ha","hi","hu","hy","ia","it","kk","kn","ku","lb","mai","ml","mn","mr","nah","nap","nb","ne","nl","nn","no","nso","pa","pap","pms","ps","pt-PT","rm","sco","se","si","so","son","sq","sv","sw","ta","te","tk","ur","yo"],nr:[1,2],fc:2},{lngs:["ay","bo","cgg","fa","ht","id","ja","jbo","ka","km","ko","ky","lo","ms","sah","su","th","tt","ug","vi","wo","zh"],nr:[1],fc:3},{lngs:["be","bs","cnr","dz","hr","ru","sr","uk"],nr:[1,2,5],fc:4},{lngs:["ar"],nr:[0,1,2,3,11,100],fc:5},{lngs:["cs","sk"],nr:[1,2,5],fc:6},{lngs:["csb","pl"],nr:[1,2,5],fc:7},{lngs:["cy"],nr:[1,2,3,8],fc:8},{lngs:["fr"],nr:[1,2],fc:9},{lngs:["ga"],nr:[1,2,3,7,11],fc:10},{lngs:["gd"],nr:[1,2,3,20],fc:11},{lngs:["is"],nr:[1,2],fc:12},{lngs:["jv"],nr:[0,1],fc:13},{lngs:["kw"],nr:[1,2,3,4],fc:14},{lngs:["lt"],nr:[1,2,10],fc:15},{lngs:["lv"],nr:[1,2,0],fc:16},{lngs:["mk"],nr:[1,2],fc:17},{lngs:["mnk"],nr:[0,1,2],fc:18},{lngs:["mt"],nr:[1,2,11,20],fc:19},{lngs:["or"],nr:[2,1],fc:2},{lngs:["ro"],nr:[1,2,20],fc:20},{lngs:["sl"],nr:[5,1,2,3],fc:21},{lngs:["he","iw"],nr:[1,2,20,21],fc:22}],zp={1:function(t){return+(t>1)},2:function(t){return+(t!=1)},3:function(t){return 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l=="number"?"_plural_".concat(l.toString()):s():this.options.compatibilityJSON==="v2"||this.options.simplifyPluralSuffix&&n.numbers.length===2&&n.numbers[0]===1?s():this.options.prepend&&o.toString()?this.options.prepend+o.toString():o.toString()}},{key:"shouldUseIntlApi",value:function(){return!bp.includes(this.options.compatibilityJSON)}}]),e}();function za(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(i){return Object.getOwnPropertyDescriptor(e,i).enumerable})),n.push.apply(n,r)}return n}function ot(e){for(var t=1;t<arguments.length;t++){var n=arguments[t]!=null?arguments[t]:{};t%2?za(Object(n),!0).forEach(function(r){nt(e,r,n[r])}):Object.getOwnPropertyDescriptors?Object.defineProperties(e,Object.getOwnPropertyDescriptors(n)):za(Object(n)).forEach(function(r){Object.defineProperty(e,r,Object.getOwnPropertyDescriptor(n,r))})}return e}var Mp=function(){function e(){var t=arguments.length>0&&arguments[0]!==void 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r="".concat(this.prefix).concat(this.unescapePrefix,"(.+?)").concat(this.unescapeSuffix).concat(this.suffix);this.regexpUnescape=new RegExp(r,"g");var i="".concat(this.nestingPrefix,"(.+?)").concat(this.nestingSuffix);this.nestingRegexp=new RegExp(i,"g")}},{key:"interpolate",value:function(n,r,i,o){var l=this,s,a,u,p=this.options&&this.options.interpolation&&this.options.interpolation.defaultVariables||{};function d(_){return _.replace(/\$/g,"$$$$")}var h=function(f){if(f.indexOf(l.formatSeparator)<0){var c=Ra(r,p,f);return l.alwaysFormat?l.format(c,void 0,i,ot(ot(ot({},o),r),{},{interpolationkey:f})):c}var m=f.split(l.formatSeparator),w=m.shift().trim(),O=m.join(l.formatSeparator).trim();return l.format(Ra(r,p,w),O,i,ot(ot(ot({},o),r),{},{interpolationkey:w}))};this.resetRegExp();var g=o&&o.missingInterpolationHandler||this.options.missingInterpolationHandler,y=o&&o.interpolation&&o.interpolation.skipOnVariables!==void 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Ma(){return{debug:!1,initImmediate:!0,ns:["translation"],defaultNS:["translation"],fallbackLng:["dev"],fallbackNS:!1,supportedLngs:!1,nonExplicitSupportedLngs:!1,load:"all",preload:!1,simplifyPluralSuffix:!0,keySeparator:".",nsSeparator:":",pluralSeparator:"_",contextSeparator:"_",partialBundledLanguages:!1,saveMissing:!1,updateMissing:!1,saveMissingTo:"fallback",saveMissingPlurals:!0,missingKeyHandler:!1,missingInterpolationHandler:!1,postProcess:!1,postProcessPassResolved:!1,returnNull:!0,returnEmptyString:!0,returnObjects:!1,joinArrays:!1,returnedObjectHandler:!1,parseMissingKeyHandler:!1,appendNamespaceToMissingKey:!1,appendNamespaceToCIMode:!1,overloadTranslationOptionHandler:function(t){var n={};if(me(t[1])==="object"&&(n=t[1]),typeof t[1]=="string"&&(n.defaultValue=t[1]),typeof t[2]=="string"&&(n.tDescription=t[2]),me(t[2])==="object"||me(t[3])==="object"){var r=t[3]||t[2];Object.keys(r).forEach(function(i){n[i]=r[i]})}return n},interpolation:{escapeValue:!0,format:function(t,n,r,i){return t},prefix:"{{",suffix:"}}",formatSeparator:",",unescapePrefix:"-",nestingPrefix:"$t(",nestingSuffix:")",nestingOptionsSeparator:",",maxReplaces:1e3,skipOnVariables:!0}}}function Ba(e){return typeof e.ns=="string"&&(e.ns=[e.ns]),typeof e.fallbackLng=="string"&&(e.fallbackLng=[e.fallbackLng]),typeof e.fallbackNS=="string"&&(e.fallbackNS=[e.fallbackNS]),e.supportedLngs&&e.supportedLngs.indexOf("cimode")<0&&(e.supportedLngs=e.supportedLngs.concat(["cimode"])),e}function Ka(e,t){var n=Object.keys(e);if(Object.getOwnPropertySymbols){var r=Object.getOwnPropertySymbols(e);t&&(r=r.filter(function(i){return Object.getOwnPropertyDescriptor(e,i).enumerable})),n.push.apply(n,r)}return n}function gt(e){for(var t=1;t<arguments.length;t++){var 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f?typeof f=="function"?new f:f:null}if(!this.options.isClone){this.modules.logger?kt.init(a(this.modules.logger),this.options):kt.init(null,this.options);var u;this.modules.formatter?u=this.modules.formatter:typeof Intl<"u"&&(u=Kp);var p=new Da(this.options);this.store=new jp(this.options.resources,this.options);var d=this.services;d.logger=kt,d.resourceStore=this.store,d.languageUtils=p,d.pluralResolver=new $p(p,{prepend:this.options.pluralSeparator,compatibilityJSON:this.options.compatibilityJSON,simplifyPluralSuffix:this.options.simplifyPluralSuffix}),u&&(!this.options.interpolation.format||this.options.interpolation.format===s.interpolation.format)&&(d.formatter=a(u),d.formatter.init(d,this.options),this.options.interpolation.format=d.formatter.format.bind(d.formatter)),d.interpolator=new Mp(this.options),d.utils={hasLoadedNamespace:this.hasLoadedNamespace.bind(this)},d.backendConnector=new Yp(a(this.modules.backend),d.resourceStore,d,this.options),d.backendConnector.on("*",function(f){for(var c=arguments.length,m=new Array(c>1?c-1:0),w=1;w<c;w++)m[w-1]=arguments[w];i.emit.apply(i,[f].concat(m))}),this.modules.languageDetector&&(d.languageDetector=a(this.modules.languageDetector),d.languageDetector.init&&d.languageDetector.init(d,this.options.detection,this.options)),this.modules.i18nFormat&&(d.i18nFormat=a(this.modules.i18nFormat),d.i18nFormat.init&&d.i18nFormat.init(this)),this.translator=new Fa(this.services,this.options),this.translator.on("*",function(f){for(var c=arguments.length,m=new Array(c>1?c-1:0),w=1;w<c;w++)m[w-1]=arguments[w];i.emit.apply(i,[f].concat(m))}),this.modules.external.forEach(function(f){f.init&&f.init(i)})}if(this.format=this.options.interpolation.format,l||(l=ai),this.options.fallbackLng&&!this.services.languageDetector&&!this.options.lng){var h=this.services.languageUtils.getFallbackCodes(this.options.fallbackLng);h.length>0&&h[0]!=="dev"&&(this.options.lng=h[0])}!this.services.languageDetector&&!this.options.lng&&this.logger.warn("init: no languageDetector is used and no lng is defined");var g=["getResource","hasResourceBundle","getResourceBundle","getDataByLanguage"];g.forEach(function(f){i[f]=function(){var c;return(c=i.store)[f].apply(c,arguments)}});var y=["addResource","addResources","addResourceBundle","removeResourceBundle"];y.forEach(function(f){i[f]=function(){var c;return(c=i.store)[f].apply(c,arguments),i}});var k=fr(),_=function(){var c=function(w,O){i.isInitialized&&!i.initializedStoreOnce&&i.logger.warn("init: i18next is already initialized. 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+可用框架：Caffe、Kaldi、Apache MXNet、PyTorch、ONNX、TensorFlow。`},Uh={apt:{steps:{download:"<0>第 1 步：</0>下载 <2>{{filename}}</2>。您也可以使用以下命令",addKey:"<0>第 2 步：</0>将此密钥添加到系统密钥环。",addRepository:"<0>第 3 步：</0>通过以下命令添加存储库。",updateList:"<0>第 4 步：</0>使用更新命令更新软件包列表。",verify:"<0>第 5 步：</0>验证 APT 存储库的设置是否正确。使用 apt-cache 命令查看包含所有可用 OpenVINO 程序包和组件的列表",install:"<0>第 6 步：</0>安装 OpenVINO 运行时。"}},brew:{steps:{install:"下载并安装程序包"}},conan:{steps:{createConanFile:"<0>第 1 步：</0>为您的 OpenVINO 项目创建一个 <2>{{txtFilename}}</2> 文件并在那里添加 “openvino” 依赖项",install:"<0>第 2 步：</0>运行下面的命令创建 <2>{{cmakeFilename}}</2> 文件，该文件将用来编译 OpenVINO 项目",compile:"<0>第 3 步：</0>配置和编译 OpenVINO 项目"}},conda:{steps:{createEnv:"<0>第 1 步：</0>创建 Anaconda 环境（以 Python 3.10 为例）",activateEnv:"<0>第 2 步：</0>激活 Anaconda 环境",update:"<0>第 3 步：</0>将 Anaconda 环境更新至最新版本",install:"<0>第 4 步：</0>下载并安装程序包。"}},download:{downloadArchives:"下载归档文件",downloadArchivesGenAI:"下载 GenAI 档案",gotoDocker:"转到 Docker",gotoInstallInstruction:"转到安装说明",useFollowingLink:"使用下方链接："},githubGitee:{steps:{useGitClone:"<0>第 1 步：</0>运用 Git 克隆 OpenVINO 工具套件存储库。",buildInstructions:"<0>第 2 步：</0>按照<2>说明从源代码构建</2>"}},npm:{steps:{install:"下载并安装程序包"}},pip:{steps:{createVenv:"<0>第 1 步：</0>创建虚拟环境。",activateVenv:"<0>第 2 步：</0>激活虚拟环境。",upgradePip:"<0>第 3 步：</0>将 pip 升级到最新版本。",install:"<0>第 4 步：</0>下载并安装程序包。"}},vcpkg:{steps:{install:"下载并安装程序包"}},yum:{steps:{createRepoFile:"<0>第 1 步：</0>以正常用户身份在 /tmp 目录中创建 YUM 存储库文件。",moveFile:"<0>第 2 步：</0>将新的 openvino-{{year}}.repo 文件移动到 YUM 配置目录 <4>{{directory}}</4>。",verify:"<0>第 3 步：</0>使用以下命令验证新建存储库的设置是否正确。",install:"<0>第 4 步：</0>安装 OpenVINO 运行时。"}},zypper:{steps:{addRepo:"<0>第 1 步：</0>使用下面的命令创建一个 ZYPPER 存储库文件",refresh:"<0>第 2 步：</0>刷新存储库",install:"<0>第 3 步：</0>安装 OpenVINO"}}},zh={selectorForm:Fh,frameworks:Dh,distributions:Uh},bh={translation:zh,common:Th,core:jh},Vh={prevReleases:"Previous Releases",installationInstructions:"Installation Instructions",getStarted:"Get Started Guide",troubleshooting:"Troubleshooting Guide",systemRequirements:"System Requirements",githubRepository:"OpenVINO Toolkit GitHub Repository",githubGenAIRepository:"OpenVINO GenAI GitHub Repository",giteeRepository:"OpenVINO Toolkit Gitee Repository",giteeGenAIRepository:"OpenVINO GenAI Gitee Repository"},$h={OpenVINONotebooks:"Ready to run OpenVINO Notebooks",tokenizers:"<0>OpenVINO Tokenizers</0> to streamline tokenizer conversion",nncf:"<0>NNCF</0> for implementing compression algorithms on models",ovms:"<0>OVMS</0> for serving models optimized for deployment"},Mh={copy:"Copy",copied:"Copied"},Bh={resources:Vh,relatedTools:$h,copyCommand:Mh},Kh={titles:{recommended:"Recommended",hddlSupport:"Includes NCS2/HDDL support",LTS:"LTS",nightlyBuild:"Nightly Build"}},Hh={devTools:{title:"Development Tools",subtitle:"Your best option to develop and optimize deep learning models."},runtime:{title:"Runtime",subtitle:"You already have a model and want to run inference on it."}},Wh={includesNPUPlugin:"Includes NPU plugin",openVINOArchives:"OpenVINO Archives",source:"Source",pythonAPIOnly:"Python API only",CAPIOnly:"C++ API only",jsAPIOnly:"JavaScript API only"},Gh={package:{base:{title:"OpenVINO Base Package",subtitle:"Full inference with basic GenAI"},genai:{title:"OpenVINO with GenAI",subtitle:"Full inference + complete GenAI package"}},versions:Kh,environment:Hh,distributions:Wh},Yh={titles:{envinronment:"Environment",os:"Operating System",version:"Version",distribution:"Distribution",frameworks:"Frameworks",package:"Package",install:"Install",resources:"Resources",relatedTools:"Related Tools"}},Qh={install:"Install Frameworks",tooltip:`We assume that needed frameworks are already installed in your environment. If not, you can install them with OpenVINO toolkit.
+Available frameworks: Caffe, Kaldi, Apache MXNet, PyTorch, ONNX, TensorFlow.`},Xh={apt:{steps:{download:"<0>Step 1:</0> Download the <2>{{filename}}</2>. You can also use the following command",addKey:"<0>Step 2:</0> Add this key to the system keyring",addRepository:"<0>Step 3:</0> Add the repository via the following command",updateList:"<0>Step 4:</0> Update the list of packages via the update command",verify:"<0>Step 5:</0> Verify that the APT repository is properly set up. Run the apt-cache command to see a list of all available OpenVINO packages and components",install:"<0>Step 6:</0> Install OpenVINO Runtime"}},brew:{steps:{install:"Download and install the package"}},conan:{steps:{createConanFile:"<0>Step 1:</0> Create a <2>{{txtFilename}}</2> file for your OpenVINO project and add “openvino” dependency in there",install:"<0>Step 2:</0> Run the command below to create <2>{{cmakeFilename}}</2> file, which will be used to compile your project with OpenVINO",compile:"<0>Step 3:</0> Configure and compile your project with OpenVINO"}},conda:{steps:{createEnv:"<0>Step 1:</0> Create the Anaconda environment (Python 3.10 used as an example)",activateEnv:"<0>Step 2:</0> Activate the Anaconda environment",update:"<0>Step 3:</0> Update the Anaconda environment to the latest version",install:"<0>Step 4:</0> Download and install the package"}},download:{downloadArchives:"Download Archives",downloadArchivesGenAI:"Download Archives with GenAI",gotoDocker:"Go to Docker",gotoInstallInstruction:"Go to Installation Instructions",useFollowingLink:"Use the following link:"},githubGitee:{steps:{useGitClone:"<0>Step 1:</0> Use Git to clone the OpenVINO toolkit repository",buildInstructions:"<0>Step 2:</0> Follow the <2>instructions to build from source</2>"}},npm:{steps:{install:"Download and install the package"}},pip:{steps:{createVenv:"<0>Step 1:</0> Create virtual environment",activateVenv:"<0>Step 2:</0> Activate virtual environment",upgradePip:"<0>Step 3:</0> Upgrade pip to latest version",install:"<0>Step 4:</0> Download and install the package"}},vcpkg:{steps:{install:"Download and install the package"}},yum:{steps:{createRepoFile:"<0>Step 1:</0> Create the YUM repo file in the /tmp directory as a normal user",moveFile:"<0>Step 2:</0> Move the new openvino-{{year}}.repo file to the YUM configuration directory <4>{{directory}}</4>",verify:"<0>Step 3:</0> Verify that the new repo is properly set up by running the following command",install:"<0>Step 4:</0> Install OpenVINO Runtime"}},zypper:{steps:{addRepo:"<0>Step 1:</0> Create a ZYPPER repository file with the command below",refresh:"<0>Step 2:</0> Refresh repositories",install:"<0>Step 3:</0> Install OpenVINO"}}},Jh={selectorForm:Yh,frameworks:Qh,distributions:Xh},Zh={translation:Jh,common:Bh,core:Gh},qn={en:Zh,cn:bh},yl="en";if(!(yl in qn))throw new Error(`'${yl}' language is not supported`);fe.use(kh).init({debug:!1,resources:qn,lng:yl,fallbackLng:"en",interpolation:{escapeValue:!1},supportedLngs:Object.keys(qn),returnObjects:!1,returnNull:!1});fe.t;var wl={},Lc={exports:{}},We={},Ac={exports:{}},jc={};/**
  * @license React
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  *
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  *
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  *
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I=(e=>(e.ROOT="ROOT",e.PACKAGE="PACKAGE",e.VERSION="VERSION",e.ENVIRONMENT="ENVIRONMENT",e.OP_SYSTEM="OP_SYSTEM",e.DISTRIBUTION="DISTRIBUTION",e.FRAMEWORK="FRAMEWORK",e))(I||{}),Df=(e=>(e.ROOT="ROOT",e))(Df||{}),zt=(e=>(e.v_2022_3_2="v_2022_3_2",e.v_2023_3_0="v_2023_3_0",e.v_2024_4_0="v_2024_4_0",e.NIGHTLY="NIGHTLY",e))(zt||{}),oi=(e=>(e.DEV_TOOLS="DEV_TOOLS",e.RUNTIME="RUNTIME",e))(oi||{}),Ze=(e=>(e.WINDOWS="WINDOWS",e.MACOS="MACOS",e.LINUX="LINUX",e))(Ze||{}),Ee=(e=>(e.OPENVINO_BASE="OPENVINO_BASE",e.OPENVINO_GENAI="OPENVINO_GENAI",e))(Ee||{});const iv={title:"Windows"},ov={title:"macOS"},lv={title:"Linux"};var F=(e=>(e.PIP="PIP",e.ARCHIVE="ARCHIVE",e.GITHUB="GITHUB",e.GITEE="GITEE",e.DOCKER="DOCKER",e.APT="APT",e.YUM="YUM",e.CONDA="CONDA",e.BREW="BREW",e.VCPKG="VCPKG",e.CONAN="CONAN",e.NPM="NPM",e.ZYPPER="ZYPPER",e.SNAP="SNAP",e))(F||{}),Me=(e=>(e.CAFFE="CAFFE",e.PYTORCH="PYTORCH",e.TENSORFLOW_1="TENSORFLOW_1",e.TENSORFLOW_2="TENSORFLOW_2",e.ONNX="ONNX",e.KALDI="KALDI",e.MXNET="MXNET",e))(Me||{});class Re{constructor(t,n=[]){Ve(this,"level");Ve(this,"key");Ve(this,"metadata");Ve(this,"children",[]);Ve(this,"isDefault",!1);Ve(this,"checked",!1);Ve(this,"childrenSelector");this.level=t.level,this.key=t.key,this.metadata=t.metadata,this.children=n,this.childrenSelector=t.childrenSelector}default(){return this.isDefault=!0,this.checked=!0,this}toOption(){return{level:this.level,key:this.key,metadata:this.metadata,checked:this.checked,hasFootnote:!1}}}const sv=rv("distributions");var ne=(e=>(e[e.UBUNTU_18=0]="UBUNTU_18",e[e.UBUNTU_20=1]="UBUNTU_20",e[e.UBUNTU_22=2]="UBUNTU_22",e[e.UBUNTU_24=3]="UBUNTU_24",e))(ne||{});class Ue extends Re{constructor(n,r=[]){super(n,r);Ve(this,"footnoteLevel",null)}toOption(){return{...super.toOption(),key:this.key,data:this.data}}addFootnote(n){return this.footnoteLevel=n,this}includesNPUPlugin(){const n=sv("includesNPUPlugin");return this.metadata.subtitle?this.metadata.subtitle=[n,this.metadata.subtitle].join(`
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 [generators]
 CMakeDeps
 CMakeToolchain
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-cmake --build <build dir> --parallel`};class dr extends De{constructor(t){super({level:I.DISTRIBUTION,key:A.CONDA,metadata:{title:"Conda"}}),this._data=t}get data(){return{...this._data,commands:wv}}}const wv={createEnv:"conda create --name py310 python=3.10",activateEnv:"conda activate py310",update:"conda update --all",getInstall:({metadata:e})=>`conda install -c conda-forge openvino=${e.condaVersion}`};class _n extends De{constructor(t){super({level:I.DISTRIBUTION,key:A.DOCKER,metadata:{title:"Docker"}}),this.data=t}}class xe extends De{constructor(){super({level:I.DISTRIBUTION,key:A.GITEE,metadata:{title:"Gitee",subtitle:K("distributions.source")}})}get data(){return{commands:kv,links:Sv}}}const kv={getCloneCommand(e){return`git clone -b ${e.metadata.giteeVersion} https://gitee.com/openvinotoolkit-prc/openvino.git`}},Sv={getBuildInstructionsLink:e=>`https://github.com/openvinotoolkit/openvino/blob/${e.metadata.giteeVersion}/docs/dev/build.md`,getRepositoryLink:e=>`https://gitee.com/openvinotoolkit-prc/openvino/tree/${e.metadata.giteeVersion}`};class Ee extends De{constructor(){super({level:I.DISTRIBUTION,key:A.GITHUB,metadata:{title:"GitHub",subtitle:K("distributions.source")}})}get data(){return{commands:Ov,links:_v}}}const Ov={getCloneCommand(e){return`git clone -b ${e.metadata.githubVersion} https://github.com/openvinotoolkit/openvino.git`}},_v={getBuildInstructionsLink:e=>`https://github.com/openvinotoolkit/openvino/blob/${e.metadata.githubVersion}/docs/dev/build.md`,getRepositoryLink:e=>`https://github.com/openvinotoolkit/openvino/tree/${e.metadata.githubVersion}`};class ha extends De{constructor(t){super({level:I.DISTRIBUTION,key:A.NPM,metadata:{title:"npm",subtitle:K("distributions.jsAPIOnly")}}),this._data=t}get data(){return{...this._data,commands:Nv}}}const Nv={getInstall:({metadata:e})=>`npm install openvino-node@${e.npmVersion}`},Pv=(e,t)=>{e.forEach(n=>{n.key===Me.TENSORFLOW_1&&n.checked&&t===Me.TENSORFLOW_2||n.key===Me.TENSORFLOW_2&&n.checked&&t===Me.TENSORFLOW_1?n.checked=!1:t===n.key&&(n.checked=!n.checked)})},bf=(e,t)=>{e.forEach(n=>{n.checked=n.key===t,n.key===A.PIP&&n.checked&&n.children.forEach(r=>r.checked=r.isDefault)})},xv=()=>[new Te({level:I.FRAMEWORK,key:Me.CAFFE,metadata:{title:"Caffe",pipKey:"caffe"}}),new Te({level:I.FRAMEWORK,key:Me.KALDI,metadata:{title:"Kaldi",pipKey:"kaldi"}}),new Te({level:I.FRAMEWORK,key:Me.MXNET,metadata:{title:"Apache MXNet",pipKey:"mxnet"}}),new Te({level:I.FRAMEWORK,key:Me.ONNX,metadata:{title:"Open Neural Network Exchange (ONNX)",pipKey:"ONNX"}}),new Te({level:I.FRAMEWORK,key:Me.PYTORCH,metadata:{title:"PyTorch",pipKey:"pytorch"}}),new Te({level:I.FRAMEWORK,key:Me.TENSORFLOW_1,metadata:{title:"TensorFlow 1.x",pipKey:"tensorflow"}}),new Te({level:I.FRAMEWORK,key:Me.TENSORFLOW_2,metadata:{title:"TensorFlow 2.x",pipKey:"tensorflow2"}})];class te extends De{constructor(t,{hasFrameworks:n,pythonAPI:r}={}){super({level:I.DISTRIBUTION,key:A.PIP,metadata:{title:"PIP",subtitle:r?K("distributions.pythonAPIOnly"):void 0},childrenSelector:Pv},n?xv():[]),this._data=t}get data(){return{...this._data,commands:Tv,frameworks:this.children.filter(({checked:t})=>t).map(t=>t.toOption())}}}function zf(e){return e.key===Ut.NIGHTLY?"openvino_nightly_env":"openvino_env"}function Ev(e,t){const n=zf(t);return e.key===Je.WINDOWS?`python -m venv ${n}`:`python3 -m venv ${n}`}function Cv(e,t){const n=zf(t);return e.key===Je.WINDOWS?`${n}\\Scripts\\activate`:`source ${n}/bin/activate`}function Iv({ovPackage:e,environment:t,os:n,version:r,distribution:i}){const o=i.data.frameworks.map(u=>u.metadata.pipKey),l=o.length?`[${o.join(",")}]`:"",s=(t==null?void 0:t.key)===oi.DEV_TOOLS?"openvino-dev":e.key===On.OPENVINO_GENAI?"openvino-genai":"openvino";if(r.key===Ut.NIGHTLY)return`pip install --pre -U ${s} --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly`;let a=`${s}${l}==${r.metadata.pipVersion}`;return n.key===Je.MACOS&&(a=`'${a}'`),`pip install ${a}`}const Tv={getCreateVenvCommand:Ev,getActivateVenvCommand:Cv,upgradeCommand:"python -m pip install --upgrade pip",getInstallCommand:Iv};class Rv extends De{constructor(t){super({level:I.DISTRIBUTION,key:A.SNAP,metadata:{title:"Snap",subtitle:K("distributions.CAPIOnly")}}),this.data=t}}class ma extends De{constructor(t){super({level:I.DISTRIBUTION,key:A.VCPKG,metadata:{title:"vcpkg",subtitle:K("distributions.source")}}),this._data=t}get data(){return{...this._data,commands:Lv}}}const Lv={install:"vcpkg install openvino"};class ga extends De{constructor(t){super({level:I.DISTRIBUTION,key:A.YUM,metadata:{title:"YUM",subtitle:K("distributions.CAPIOnly")}}),this._data=t}get data(){return{...this._data,commands:jv}}}const ac="/etc/yum.repos.d",jv={directory:ac,getCreateRepoCommand:e=>`tee > /tmp/openvino-${e.metadata.yumYear}.repo << EOF
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+cmake --build <build dir> --parallel`};class dr extends Ue{constructor(t){super({level:I.DISTRIBUTION,key:F.CONDA,metadata:{title:"Conda"}}),this._data=t}get data(){return{...this._data,commands:vv}}}const vv={createEnv:"conda create --name py310 python=3.10",activateEnv:"conda activate py310",update:"conda update --all",getInstall:({metadata:e})=>`conda install -c conda-forge openvino=${e.condaVersion}`};class bt extends Ue{constructor(t){super({level:I.DISTRIBUTION,key:F.DOCKER,metadata:{title:"Docker"}}),this.data=t}}class ie extends Ue{constructor(){super({level:I.DISTRIBUTION,key:F.GITEE,metadata:{title:"Gitee",subtitle:q("distributions.source")}})}get data(){return{commands:yv,links:wv}}}const yv={getCloneCommand(e,t){return e.key===Ee.OPENVINO_BASE?`git clone -b ${t.metadata.giteeVersion} https://gitee.com/openvinotoolkit-prc/openvino.git`:`git clone -b ${t.metadata.genaiGitVersion} --recursive https://gitee.com/openvinotoolkit-prc/openvino.genai.git`}},wv={getBuildInstructionsLink:(e,t)=>e.key===Ee.OPENVINO_BASE?`https://github.com/openvinotoolkit/openvino/blob/${t.metadata.giteeVersion}/docs/dev/build.md`:`https://github.com/openvinotoolkit/openvino.genai/blob/${t.metadata.genaiGitVersion}/src/docs/BUILD.md`,getRepositoryLink:(e,t)=>e.key===Ee.OPENVINO_BASE?`https://gitee.com/openvinotoolkit-prc/openvino/tree/${t.metadata.giteeVersion}`:`https://gitee.com/openvinotoolkit-prc/openvino.genai/tree/${t.metadata.genaiGitVersion}`};class oe extends Ue{constructor(){super({level:I.DISTRIBUTION,key:F.GITHUB,metadata:{title:"GitHub",subtitle:q("distributions.source")}})}get data(){return{commands:kv,links:Sv}}}const kv={getCloneCommand(e,t){return e.key===Ee.OPENVINO_BASE?`git clone -b ${t.metadata.githubVersion} https://github.com/openvinotoolkit/openvino.git`:`git clone -b ${t.metadata.genaiGitVersion} --recursive https://github.com/openvinotoolkit/openvino.genai.git`}},Sv={getBuildInstructionsLink:(e,t)=>e.key===Ee.OPENVINO_BASE?`https://github.com/openvinotoolkit/openvino/blob/${t.metadata.githubVersion}/docs/dev/build.md`:`https://github.com/openvinotoolkit/openvino.genai/blob/${t.metadata.genaiGitVersion}/src/docs/BUILD.md`,getRepositoryLink:(e,t)=>e.key===Ee.OPENVINO_BASE?`https://github.com/openvinotoolkit/openvino/tree/${t.metadata.githubVersion}`:`https://github.com/openvinotoolkit/openvino.genai/tree/${t.metadata.genaiGitVersion}`};class ha extends Ue{constructor(t){super({level:I.DISTRIBUTION,key:F.NPM,metadata:{title:"npm",subtitle:q("distributions.jsAPIOnly")}}),this._data=t}get data(){return{...this._data,commands:Ov}}}const Ov={getInstall:({metadata:e})=>`npm install openvino-node@${e.npmVersion}`},_v=(e,t)=>{e.forEach(n=>{n.key===Me.TENSORFLOW_1&&n.checked&&t===Me.TENSORFLOW_2||n.key===Me.TENSORFLOW_2&&n.checked&&t===Me.TENSORFLOW_1?n.checked=!1:t===n.key&&(n.checked=!n.checked)})},zf=(e,t)=>{e.forEach(n=>{n.checked=n.key===t,n.key===F.PIP&&n.checked&&n.children.forEach(r=>r.checked=r.isDefault)})},Nv=()=>[new Re({level:I.FRAMEWORK,key:Me.CAFFE,metadata:{title:"Caffe",pipKey:"caffe"}}),new Re({level:I.FRAMEWORK,key:Me.KALDI,metadata:{title:"Kaldi",pipKey:"kaldi"}}),new Re({level:I.FRAMEWORK,key:Me.MXNET,metadata:{title:"Apache MXNet",pipKey:"mxnet"}}),new Re({level:I.FRAMEWORK,key:Me.ONNX,metadata:{title:"Open Neural Network Exchange (ONNX)",pipKey:"ONNX"}}),new Re({level:I.FRAMEWORK,key:Me.PYTORCH,metadata:{title:"PyTorch",pipKey:"pytorch"}}),new Re({level:I.FRAMEWORK,key:Me.TENSORFLOW_1,metadata:{title:"TensorFlow 1.x",pipKey:"tensorflow"}}),new Re({level:I.FRAMEWORK,key:Me.TENSORFLOW_2,metadata:{title:"TensorFlow 2.x",pipKey:"tensorflow2"}})];class te extends Ue{constructor(t,{hasFrameworks:n,pythonAPI:r}={}){super({level:I.DISTRIBUTION,key:F.PIP,metadata:{title:"PIP",subtitle:r?q("distributions.pythonAPIOnly"):void 0},childrenSelector:_v},n?Nv():[]),this._data=t}get data(){return{...this._data,commands:Cv,frameworks:this.children.filter(({checked:t})=>t).map(t=>t.toOption())}}}function bf(e){return e.key===zt.NIGHTLY?"openvino_nightly_env":"openvino_env"}function Pv(e,t){const n=bf(t);return e.key===Ze.WINDOWS?`python -m venv ${n}`:`python3 -m venv ${n}`}function Ev(e,t){const n=bf(t);return e.key===Ze.WINDOWS?`${n}\\Scripts\\activate`:`source ${n}/bin/activate`}function xv({ovPackage:e,environment:t,os:n,version:r,distribution:i}){const o=i.data.frameworks.map(u=>u.metadata.pipKey),l=o.length?`[${o.join(",")}]`:"",s=(t==null?void 0:t.key)===oi.DEV_TOOLS?"openvino-dev":e.key===Ee.OPENVINO_GENAI?"openvino-genai":"openvino";if(r.key===zt.NIGHTLY)return`pip install --pre -U ${s} --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly`;let a=`${s}${l}==${r.metadata.pipVersion}`;return n.key===Ze.MACOS&&(a=`'${a}'`),`pip install ${a}`}const Cv={getCreateVenvCommand:Pv,getActivateVenvCommand:Ev,upgradeCommand:"python -m pip install --upgrade pip",getInstallCommand:xv};class Iv extends Ue{constructor(t){super({level:I.DISTRIBUTION,key:F.SNAP,metadata:{title:"Snap",subtitle:q("distributions.CAPIOnly")}}),this.data=t}}class ma extends Ue{constructor(t){super({level:I.DISTRIBUTION,key:F.VCPKG,metadata:{title:"vcpkg",subtitle:q("distributions.source")}}),this._data=t}get data(){return{...this._data,commands:Tv}}}const Tv={install:"vcpkg install openvino"};class ga extends Ue{constructor(t){super({level:I.DISTRIBUTION,key:F.YUM,metadata:{title:"YUM",subtitle:q("distributions.CAPIOnly")}}),this._data=t}get data(){return{...this._data,commands:Rv}}}const ac="/etc/yum.repos.d",Rv={directory:ac,getCreateRepoCommand:e=>`tee > /tmp/openvino-${e.metadata.yumYear}.repo << EOF
 [OpenVINO]
 name=Intel(R) Distribution of OpenVINO ${e.metadata.yumYear}
 baseurl=https://yum.repos.intel.com/openvino/${e.metadata.yumYear}
@@ -58,4 +58,4 @@ enabled=1
 gpgcheck=1
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spark-focus-visible-snap"},qy=({label:e,checked:t=!1,onClick:n,testId:r})=>R("label",{className:ml.toggleSwitchField,"data-cy":r,children:[v("input",{type:"checkbox",className:ml.toggleSwitchInput,role:"switch",checked:t,onChange:()=>n==null?void 0:n()}),v("span",{className:ml.toggleSwitchSelector}),v("div",{children:e})]});const dn={toggleButton:"spark-button spark-button-size-l spark-focus-visible spark-focus-visible-self spark-focus-visible-snap",toggleButtonGroup:"spark-button-group spark-button-group-orientation-horizontal spark-button-group-align-start spark-button-group-spacing-l",actionButton:"spark-button-action",secondaryButton:"spark-button-secondary",disabledButton:"spark-button-disabled",buttonContent:"spark-button-content",fontXs:"spark-font-25"},Yf=({onClick:e,checked:t=!1,disabled:n=!1,title:r,subtitle:i,value:o})=>v("button",{className:`${dn.toggleButton} ${t?dn.actionButton:dn.secondaryButton} 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r=U.useContext(ya),i=t.map(({level:o,key:l,checked:s,metadata:a})=>v(Yf,{value:`${o}_${l}`,checked:s,title:a.title,subtitle:a.subtitle,onClick:()=>r(o,l)},l));return v(Ie,{title:e,testId:n,children:v(Qf,{children:i})})}function t0({state:e}){var a,u;const t=e.PACKAGE.nodes,n=e.VERSION.nodes,r=(a=e.ENVIRONMENT)==null?void 0:a.nodes.map(p=>({...p,metadata:{...p.metadata,subtitle:void 0}})),i=e.OP_SYSTEM.nodes,o=e.DISTRIBUTION.nodes,l=(u=e.FRAMEWORK)==null?void 0:u.nodes;U.useEffect(()=>Ye.combinationView(),[e]);const{t:s}=B("translation",{keyPrefix:"selectorForm.titles"});return 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diff --git a/docs/sphinx_setup/_static/selector-tool/selector-9dbd5b1.html b/docs/sphinx_setup/_static/selector-tool/selector-8d4cf1d.html
similarity index 77%
rename from docs/sphinx_setup/_static/selector-tool/selector-9dbd5b1.html
rename to docs/sphinx_setup/_static/selector-tool/selector-8d4cf1d.html
index bafe5d817e8171..460b5c1484857b 100644
--- a/docs/sphinx_setup/_static/selector-tool/selector-9dbd5b1.html
+++ b/docs/sphinx_setup/_static/selector-tool/selector-8d4cf1d.html
@@ -1,7 +1,7 @@
 <!DOCTYPE html>
 <html lang="en">
   <head>
-    <meta name="version" content="v_2024_3_0-9dbd5b1" />
+    <meta name="version" content="v_2024_4_0-8d4cf1d" />
     <meta charset="UTF-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" />
     <title>Download Intel® Distribution of OpenVINO™ Toolkit</title>
@@ -9,11 +9,11 @@
       name="description"
       content="Download a version of the Intel® Distribution of OpenVINO™ toolkit for Linux, Windows, or macOS."
     />
-    <script type="module" crossorigin src="./assets/selector-83076730.js"></script>
+    <script type="module" crossorigin src="./assets/selector-4f9c3c09.js"></script>
     <link rel="stylesheet" href="./assets/selector-b0e2854e.css">
   </head>
   <body>
     <div id="root"></div>
-
+    
   </body>
 </html>

From 97a71c298c1b5aeb5df76830508b2c404977ed89 Mon Sep 17 00:00:00 2001
From: Tomasz Jankowski <tomasz1.jankowski@intel.com>
Date: Fri, 20 Sep 2024 11:34:34 +0200
Subject: [PATCH 25/49] [Templ test] Mish: Enable whole Tensor comparison
 (#26681)

### Details:
 - Enabled whole tensor comparison.
 - Fixed function template argument deduction.
 - Code improvements.

### Tickets:
 - CVS-137185
---
 .../tests/functional/op_reference/mish.cpp    | 94 ++++++++-----------
 1 file changed, 37 insertions(+), 57 deletions(-)

diff --git a/src/plugins/template/tests/functional/op_reference/mish.cpp b/src/plugins/template/tests/functional/op_reference/mish.cpp
index 18c48df95c2dcf..405ca06d4e2b14 100644
--- a/src/plugins/template/tests/functional/op_reference/mish.cpp
+++ b/src/plugins/template/tests/functional/op_reference/mish.cpp
@@ -6,6 +6,7 @@
 
 #include <gtest/gtest.h>
 
+#include <cmath>
 #include <random>
 
 #include "base_reference_test.hpp"
@@ -16,24 +17,22 @@ using namespace ov;
 namespace {
 struct MishParams {
     template <class IT>
-    MishParams(const ov::PartialShape& dynamicShape,
-               const ov::Shape& inputShape,
+    MishParams(const ov::PartialShape& parameterShape,
+               const ov::Shape& tensorShape,
                const ov::element::Type& iType,
                const std::vector<IT>& iValues,
                const std::vector<IT>& oValues,
                const std::string& test_name = "")
-        : dynamicShape(dynamicShape),
-          inputShape(inputShape),
+        : parameterShape(parameterShape),
+          tensorShape(tensorShape),
           inType(iType),
-          outType(iType),
-          inputData(CreateTensor(inputShape, iType, iValues)),
-          refData(CreateTensor(inputShape, iType, oValues)),
+          inputData(CreateTensor(tensorShape, iType, iValues)),
+          refData(CreateTensor(tensorShape, iType, oValues)),
           testcaseName(test_name) {}
 
-    ov::PartialShape dynamicShape;
-    ov::PartialShape inputShape;
+    ov::PartialShape parameterShape;
+    ov::Shape tensorShape;
     ov::element::Type inType;
-    ov::element::Type outType;
     ov::Tensor inputData;
     ov::Tensor refData;
     std::string testcaseName;
@@ -42,24 +41,19 @@ struct MishParams {
 class ReferenceMishLayerTest : public testing::TestWithParam<MishParams>, public CommonReferenceTest {
 public:
     void SetUp() override {
-        legacy_compare = true;
-        auto params = GetParam();
-        function = CreateFunction(params.dynamicShape, params.inType);
+        const auto& params = GetParam();
+        function = CreateFunction(params.parameterShape, params.inType);
         inputData = {params.inputData};
         refOutData = {params.refData};
     }
     static std::string getTestCaseName(const testing::TestParamInfo<MishParams>& obj) {
-        auto param = obj.param;
+        const auto& param = obj.param;
         std::ostringstream result;
-        result << "dShape=" << param.dynamicShape << "_";
-        result << "iShape=" << param.inputShape << "_";
-        result << "iType=" << param.inType << "_";
-        if (param.testcaseName != "") {
-            result << "oType=" << param.outType << "_";
-            result << param.testcaseName;
-        } else {
-            result << "oType=" << param.outType;
-        }
+        result << "dShape=" << param.parameterShape << "_";
+        result << "iShape=" << param.tensorShape << "_";
+        result << "iType=" << param.inType;
+        if (!param.testcaseName.empty())
+            result << "_" << param.testcaseName;
         return result.str();
     }
 
@@ -67,7 +61,7 @@ class ReferenceMishLayerTest : public testing::TestWithParam<MishParams>, public
     static std::shared_ptr<Model> CreateFunction(const PartialShape& input_shape, const element::Type& input_type) {
         const auto in = std::make_shared<op::v0::Parameter>(input_type, input_shape);
         const auto Mish = std::make_shared<op::v4::Mish>(in);
-        return std::make_shared<ov::Model>(NodeVector{Mish}, ParameterVector{in});
+        return std::make_shared<Model>(NodeVector{Mish}, ParameterVector{in});
     }
 };
 
@@ -76,13 +70,13 @@ TEST_P(ReferenceMishLayerTest, CompareWithRefs) {
 }
 
 template <element::Type_t IN_ET>
-std::vector<MishParams> generateMishFloatParams(const PartialShape& dynamicShape,
-                                                const Shape& staticShape,
-                                                const std::string& test_name = "") {
+MishParams generateMishFloatParams(const PartialShape& parameterShape,
+                                   const Shape& tensorShape,
+                                   const std::string& test_name = "") {
     using T = typename element_type_traits<IN_ET>::value_type;
 
     // generate input tensor (with possible type conversion)
-    auto staticSize = shape_size(staticShape);
+    const auto staticSize = shape_size(tensorShape);
     std::vector<T> expected;
     std::vector<T> input;
     {
@@ -90,43 +84,29 @@ std::vector<MishParams> generateMishFloatParams(const PartialShape& dynamicShape
         std::normal_distribution<> d{0.0, 20.0};
 
         for (auto i = staticSize; i > 0; i--) {
-            auto x = static_cast<T>(d(gen));
-            auto y = static_cast<T>(static_cast<double>(x) * std::tanh(std::log(1.0 + std::exp(x))));
+            const auto x = static_cast<T>(d(gen));
+            const auto y = static_cast<T>(x * std::tanh(std::log(std::exp(x) + T{1})));
             input.push_back(x);
             expected.push_back(y);
         }
     }
 
-    std::vector<MishParams> mishParams;
-
-    if (test_name != "") {
-        mishParams = {MishParams(dynamicShape, staticShape, IN_ET, input, expected, test_name)};
-    } else {
-        mishParams = {MishParams(dynamicShape, staticShape, IN_ET, input, expected)};
-    }
-    return mishParams;
+    return MishParams{parameterShape, tensorShape, IN_ET, input, expected, test_name};
 }
 
 std::vector<MishParams> generateMishCombinedParams() {
-    const std::vector<std::vector<MishParams>> mishTypeParams{
-        generateMishFloatParams<element::Type_t::f32>({2, 5}, {2, 5}),
-        generateMishFloatParams<element::Type_t::f32>({2, 3, 4, 5}, {2, 3, 4, 5}),
-        generateMishFloatParams<element::Type_t::f32>(PartialShape::dynamic(), {2, 3, 4, 5}),
-        generateMishFloatParams<element::Type_t::f32>({2, Dimension::dynamic(), 4, 5},
-                                                      {2, 3, 4, 5},
-                                                      "dimensionDynamic"),
-        generateMishFloatParams<element::Type_t::f16>({2, 5}, {2, 5}),
-        generateMishFloatParams<element::Type_t::f16>({2, 3, 4, 5}, {2, 3, 4, 5}),
-        generateMishFloatParams<element::Type_t::f16>(PartialShape::dynamic(), {2, 3, 4, 5}),
-        generateMishFloatParams<element::Type_t::f16>({2, Dimension::dynamic(), 4, 5},
-                                                      {2, 3, 4, 5},
-                                                      "dimensionDynamic")};
-    std::vector<MishParams> combinedParams;
-
-    for (const auto& params : mishTypeParams) {
-        combinedParams.insert(combinedParams.end(), params.begin(), params.end());
-    }
-    return combinedParams;
+    return std::vector<MishParams>{generateMishFloatParams<element::Type_t::f32>({2, 5}, {2, 5}),
+                                   generateMishFloatParams<element::Type_t::f32>({2, 3, 4, 5}, {2, 3, 4, 5}),
+                                   generateMishFloatParams<element::Type_t::f32>(PartialShape::dynamic(), {2, 3, 4, 5}),
+                                   generateMishFloatParams<element::Type_t::f32>({2, Dimension::dynamic(), 4, 5},
+                                                                                 {2, 3, 4, 5},
+                                                                                 "dimensionDynamic"),
+                                   generateMishFloatParams<element::Type_t::f16>({2, 5}, {2, 5}),
+                                   generateMishFloatParams<element::Type_t::f16>({2, 3, 4, 5}, {2, 3, 4, 5}),
+                                   generateMishFloatParams<element::Type_t::f16>(PartialShape::dynamic(), {2, 3, 4, 5}),
+                                   generateMishFloatParams<element::Type_t::f16>({2, Dimension::dynamic(), 4, 5},
+                                                                                 {2, 3, 4, 5},
+                                                                                 "dimensionDynamic")};
 }
 
 INSTANTIATE_TEST_SUITE_P(smoke_Mish_With_Hardcoded_Refs,

From ec9b970ac53dd97b441fa4aa023d9889adbf344f Mon Sep 17 00:00:00 2001
From: David Nam <david.nam@intel.com>
Date: Fri, 20 Sep 2024 18:27:20 +0800
Subject: [PATCH 26/49] Remove bucketize dynamic single layer test (#26694)

### Details:
 - Remove bucketize dynamic single layer test
- This is regarding #26292 which is for static case and currently
doesn't need dynamic test case.

### Tickets:
 - 152487
---
 .../single_layer_tests/dynamic/bucketize.cpp  | 154 ------------------
 1 file changed, 154 deletions(-)
 delete mode 100644 src/plugins/intel_gpu/tests/functional/single_layer_tests/dynamic/bucketize.cpp

diff --git a/src/plugins/intel_gpu/tests/functional/single_layer_tests/dynamic/bucketize.cpp b/src/plugins/intel_gpu/tests/functional/single_layer_tests/dynamic/bucketize.cpp
deleted file mode 100644
index d65f4ea51c9f1d..00000000000000
--- a/src/plugins/intel_gpu/tests/functional/single_layer_tests/dynamic/bucketize.cpp
+++ /dev/null
@@ -1,154 +0,0 @@
-// Copyright (C) 2018-2022 Intel Corporation
-// SPDX-License-Identifier: Apache-2.0
-//
-#include <random>
-
-#include "common_test_utils/ov_tensor_utils.hpp"
-#include "shared_test_classes/base/ov_subgraph.hpp"
-
-#include "openvino/op/parameter.hpp"
-#include "openvino/op/result.hpp"
-#include "openvino/op/bucketize.hpp"
-
-namespace {
-using ov::test::InputShape;
-
-typedef std::tuple<InputShape,        // input shape
-                   InputShape,        // boundary shape
-                   bool,              // with_right_bound
-                   ov::element::Type, // input type
-                   ov::element::Type, // boundary type
-                   ov::element::Type, // output type
-                   std::string
-> BucketizeLayerParamsSet;
-
-class BucketizeLayerDynamicTest : public testing::WithParamInterface<BucketizeLayerParamsSet>,
-                              virtual public ov::test::SubgraphBaseTest {
-public:
-    static std::string getTestCaseName(testing::TestParamInfo<BucketizeLayerParamsSet> obj) {
-        InputShape shapes;
-        InputShape boundary;
-        bool with_right_boundary;
-        ov::element::Type input_type;
-        ov::element::Type boundary_type;
-        ov::element::Type output_type;
-        std::string deviceName;
-        std::tie(shapes, boundary, with_right_boundary, input_type, boundary_type, output_type, deviceName) = obj.param;
-
-        std::ostringstream results;
-        results << "IS=" << ov::test::utils::partialShape2str({shapes.first}) << "_";
-        results << "TS=";
-        for (const auto& item : shapes.second) {
-            results << ov::test::utils::vec2str(item) << "_";
-        }
-        for (const auto& item : boundary.second) {
-            results << ov::test::utils::vec2str(item) << "_";
-        }
-        results << "withRightBoundary=" << with_right_boundary << "_";
-        results << "netPRC=" << input_type << "_";
-        results << "boundPRC=" << boundary_type << "_";
-        results << "outPRC=" << output_type << "_";
-
-        return results.str();
-    }
-
-    void generate_inputs(const std::vector<ov::Shape>& targetInputStaticShapes) override {
-        inputs.clear();
-        const auto& func_inputs = function->inputs();
-        ov::Tensor tensor;
-        for (size_t i = 0; i < func_inputs.size(); ++i) {
-            const auto& func_input = func_inputs[i];
-            tensor = ov::test::utils::create_and_fill_tensor(func_input.get_element_type(), targetInputStaticShapes[i]);
-            inputs.insert({func_input.get_node_shared_ptr(), tensor});
-        }
-        inferRequestNum++;
-    }
-
-protected:
-    size_t inferRequestNum = 0;
-
-    void SetUp() override {
-        InputShape shapes;
-        InputShape boundary;
-        bool with_right_boundary;
-        ov::element::Type input_type;
-        ov::element::Type boundary_type;
-        ov::element::Type output_type;
-        std::string deviceName;
-        std::tie(shapes, boundary, with_right_boundary, input_type, boundary_type, output_type, deviceName) = this->GetParam();
-
-        targetDevice = ov::test::utils::DEVICE_GPU;
-
-        std::vector<InputShape> input_shapes;
-        input_shapes.push_back(shapes);
-        input_shapes.push_back(boundary);
-
-        init_input_shapes(input_shapes);
-
-        ov::ParameterVector params{std::make_shared<ov::op::v0::Parameter>(input_type, inputDynamicShapes.front()),
-                                   std::make_shared<ov::op::v0::Parameter>(boundary_type, inputDynamicShapes.back())};
-
-        auto bucketize = std::make_shared<ov::op::v3::Bucketize>(params[0], params[1], output_type, with_right_boundary);
-
-        ov::ResultVector results;
-        for (size_t i = 0; i < bucketize->get_output_size(); i++) {
-            results.push_back(std::make_shared<ov::op::v0::Result>(bucketize->output(i)));
-        }
-
-        function = std::make_shared<ov::Model>(results, params, "BucketizeFuncTest");
-    }
-};
-
-TEST_P(BucketizeLayerDynamicTest, inference) {
-    run();
-}
-
-const std::vector<InputShape> input_shapes = {
-        {{-1, -1}, {{2, 3}, {3, 4}}}
-};
-
-const std::vector<InputShape> boundary_shapes = {
-        {{-1}, {{1}, {0}}}
-};
-
-const std::vector<bool> with_right_boundary = {
-    true,
-    false
-};
-
-const std::vector<ov::element::Type> input_precisions = {
-    ov::element::f16,
-    ov::element::f32,
-    ov::element::i64,
-    ov::element::i32,
-    ov::element::i8,
-    ov::element::u8
-};
-
-const std::vector<ov::element::Type> boundary_precisions = {
-    ov::element::f16,
-    ov::element::f32,
-    ov::element::i64,
-    ov::element::i32,
-    ov::element::i8,
-    ov::element::u8
-};
-
-const std::vector<ov::element::Type> output_precisions = {
-    ov::element::i64,
-    ov::element::i32
-};
-
-INSTANTIATE_TEST_SUITE_P(smoke_bucketize_dynamic,
-    BucketizeLayerDynamicTest,
-    ::testing::Combine(
-        ::testing::ValuesIn(input_shapes),
-        ::testing::ValuesIn(boundary_shapes),
-        ::testing::ValuesIn(with_right_boundary),
-        ::testing::ValuesIn(input_precisions),
-        ::testing::ValuesIn(boundary_precisions),
-        ::testing::ValuesIn(output_precisions),
-        ::testing::Values(ov::test::utils::DEVICE_GPU)),
-    BucketizeLayerDynamicTest::getTestCaseName);
-
-} // namespace
\ No newline at end of file

From 6cf90f203225828c421166f871ae0e0ac892d795 Mon Sep 17 00:00:00 2001
From: Anastasia Kuporosova <anastasia.kuporosova@intel.com>
Date: Fri, 20 Sep 2024 13:10:14 +0200
Subject: [PATCH 27/49] Bump pytest-timeout (#26676)

### Details:
- resolve pip conflict from
https://github.com/openvinotoolkit/openvino/pull/26651

### Tickets:
 - *ticket-id*
---
 src/bindings/python/constraints.txt | 2 +-
 tests/constraints.txt               | 2 +-
 tests/e2e_tests/requirements.txt    | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/src/bindings/python/constraints.txt b/src/bindings/python/constraints.txt
index 6b5f5bd7190f2f..2f91d0a4a8b597 100644
--- a/src/bindings/python/constraints.txt
+++ b/src/bindings/python/constraints.txt
@@ -5,7 +5,7 @@ numpy>=1.16.6,<2.1.0  # Python bindings, frontends
 pytest>=5.0,<8.4
 pytest-dependency==0.6.0
 pytest-html==4.1.1
-pytest-timeout==2.2.0
+pytest-timeout==2.3.1
 
 # Python bindings
 py>=1.9.0
diff --git a/tests/constraints.txt b/tests/constraints.txt
index eadf73ecc02aaf..e7a6a2bff46f7f 100644
--- a/tests/constraints.txt
+++ b/tests/constraints.txt
@@ -21,7 +21,7 @@ py>=1.9.0
 pytest>=5.0,<8.4
 pytest-dependency==0.5.1
 pytest-html==4.1.1
-pytest-timeout==2.2.0
+pytest-timeout==2.3.1
 jax<=0.4.33
 jaxlib<=0.4.33
 kornia==0.7.0
diff --git a/tests/e2e_tests/requirements.txt b/tests/e2e_tests/requirements.txt
index 0f33b0c4603f41..a4716e496470f4 100644
--- a/tests/e2e_tests/requirements.txt
+++ b/tests/e2e_tests/requirements.txt
@@ -27,7 +27,7 @@ pytest-html
 pytest-json-report==1.5.0
 # pytest-metadata==1.7.0
 pytest-xdist==2.1.0
-pytest-timeout==2.2.0
+pytest-timeout==2.3.1
 
 # for common utils, e2e_tests
 openvino-dev

From 7cf05641b6b1b249904c96e02ac07ee384219bb4 Mon Sep 17 00:00:00 2001
From: Anastasia Kuporosova <anastasia.kuporosova@intel.com>
Date: Fri, 20 Sep 2024 13:13:39 +0200
Subject: [PATCH 28/49] [Docs] Add ov.experimental to docs (#26677) (#26685)

### Details:
 - cherry-pick https://github.com/openvinotoolkit/openvino/pull/26677

### Tickets:
 - *ticket-id*
---
 docs/sphinx_setup/api/ie_python_api/api.rst | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/docs/sphinx_setup/api/ie_python_api/api.rst b/docs/sphinx_setup/api/ie_python_api/api.rst
index 29665c35141bc3..efbe25ee40bcde 100644
--- a/docs/sphinx_setup/api/ie_python_api/api.rst
+++ b/docs/sphinx_setup/api/ie_python_api/api.rst
@@ -190,3 +190,9 @@ OpenVINO Python API
    :template: custom-module-template.rst
 
    openvino.frontend
+
+.. autosummary::
+   :toctree: _autosummary
+   :template: custom-module-template.rst
+
+   openvino.experimental

From 2fa4c61834647a34c03369f397aa4ff637de01f2 Mon Sep 17 00:00:00 2001
From: Aleksandr Voron <aleksandr.voron@intel.com>
Date: Fri, 20 Sep 2024 15:20:05 +0200
Subject: [PATCH 29/49] [CPU][ARM] Do not run SLT Deconvolution tests with
 blocked layout on ARM (#26653)

### Details:
- There were 2 SLT Deconvolution tests
(`smoke_arm_Deconv_2D_AutoPadding_FP16`,
`smoke_arm_Deconv_2D_AutoPadding_FP32`) that test blocked layouts on
ARM.
 - ARM does not support blocked layout, so these tests were removed
- It was verified that removed tests are already presented in `x64`
directory, so no need to add these tests in `x64`.
- It was discussed with @allnes that tests with planar layout are
splited as well, @allnes is going to verify the possibility to move
planar tests into `common` directory. Planar tests refactoring is not
part of this PR.

### Tickets:
 - *ticket-id*
---
 .../arm/convolution_backprop_data.cpp         | 44 -------------------
 1 file changed, 44 deletions(-)

diff --git a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/arm/convolution_backprop_data.cpp b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/arm/convolution_backprop_data.cpp
index 86f80bfb3c755d..6229f9a30d3c45 100755
--- a/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/arm/convolution_backprop_data.cpp
+++ b/src/plugins/intel_cpu/tests/functional/custom/single_layer_tests/instances/arm/convolution_backprop_data.cpp
@@ -81,48 +81,4 @@ INSTANTIATE_TEST_SUITE_P(nightly_arm_Deconv_2D_Planar_FP32,
                                             ::testing::ValuesIn(filterCPUInfo({conv_gemm_2D, conv_gemm_acl_2D, conv_gemm_acl_2D_nspc})),
                                             ::testing::Values(empty_plugin_config)),
                          DeconvolutionLayerCPUTest::getTestCaseName);
-
-/* ============= Deconvolution auto padding tests ============= */
-const std::vector<DeconvInputData> inputs_2D_AutoPadding = {
-        DeconvInputData{InputShape{{}, {{2, 67, 7, 7}}}, ov::test::utils::InputLayerType::CONSTANT, {}},
-        DeconvInputData{InputShape{{-1, 67, -1, -1}, {{1, 67, 9, 4}, {2, 67, 5, 7}, {1, 67, 9, 4}}},
-                        ov::test::utils::InputLayerType::CONSTANT,
-                        {}},
-        DeconvInputData{InputShape{{-1, 67, -1, -1}, {{2, 67, 7, 7}, {2, 67, 5, 7}, {1, 67, 9, 4}}},
-                        ov::test::utils::InputLayerType::CONSTANT,
-                        {}},
-        DeconvInputData{InputShape{{-1, 67, -1, -1}, {{1, 67, 9, 4}, {2, 67, 5, 7}, {1, 67, 9, 4}}},
-                        ov::test::utils::InputLayerType::PARAMETER,
-                        {}}};
-
-const auto deconvParams_AutoPadding_2D =
-        ::testing::Combine(::testing::ValuesIn(kernels2d),
-                           ::testing::ValuesIn(strides2d),
-                           ::testing::ValuesIn(padBegins2d),
-                           ::testing::ValuesIn(padEnds2d),
-                           ::testing::ValuesIn(dilations2d),
-                           ::testing::ValuesIn(numOutChannels_Blocked),
-                           ::testing::Values(ov::op::PadType::SAME_UPPER, ov::op::PadType::SAME_LOWER),
-                           ::testing::ValuesIn(emptyOutputPadding));
-
-INSTANTIATE_TEST_SUITE_P(smoke_arm_Deconv_2D_AutoPadding_FP16,
-                         DeconvolutionLayerCPUTest,
-                         ::testing::Combine(deconvParams_AutoPadding_2D,
-                                            ::testing::ValuesIn(inputs_2D_AutoPadding),
-                                            ::testing::Values(ElementType::f16),
-                                            ::testing::Values(emptyFusingSpec),
-                                            ::testing::ValuesIn(filterCPUInfo({conv_gemm_2D, conv_gemm_acl_2D, conv_gemm_acl_2D_nspc, conv_avx512_2D})),
-                                            ::testing::Values(cpu_f16_plugin_config)),
-                         DeconvolutionLayerCPUTest::getTestCaseName);
-
-INSTANTIATE_TEST_SUITE_P(smoke_arm_Deconv_2D_AutoPadding_FP32,
-                         DeconvolutionLayerCPUTest,
-                         ::testing::Combine(deconvParams_AutoPadding_2D,
-                                            ::testing::ValuesIn(inputs_2D_AutoPadding),
-                                            ::testing::Values(ElementType::f32),
-                                            ::testing::Values(emptyFusingSpec),
-                                            ::testing::ValuesIn(filterCPUInfo({conv_gemm_2D, conv_gemm_acl_2D, conv_gemm_acl_2D_nspc, conv_avx512_2D})),
-                                            ::testing::Values(empty_plugin_config)),
-                         DeconvolutionLayerCPUTest::getTestCaseName);
-
 } // namespace

From f99b3276ad75e5763704b195d01fb52084b02968 Mon Sep 17 00:00:00 2001
From: Roman Lyamin <Roman.Lyamin@intel.com>
Date: Fri, 20 Sep 2024 17:32:21 +0400
Subject: [PATCH 30/49] [GPU] Allowed fusing i8/u8 fc with eltwise in dynamic
 case (#26646)

### Details:
- *When creating an unfused subgraph, old weights dependencies are used,
which leads to name conflicts*
- *Using unfuse logic leads to significant performance drop, added
ability to use `fully_connected_gpu_imad` kernel for eltwise fusing*
---
 .../intel_gpu/src/graph/primitive_inst.cpp    |  5 ++-
 .../fusions/fully_connected_fusion_test.cpp   | 42 +++++++++++++++++++
 2 files changed, 45 insertions(+), 2 deletions(-)

diff --git a/src/plugins/intel_gpu/src/graph/primitive_inst.cpp b/src/plugins/intel_gpu/src/graph/primitive_inst.cpp
index 00c321e1f1db51..d69a2767c018f6 100644
--- a/src/plugins/intel_gpu/src/graph/primitive_inst.cpp
+++ b/src/plugins/intel_gpu/src/graph/primitive_inst.cpp
@@ -2406,8 +2406,9 @@ bool primitive_inst::is_valid_fusion() const {
         // TODO: Only fc_bf_tiled_kernel & ref kernel are verified for fused eltwise. To support more fc kernels for eltwise fusion
         if (!_node->get_selected_impl())
             return false;
-        if ((_node->get_selected_impl()->get_kernel_name().find("fully_connected_gpu_bf_tiled") == std::string::npos)
-            && (_node->get_selected_impl()->get_kernel_name().find("fully_connected_gpu_bfyx_ref") == std::string::npos)) {
+        if (!data_type_traits::is_i8_u8(_node->get_input_layout(0).data_type) &&
+            (_node->get_selected_impl()->get_kernel_name().find("fully_connected_gpu_bf_tiled") == std::string::npos) &&
+            (_node->get_selected_impl()->get_kernel_name().find("fully_connected_gpu_bfyx_ref") == std::string::npos)) {
             return false;
         }
     }
diff --git a/src/plugins/intel_gpu/tests/unit/fusions/fully_connected_fusion_test.cpp b/src/plugins/intel_gpu/tests/unit/fusions/fully_connected_fusion_test.cpp
index 4a1133ab00507f..8bdaaaacb95d7f 100644
--- a/src/plugins/intel_gpu/tests/unit/fusions/fully_connected_fusion_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/fusions/fully_connected_fusion_test.cpp
@@ -899,3 +899,45 @@ INSTANTIATE_TEST_SUITE_P(fusings_gpu, fc_fp16_eltwise_add_ocl_dynamic, ::testing
     fully_connected_test_params{ DYN_CASE_FC_FP16_3D_1, 2, 3 },
     fully_connected_test_params{ DYN_CASE_FC_FP16_3D_2, 2, 3 },
 }));
+
+class fc_imad_int8_eltwise_add_ocl_dynamic : public FullyConnectedFusingTest {
+public:
+    void run_test() {
+        auto p = GetParam();
+        auto test_input_layout = get_input_layout(p);
+
+        auto dyn_input_pshape = ov::PartialShape::dynamic(test_input_layout.get_partial_shape().size());
+        dyn_input_pshape[p.in_shape.size() - 1] = p.in_shape[p.in_shape.size() - 1];
+        auto dynamic_input_layout = layout{dyn_input_pshape, test_input_layout.data_type, test_input_layout.format};
+
+        auto eltwise_data_shape = p.out_shape.size() == 3 ? ov::PartialShape{1, 1, p.out_shape[2]} : ov::PartialShape{1, p.out_shape[1]};
+        auto eltwise_data_layout = layout{eltwise_data_shape, p.default_type, p.default_format};
+
+        create_topologies(
+            input_layout("input", dynamic_input_layout),
+            data("weights", get_mem(get_weights_layout(p))),
+            data("bias", get_mem(get_bias_layout(p))),
+            data("eltwise_data", get_mem(eltwise_data_layout, 1, 9)),
+            fully_connected("fc_prim", input_info("input"), "weights", "bias", get_output_dim_size(p)),
+            eltwise("eltwise", { input_info("fc_prim"), input_info("eltwise_data") }, eltwise_mode::sum),
+            reorder("reorder_bfyx", input_info("eltwise"), p.default_format, data_types::f32)
+        );
+
+        ov::intel_gpu::ImplementationDesc fc_impl = { p.input_format, "fully_connected_gpu_imad", impl_types::ocl };
+        cfg_fused.set_property(ov::intel_gpu::force_implementations(ov::intel_gpu::ImplForcingMap{ { "fc_prim", fc_impl } }));
+
+        tolerance = default_tolerance(p.data_type);
+        execute(p, true);
+    }
+};
+
+TEST_P(fc_imad_int8_eltwise_add_ocl_dynamic, basic) {
+    if (engine.get_device_info().supports_immad)
+        return;
+    run_test();
+}
+
+INSTANTIATE_TEST_SUITE_P(fusings_gpu, fc_imad_int8_eltwise_add_ocl_dynamic, ::testing::ValuesIn(std::vector<fully_connected_test_params>{
+    fully_connected_test_params{ CASE_FC_U8S8_1, 2, 3 },
+    fully_connected_test_params{ CASE_FC_U8S8_4, 2, 3 },
+}));

From 435153940f0202ba260f1b58d469ca3d754a360f Mon Sep 17 00:00:00 2001
From: Alexandra Sidorova <alexandra.sidorova@intel.com>
Date: Fri, 20 Sep 2024 17:42:54 +0400
Subject: [PATCH 31/49] [Snippets] Fixed the pass ExtractLoopInvariants
 (#26590)

### Details:
- *The pass `ExtractLoopInvariants` cannot extract loop invariant from
the outermost or any intermediate loops which have inner loops. This PR
adds check that `inner_loop_id == potential_expr->get_loop_ids().back`*
 - *Added snippets test*

### Tickets:
 - *152426*
---
 .../lowered/pass/extract_loop_invariants.cpp  | 11 ++++--
 .../pass/extracted_loop_invariants.cpp        | 34 +++++++++++++++++++
 2 files changed, 43 insertions(+), 2 deletions(-)

diff --git a/src/common/snippets/src/lowered/pass/extract_loop_invariants.cpp b/src/common/snippets/src/lowered/pass/extract_loop_invariants.cpp
index f20ace893df463..27f4713810bfe0 100644
--- a/src/common/snippets/src/lowered/pass/extract_loop_invariants.cpp
+++ b/src/common/snippets/src/lowered/pass/extract_loop_invariants.cpp
@@ -61,7 +61,14 @@ int64_t get_stride_after_move_outer(const LoopPort& loop_port) {
     }
 }
 
-bool is_extraction_applicable(const ExpressionPtr& expr, const UnifiedLoopInfoPtr& inner_loop_info) {
+bool is_extraction_applicable(const ExpressionPtr& expr, const UnifiedLoopInfoPtr& inner_loop_info, size_t loop_id) {
+    // Extraction is possible only from the innermost Loop!
+    // We cannot extract Expression from the outermost or any intermediate Loop with other Loops inside
+    const auto& loop_ids = expr->get_loop_ids();
+    OPENVINO_ASSERT(!loop_ids.empty(), "Expression must be in a Loop");
+    if (loop_ids.back() != loop_id)
+        return false;
+
     const auto& expr_input_ports = expr->get_input_ports();
     const auto& input_port_size = expr_input_ports.size();
     if (input_port_size == 0)
@@ -162,7 +169,7 @@ bool extract_from_loop(const size_t& inner_loop_id, LinearIR& linear_ir) {
         const auto& potential_extractable_exprs = get_loop_input_exprs(inner_loop_input_ports);
         bool expr_extracted = false;
         for (const auto& port_expr : potential_extractable_exprs) {
-            if (is_extraction_applicable(port_expr, inner_loop_info)) {
+            if (is_extraction_applicable(port_expr, inner_loop_info, inner_loop_id)) {
                 status = true;
                 LinearIR::constExprIt inner_loop_begin_pos, inner_loop_end_pos;
                 std::tie(inner_loop_begin_pos, inner_loop_end_pos) = loop_manager->get_loop_bounds(linear_ir, inner_loop_id);
diff --git a/src/common/snippets/tests/src/lowered/pass/extracted_loop_invariants.cpp b/src/common/snippets/tests/src/lowered/pass/extracted_loop_invariants.cpp
index 3e148d3c1cf329..ee76c5af7234d8 100644
--- a/src/common/snippets/tests/src/lowered/pass/extracted_loop_invariants.cpp
+++ b/src/common/snippets/tests/src/lowered/pass/extracted_loop_invariants.cpp
@@ -288,6 +288,40 @@ TEST_F(ExtractLoopInvariantsTest, ExtractedLoopInvariantsFromInnermostToLoopOuts
     }
 }
 
+TEST_F(ExtractLoopInvariantsTest, ExtractedLoopInvariantsImpossible) {
+    const auto input_precision = ov::element::f32;
+    const ov::Shape input_shape_0{32, 8, 1};
+    ov::snippets::VectorDims order{1, 2, 0};
+    ov::snippets::VectorDims layout{0, 1, 2};
+    ov::snippets::VectorDims subtensor{1, 1};
+    /* 
+     *  < Transpose decomposition >
+     *
+     *        Param0(32,8,1)
+     *             |
+     *       LoadReshape with order (1,2,0)
+     *             |
+     *           Store
+     *             |
+     *           Result
+    */
+    {
+        auto param = linear_ir->push_node<ov::opset10::Parameter>(input_precision, input_shape_0);
+        auto load_reshape = linear_ir->push_node<ov::snippets::op::LoadReshape>(param.second, 1, 0, layout);
+        auto store = linear_ir->push_node<ov::snippets::op::Store>(load_reshape.second, 1, 0);
+        init_expr_descriptors(*load_reshape.first, {subtensor, subtensor}, {order, layout});
+        init_expr_descriptors(*store.first, {subtensor, subtensor}, {layout, layout});
+        auto result = linear_ir->push_node<ov::opset10::Result>(store.second);
+        linear_ir->get_loop_manager()->mark_loop(load_reshape.first, result.first, 32, 1,
+                                                 std::vector<LoopPort>{LoopPort((*load_reshape.first)->get_input_port(0), true, 0)},
+                                                 std::vector<LoopPort>{LoopPort((*store.first)->get_output_port(0), true, 0)});
+        linear_ir->get_loop_manager()->mark_loop(load_reshape.first, result.first, 1, 1,
+                                                 std::vector<LoopPort>{LoopPort((*load_reshape.first)->get_input_port(0), true, 1)},
+                                                 std::vector<LoopPort>{LoopPort((*store.first)->get_output_port(0), true, 1)});
+        linear_ir->set_loop_depth(2);
+    }
+}
+
 TEST_F(ExtractLoopInvariantsTest, ExtractedLoopInvariantsSplitLoops) {
     size_t vector_size = 16;
     size_t block_size = 32;

From 501a37a2a06582d087b29fb0f288f9bfc7cc3f4a Mon Sep 17 00:00:00 2001
From: Damian Kurek <damian.kurek@intel.com>
Date: Fri, 20 Sep 2024 15:54:27 +0200
Subject: [PATCH 32/49] [GPU] Further optimize softmax bf (#26697)

### Details:
 - Very significant softmax bf performance improvement

### Tickets:
 - 152926
---
 .../cl_kernels/softmax_gpu_bf.cl              | 118 +++++++-----------
 .../kernels/softmax/softmax_kernel_base.cpp   |   1 -
 .../kernels/softmax/softmax_kernel_base.h     |   1 -
 .../kernels/softmax/softmax_kernel_bf.cpp     |   3 -
 4 files changed, 48 insertions(+), 75 deletions(-)

diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/softmax_gpu_bf.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/softmax_gpu_bf.cl
index 51698079847a12..496bcd704a0c94 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/softmax_gpu_bf.cl
+++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/softmax_gpu_bf.cl
@@ -83,8 +83,6 @@ KERNEL (softmax_gpu_continuous_bfyx)(
     INPUT0_TYPE my_sum = UNIT_VAL_ZERO;
     INPUT0_TYPE my_maximum = -UNIT_VAL_MAX;
 
-    __local INPUT0_TYPE lg_storage[SLM_SIZE];
-
     // Read inputs and Get maximum value from data set
     uint input_idx=0;
 
@@ -156,28 +154,17 @@ KERNEL (softmax_gpu_continuous_bfyx)(
         my_maximum = max(my_maximum, tmp);
     }
 
-    my_maximum = sub_group_reduce_max(my_maximum);
-
-    if (get_sub_group_local_id() == 0)
-        lg_storage[get_sub_group_id()] = my_maximum;
-
-    barrier(CLK_LOCAL_MEM_FENCE);
-    if (in_data_set_idx == 0)
-    {
-        for (uint j=1; j<get_num_sub_groups(); ++j)
-            my_maximum = max(my_maximum, lg_storage[j]);
-
-        lg_storage[0] = my_maximum;
-    }
-    barrier(CLK_LOCAL_MEM_FENCE);
-
-    //my_maximum from this point is in fact global maximum
-    my_maximum = lg_storage[0];
+#if !IS_DYNAMIC
+    #if LWS == SUB_GROUP_SIZE
+        my_maximum = sub_group_reduce_max(my_maximum);
+    #else
+        my_maximum = work_group_reduce_max(my_maximum);
+    #endif
+#else
+    my_maximum = work_group_reduce_max(my_maximum);
+#endif
 
-    // Get exp(x-max) and sum of exp(x-max)
-    barrier(CLK_LOCAL_MEM_FENCE);
-
-    const uint num_iters = input_idx;
+    uint num_iters = items_num;
 
     for (uint j=0; j<num_iters; ++j)
     {
@@ -186,22 +173,31 @@ KERNEL (softmax_gpu_continuous_bfyx)(
         my_chunk[j] = tmp;
     }
 
-    my_sum = sub_group_reduce_add(my_sum);
+    if (in_data_set_idx < aligned_offset)
+    {
+        INPUT0_TYPE tmp = native_exp(my_chunk[num_iters] - my_maximum);
+        my_sum += tmp;
+        my_chunk[num_iters] = tmp;
 
-    if (get_sub_group_local_id() == 0)
-        lg_storage[get_sub_group_id()] = my_sum;
+        num_iters++;
+    }
 
-    barrier(CLK_LOCAL_MEM_FENCE);
-    if (in_data_set_idx == 0)
+    if (in_data_set_idx < actual_leftovers)
     {
-        for (uint j=1; j<get_num_sub_groups(); ++j)
-            my_sum += lg_storage[j];
-
-        lg_storage[0] = my_sum;
+        INPUT0_TYPE tmp = native_exp(my_chunk[num_iters] - my_maximum);
+        my_sum += tmp;
+        my_chunk[num_iters] = tmp;
     }
-    barrier(CLK_LOCAL_MEM_FENCE);
 
-    my_sum = lg_storage[0];
+#if !IS_DYNAMIC
+    #if LWS == SUB_GROUP_SIZE
+        my_sum = sub_group_reduce_add(my_sum);
+    #else
+        my_sum = work_group_reduce_add(my_sum);
+    #endif
+#else
+    my_sum = work_group_reduce_add(my_sum);
+#endif
 
     // Write outputs
     uint output_idx = 0;
@@ -366,26 +362,15 @@ KERNEL (softmax_gpu_continuous_bfyx)(
         my_maximum = max(my_maximum, tmp);
     }
 
-    my_maximum = sub_group_reduce_max(my_maximum);
-
-    if (get_sub_group_local_id() == 0)
-        lg_storage[get_sub_group_id()] = my_maximum;
-
-    barrier(CLK_LOCAL_MEM_FENCE);
-    if (in_data_set_idx == 0)
-    {
-        for (uint j=1; j<get_num_sub_groups(); ++j)
-            my_maximum = max(my_maximum, lg_storage[j]);
-
-        lg_storage[0] = my_maximum;
-    }
-    barrier(CLK_LOCAL_MEM_FENCE);
-
-    //my_maximum from this point is in fact global maximum
-    my_maximum = lg_storage[0];
-
-    // Get exp(x-max) and sum of exp(x-max)
-    barrier(CLK_LOCAL_MEM_FENCE);
+#if !IS_DYNAMIC
+    #if LWS == SUB_GROUP_SIZE
+        my_maximum = sub_group_reduce_max(my_maximum);
+    #else
+        my_maximum = work_group_reduce_max(my_maximum);
+    #endif
+#else
+    my_maximum = work_group_reduce_max(my_maximum);
+#endif
 
     const uint num_iters = input_idx;
 
@@ -407,22 +392,15 @@ KERNEL (softmax_gpu_continuous_bfyx)(
         output[leftover_idx] = tmp;
     }
 
-    my_sum = sub_group_reduce_add(my_sum);
-
-    if (get_sub_group_local_id() == 0)
-        lg_storage[get_sub_group_id()] = my_sum;
-
-    barrier(CLK_LOCAL_MEM_FENCE);
-    if (in_data_set_idx == 0)
-    {
-        for (uint j=1; j<get_num_sub_groups(); ++j)
-            my_sum += lg_storage[j];
-
-        lg_storage[0] = my_sum;
-    }
-    barrier(CLK_LOCAL_MEM_FENCE);
-
-    my_sum = lg_storage[0];
+#if !IS_DYNAMIC
+    #if LWS == SUB_GROUP_SIZE
+        my_sum = sub_group_reduce_add(my_sum);
+    #else
+        my_sum = work_group_reduce_add(my_sum);
+    #endif
+#else
+    my_sum = work_group_reduce_add(my_sum);
+#endif
 
     // Write outputs
     uint output_idx = 0;
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/softmax/softmax_kernel_base.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/softmax/softmax_kernel_base.cpp
index c2179ef551c507..38c0fc2eeca589 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/softmax/softmax_kernel_base.cpp
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/softmax/softmax_kernel_base.cpp
@@ -30,7 +30,6 @@ SoftmaxKernelBase::DispatchData SoftmaxKernelBase::SetDefault(const softmax_para
     dispatchData.normIndex = 0;
     dispatchData.dataSetsCount = 0;
     dispatchData.dataSetSize = 0;
-    dispatchData.maxSlmSize = 0;
 
     return dispatchData;
 }
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/softmax/softmax_kernel_base.h b/src/plugins/intel_gpu/src/kernel_selector/kernels/softmax/softmax_kernel_base.h
index 35b4feb242643c..87038772f04c12 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/softmax/softmax_kernel_base.h
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/softmax/softmax_kernel_base.h
@@ -36,7 +36,6 @@ class SoftmaxKernelBase : public KernelBaseOpenCL {
         size_t leftovers;
         size_t dataSetsCount;
         size_t dataSetSize;
-        size_t maxSlmSize;
         size_t normIndex;  // which dimension (from in-memory representation) is normalized, e.g. for bfyx and
                            // softmax::normalize_f, it will be f's index == 2 (used only by naive kernel)
         size_t subgroupBlockSize;
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/softmax/softmax_kernel_bf.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/softmax/softmax_kernel_bf.cpp
index fc1ffac2380e55..57b5bed3842d9d 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/softmax/softmax_kernel_bf.cpp
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/softmax/softmax_kernel_bf.cpp
@@ -45,7 +45,6 @@ SoftmaxKernel_bf::Parent::DispatchData SoftmaxKernel_bf::SetDefault(const softma
     auto local_mem_per_wi = 2 * BytesPerElement(params.inputs[0].GetDType());
     // Combining device execution and local memory restrictions to compute maximum possible LWS.
     auto max_lws = std::min(params.engineInfo.maxWorkGroupSize, params.engineInfo.maxLocalMemSize / local_mem_per_wi);
-    dispatchData.maxSlmSize = max_lws;
     if (!params.has_dynamic_tensors()) {
         // start with 1 thread per data set
         dispatchData.gws[0] = 1;
@@ -138,7 +137,6 @@ JitConstants SoftmaxKernel_bf::GetJitConstants(const softmax_params& params, Dis
         constexpr size_t stack_size = 34; // The size of stack for my_chunk
         jit.AddConstants({
             MakeJitConstant("LWS", lws_0),
-            MakeJitConstant("SLM_SIZE", dispatchData.maxSlmSize),
             MakeJitConstant("DATA_SETS_COUNT", data_set_count),
             MakeJitConstant("DATA_SET_SIZE", data_set_size),
             MakeJitConstant("STACK_SIZE", stack_size),
@@ -147,7 +145,6 @@ JitConstants SoftmaxKernel_bf::GetJitConstants(const softmax_params& params, Dis
         jit.AddConstants({
             MakeJitConstant("ITEMS_NUM", dispatchData.itemsNum),
             MakeJitConstant("LWS", dispatchData.lws[0]),
-            MakeJitConstant("SLM_SIZE", dispatchData.lws[0]),
             MakeJitConstant("DATA_SETS_COUNT", dispatchData.dataSetsCount),
             MakeJitConstant("DATA_SET_SIZE", dispatchData.dataSetSize),
             MakeJitConstant("STACK_SIZE", dispatchData.itemsNum + 2), // (aligned offset + leftovers)

From ffe347a07100ec951c409cf83bc6c897587418b7 Mon Sep 17 00:00:00 2001
From: Wilson Seok <wilson.seok@intel.com>
Date: Fri, 20 Sep 2024 08:18:53 -0700
Subject: [PATCH 33/49] [GPU] Check data_size string in rms opt kernel to avoid
 std::stoi() exception (#26703)

### Details:
- Check data_size string in rms opt kernel to avoid std::stoi()
exception

### Tickets:
 - 152574
---
 .../kernels/rms/rms_kernel_bfyx_opt.cpp       | 10 ++++-
 .../tests/unit/test_cases/rms_gpu_test.cpp    | 45 +++++++++++++++++++
 2 files changed, 53 insertions(+), 2 deletions(-)

diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/rms/rms_kernel_bfyx_opt.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/rms/rms_kernel_bfyx_opt.cpp
index 88d27789e7ce89..9f57d8a78121a6 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/rms/rms_kernel_bfyx_opt.cpp
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/rms/rms_kernel_bfyx_opt.cpp
@@ -74,12 +74,18 @@ JitConstants RMSKernelBfyxOpt::GetJitConstants(const rms_params& params, Dispatc
         }
 
         const std::string lws_0 = "get_local_size(0)";
-        auto item_num_and_lws = get_item_num_and_lws(params, stoi(data_size));
+        // data_size string starts digit when it has static dim.
+        bool is_static_data_size = std::isdigit(data_size[0]);
+        size_t stack_size = 33;
+        if (is_static_data_size) {
+            auto item_num_and_lws = get_item_num_and_lws(params, stoi(data_size));
+            stack_size = cldnn::ceil_div(std::stoi(data_size), item_num_and_lws.second);
+        }
         jit.AddConstants({
             MakeJitConstant("DATA_SIZE", data_size),
             MakeJitConstant("LWS", lws_0),
             MakeJitConstant("SLM_SIZE", dispatchData.maxSlmSize),
-            MakeJitConstant("STACK_SIZE", cldnn::ceil_div(std::stoi(data_size), item_num_and_lws.second))
+            MakeJitConstant("STACK_SIZE", stack_size)
         });
     } else {
         jit.AddConstants({
diff --git a/src/plugins/intel_gpu/tests/unit/test_cases/rms_gpu_test.cpp b/src/plugins/intel_gpu/tests/unit/test_cases/rms_gpu_test.cpp
index 15dfa2398c3a27..32b63657b6c027 100644
--- a/src/plugins/intel_gpu/tests/unit/test_cases/rms_gpu_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/test_cases/rms_gpu_test.cpp
@@ -228,6 +228,51 @@ TEST(rms_gpu_test, rms_test_bfyx_opt_dyn) {
     }
 }
 
+TEST(rms_gpu_test, rms_test_bfyx_opt_all_dims_dyn) {
+    auto& engine = get_test_engine();
+
+    auto input_layout_dynamic = layout{ov::PartialShape{ov::Dimension::dynamic(), ov::Dimension::dynamic(), ov::Dimension::dynamic()},
+                                       data_types::f32, format::bfyx};
+    auto input = engine.allocate_memory({ov::PartialShape{2, 1, 4096}, data_types::f32, format::bfyx});
+    auto gamma = engine.allocate_memory({ov::PartialShape{1, 1, 4096}, data_types::f32, format::bfyx});
+    auto output_ref = engine.allocate_memory({ov::PartialShape{2, 1, 4096}, data_types::f32, format::bfyx});
+
+    tests::set_random_values<float>(input, true, 8, 100);
+    tests::set_random_values<float>(gamma, true, 8, 100);
+
+    rms_ref<float>(input, gamma, output_ref, 1e-5f);
+
+    topology topology;
+    topology.add(input_layout("input", input_layout_dynamic));
+    topology.add(input_layout("gamma", gamma->get_layout()));
+    topology.add(rms("rms", input_info("input"), input_info("gamma"), 1e-5f));
+
+    ExecutionConfig config = get_test_default_config(engine);
+    config.set_property(ov::intel_gpu::allow_new_shape_infer(true));
+
+    network network(engine, topology, config);
+
+    network.set_input_data("input", input);
+    network.set_input_data("gamma", gamma);
+
+    auto inst = network.get_primitive("rms");
+    auto impl = inst->get_impl();
+    ASSERT_TRUE(impl != nullptr);
+    ASSERT_TRUE(impl->is_dynamic());
+
+    auto outputs = network.execute();
+    ASSERT_EQ(outputs.size(), size_t(1));
+    ASSERT_EQ(outputs.begin()->first, "rms");
+
+    auto output = outputs.begin()->second.get_memory();
+    cldnn::mem_lock<float> output_ptr(output, get_test_stream());
+    cldnn::mem_lock<float> output_ref_ptr(output_ref, get_test_stream());
+
+    for (unsigned int i = 0; i < output_ref->count(); ++i) {
+        EXPECT_NEAR(output_ptr[i], output_ref_ptr[i], 1e-3);
+    }
+}
+
 TEST(rms_gpu_test, rms_test_bfyx_opt_leftovers_dyn) {
     auto& engine = get_test_engine();
 

From 423ebd2acb69c675553501a1240e18c675ad91a7 Mon Sep 17 00:00:00 2001
From: Roman Kazantsev <roman.kazantsev@intel.com>
Date: Fri, 20 Sep 2024 19:41:24 +0400
Subject: [PATCH 34/49] [TFLite FE] Clarify public dev API documentation for
 TFLite Delegate integration (#26688)

**Details:** Clarify public dev API documentation for TFLite Delegate
integration. It gives clarity upon important aspects for using TFL FE
API.

**Ticket:** TBD

---------

Signed-off-by: Kazantsev, Roman <roman.kazantsev@intel.com>
---
 .../frontend/tensorflow_lite/decoder.hpp      | 24 +++++++++++++++++++
 .../tensorflow_lite/graph_iterator.hpp        | 11 +++++++++
 2 files changed, 35 insertions(+)

diff --git a/src/frontends/tensorflow_lite/include/openvino/frontend/tensorflow_lite/decoder.hpp b/src/frontends/tensorflow_lite/include/openvino/frontend/tensorflow_lite/decoder.hpp
index d77e243d9c855b..b3415cf288c4be 100644
--- a/src/frontends/tensorflow_lite/include/openvino/frontend/tensorflow_lite/decoder.hpp
+++ b/src/frontends/tensorflow_lite/include/openvino/frontend/tensorflow_lite/decoder.hpp
@@ -30,21 +30,39 @@ class DecoderBase : public ov::frontend::DecoderBase {};
 class DecoderBaseOperation : public ov::frontend::tensorflow_lite::DecoderBase {
 public:
     /// \brief Get input tensor name by index
+    /// Operation nodes are connected between each other by tensors.
+    /// Each tensor must have unique name in a graph.
+    /// The tensor name uniqueness is provided by developer during GraphIterator construction.
+    /// This method returns tensor name that comes to this operation node by input index idx
+    /// If idx is out-of-range, it throws std::exception inherited exception
     virtual std::string get_input_tensor_name(size_t idx) const = 0;
 
     /// \brief Get input tensor type by index
+    /// If idx is out-of-range, it throws std::exception inherited exception
     virtual ov::element::Type get_input_tensor_type(size_t idx) const = 0;
 
     /// \brief Get output tensor name by index
+    /// Operation nodes are connected between each other by tensors.
+    /// Each tensor must have unique name in a graph.
+    /// The tensor name uniqueness is provided by developer during GraphIterator construction.
+    /// This method returns tensor name that outputs by output index idx from this operation
+    /// If idx is out-of-range, it throws std::exception inherited exception
     virtual std::string get_output_tensor_name(size_t idx) const = 0;
 
     /// \brief Get output tensor type by index
+    /// If idx is out-of-range, it throws std::exception inherited exception
     virtual ov::element::Type get_output_tensor_type(size_t idx) const = 0;
 
     /// \brief Get input tensor info
+    /// returns TensorInfo by input idx index that corresponds to a tensor
+    /// (it can correspond to Constant, Parameter or  intermediate tensor connecting a producer and this current node)
+    /// If idx is out-of-range, it throws std::exception inherited exception
     virtual TensorMetaInfo get_input_tensor_info(size_t idx) const = 0;
 
     /// \brief Get output tensor info
+    /// returns TensorInfo by output idx index that corresponds to a tensor
+    /// (it can correspond to intermediate tensor connecting this current node and a consumer)
+    /// If idx is out-of-range, it throws std::exception inherited exception
     virtual TensorMetaInfo get_output_tensor_info(size_t idx) const = 0;
 
     /// \brief Get a number of outputs
@@ -59,9 +77,15 @@ class DecoderBaseTensor : public ov::frontend::tensorflow_lite::DecoderBase {
     virtual TensorMetaInfo get_tensor_info() const = 0;
 
     /// \brief Get input index for tensor
+    /// returns index of this input in the list of inputs in the model
+    /// it must be from 0 to n-1, where n - number of inputs in the model
+    /// if it is not input, returns  -1
     virtual int64_t get_input_idx() const = 0;
 
     /// \brief Get output index for tensor
+    /// returns index of this output in the list of outputs in the model
+    /// it must be from 0 to m-1, where m - number of outputs in the model
+    /// if it is not input, returns  -1
     virtual int64_t get_output_idx() const = 0;
 };
 
diff --git a/src/frontends/tensorflow_lite/include/openvino/frontend/tensorflow_lite/graph_iterator.hpp b/src/frontends/tensorflow_lite/include/openvino/frontend/tensorflow_lite/graph_iterator.hpp
index 30c39f4db29f13..8ec2bc3f05c358 100644
--- a/src/frontends/tensorflow_lite/include/openvino/frontend/tensorflow_lite/graph_iterator.hpp
+++ b/src/frontends/tensorflow_lite/include/openvino/frontend/tensorflow_lite/graph_iterator.hpp
@@ -13,6 +13,17 @@ namespace frontend {
 namespace tensorflow_lite {
 
 /// Abstract representation for an input model graph that gives nodes in topologically sorted order
+/// It returns decoders for model inputs and outputs (DecoderBaseTensor objects) and for operation nodes
+/// (DecoderBaseOperation objects) DecoderBaseOperation objects for operation nodes must be sorted in topological order
+/// from producing nodes to consumer nodes when `get_decoder()` is called. DecoderBaseTensor objects for inputs and
+/// outputs must be returned first by `get_decoder()` method. Order of DecoderBaseTensor objects for inputs and outputs
+/// defines their order in the original model, i.e. model input index and model output index.
+/// For example, calling `get_decoder()` during iterating GraphIterator returns
+/// DecoderBaseTensor (for input 0), ..., DecoderBaseTensor (for input n-1),
+/// DecoderBaseTensor (for output 0), ..., DecoderBaseTensor (for output m-1),
+/// DecoderBaseOperation (for op 1), ..., DecoderBaseOperation (for op k),
+/// where n - number of inputs in the model, m - number of outputs in the model k - number of operation nodes.
+/// NOTE: constants are ignored and no decoder object is returned for constant.
 class GraphIterator : ::ov::RuntimeAttribute {
 public:
     using Ptr = std::shared_ptr<GraphIterator>;

From ae351c96e9e8606269f731bf5d7e845daf08534d Mon Sep 17 00:00:00 2001
From: Andrii Staikov <andrii.staikov@intel.com>
Date: Fri, 20 Sep 2024 19:12:04 +0200
Subject: [PATCH 35/49] Improve incorrect implicit conversion of Node to Output
 in KVCacheFusionMatcher  (#26635)

Improve incorrect implicit conversion of Node to Output in
KVCacheFusionMatcher

Fix utilization of implicit conversion of Node to Output for
VariadicSplit in KVCacheFusionMatcher transformation. Instead,
explicitly pass an output.

Signed-off-by: Andrii Staikov <andrii.staikov@intel.com>

Tickets:
	* CVS-152156

---------

Co-authored-by: Pawel Raasz <pawel.raasz@intel.com>
---
 .../include/openvino/core/node_output.hpp     |  3 +-
 .../transformations/indirect_kv_cache.cpp     |  2 +-
 .../transformations/kv_cache_fusion.cpp       |  4 +-
 .../transformations/kv_cache_fusion_test.cpp  | 38 +++++++++++++++++++
 4 files changed, 43 insertions(+), 4 deletions(-)

diff --git a/src/core/include/openvino/core/node_output.hpp b/src/core/include/openvino/core/node_output.hpp
index 90722efa5b57bc..879248349f9218 100644
--- a/src/core/include/openvino/core/node_output.hpp
+++ b/src/core/include/openvino/core/node_output.hpp
@@ -40,7 +40,8 @@ class OPENVINO_API Output<Node> {
     ///
     Output(const std::shared_ptr<Node>& node, size_t index);
 
-    /// \brief Constructs a Output, referencing the zeroth output of the node.
+    /// \brief Constructs a Output, referencing the default output of the node.
+    ///        If the node doesn't have a default output, an exception will be thrown.
     /// \param node A `shared_ptr` to the node for the output handle.
     template <typename T>
     Output(const std::shared_ptr<T>& node) : Output(node ? node->get_default_output() : Output<Node>()) {}
diff --git a/src/plugins/intel_gpu/src/plugin/transformations/indirect_kv_cache.cpp b/src/plugins/intel_gpu/src/plugin/transformations/indirect_kv_cache.cpp
index 429fa671fdd21a..7ecf4f28496e4d 100644
--- a/src/plugins/intel_gpu/src/plugin/transformations/indirect_kv_cache.cpp
+++ b/src/plugins/intel_gpu/src/plugin/transformations/indirect_kv_cache.cpp
@@ -74,7 +74,7 @@ IndirectGemmOpt::IndirectGemmOpt() {
         ov::replace_node(gather_node, gather_input_node);
 
         auto indirect_kv_cache = std::make_shared<op::KVCache>(gather_input_node,
-                                                               kv_cache_node->get_input_node_shared_ptr(1),
+                                                               kv_cache_node->input(1).get_source_output(),
                                                                beam_idx_node,
                                                                kv_cache_node->get_variable(),
                                                                kv_cache_node->get_concat_axis(),
diff --git a/src/plugins/intel_gpu/src/plugin/transformations/kv_cache_fusion.cpp b/src/plugins/intel_gpu/src/plugin/transformations/kv_cache_fusion.cpp
index dc5d756033a79b..b97389a7d18c76 100644
--- a/src/plugins/intel_gpu/src/plugin/transformations/kv_cache_fusion.cpp
+++ b/src/plugins/intel_gpu/src/plugin/transformations/kv_cache_fusion.cpp
@@ -84,13 +84,13 @@ KVCacheFusionMatcher::KVCacheFusionMatcher() {
 
         if (pattern_map.count(gather_past) > 0) {
             kv_cache_node = std::make_shared<op::KVCache>(pattern_map.at(gather_past).get_node_shared_ptr(),
-                                                          concat_node->get_input_node_shared_ptr(1),
+                                                          concat_node->input(1).get_source_output(),
                                                           variable,
                                                           concat_axis,
                                                           new_read_value_node->get_output_element_type(0));
         } else {
             kv_cache_node = std::make_shared<op::KVCache>(new_read_value_node,
-                                                          concat_node->get_input_node_shared_ptr(1),
+                                                          concat_node->input(1).get_source_output(),
                                                           variable,
                                                           concat_axis,
                                                           new_read_value_node->get_output_element_type(0));
diff --git a/src/plugins/intel_gpu/tests/unit/transformations/kv_cache_fusion_test.cpp b/src/plugins/intel_gpu/tests/unit/transformations/kv_cache_fusion_test.cpp
index bf07814c705911..74acecb5715d4c 100644
--- a/src/plugins/intel_gpu/tests/unit/transformations/kv_cache_fusion_test.cpp
+++ b/src/plugins/intel_gpu/tests/unit/transformations/kv_cache_fusion_test.cpp
@@ -22,6 +22,7 @@
 #include "openvino/op/parameter.hpp"
 #include "openvino/op/result.hpp"
 #include "openvino/op/sink.hpp"
+#include "openvino/op/variadic_split.hpp"
 #include "intel_gpu/op/kv_cache.hpp"
 
 using namespace testing;
@@ -78,3 +79,40 @@ TEST_F(TransformationTestsF, KVCacheFusionTest2) {
         model_ref = std::make_shared<ov::Model>(ov::ResultVector{result}, ov::ParameterVector{parameter, beam_idx});
     }
 }
+
+// Capturing a case when 1st input to Concat is a Node with multiple outputs
+TEST_F(TransformationTestsF, KVCacheFusionTest3) {
+        {
+        auto variable = std::make_shared<ov::op::util::Variable>(ov::op::util::VariableInfo{{1, 32, -1, 80}, ov::element::f32, "v0"});
+        auto past = std::make_shared<ov::op::v6::ReadValue>(variable);
+        auto parameter = std::make_shared<ov::op::v0::Parameter>(ov::element::f32, ov::PartialShape{1, 32, -1, 80});
+        auto var_split = std::make_shared<ov::op::v1::VariadicSplit>(parameter,
+                                                                     ov::op::v0::Constant::create(ov::element::i32, ov::Shape{}, {1}),
+                                                                     ov::op::v0::Constant::create(ov::element::i32, ov::Shape{1}, {32}));
+                                                                     std::cout << var_split->get_output_partial_shape(0) << std::endl;
+        auto beam_idx = std::make_shared<ov::op::v0::Parameter>(ov::element::i32, ov::PartialShape{1});
+        auto axis = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{}, 0);
+        auto gather_past = std::make_shared<ov::op::v8::Gather>(past, beam_idx, axis);
+        auto concat = std::make_shared<ov::op::v0::Concat>(ov::OutputVector{gather_past, var_split->output(0)}, 2);
+        auto present = std::make_shared<ov::op::v6::Assign>(concat, variable);
+        auto result = std::make_shared<ov::op::v0::Result>(concat);
+
+        model = std::make_shared<ov::Model>(ov::ResultVector{result}, ov::SinkVector{present}, ov::ParameterVector{parameter, beam_idx});
+        manager.register_pass<KVCacheFusion>();
+    }
+    {
+        auto variable = std::make_shared<ov::op::util::Variable>(ov::op::util::VariableInfo{{1, 32, -1, 80}, ov::element::f32, "v0"});
+        auto parameter = std::make_shared<ov::op::v0::Parameter>(ov::element::f32, ov::PartialShape{1, 32, -1, 80});
+        auto var_split = std::make_shared<ov::op::v1::VariadicSplit>(parameter,
+                                                                     ov::op::v0::Constant::create(ov::element::i32, ov::Shape{}, {1}),
+                                                                     ov::op::v0::Constant::create(ov::element::i32, ov::Shape{1}, {32}));
+        auto beam_idx = std::make_shared<ov::op::v0::Parameter>(ov::element::i32, ov::PartialShape{1});
+        auto past = std::make_shared<ov::intel_gpu::op::ReadValue>(variable);
+        auto axis = std::make_shared<ov::op::v0::Constant>(ov::element::i64, ov::Shape{}, 0);
+        auto gather_past = std::make_shared<ov::op::v8::Gather>(past, beam_idx, axis);
+        auto kv_cache = std::make_shared<ov::intel_gpu::op::KVCache>(gather_past, var_split->output(0), variable, 2, ov::element::f32);
+        auto result = std::make_shared<ov::op::v0::Result>(kv_cache);
+
+        model_ref = std::make_shared<ov::Model>(ov::ResultVector{result}, ov::ParameterVector{parameter, beam_idx});
+    }
+}

From 409cb331d15526121222ebf448c0c837942d757e Mon Sep 17 00:00:00 2001
From: Sungeun Kim <sungeun.kim@intel.com>
Date: Sat, 21 Sep 2024 02:36:48 +0900
Subject: [PATCH 36/49] [GPU] fix a bug for resample_pil_ref/sdpa_micro
 (#26506)

**resample_pil_ref**
- wrong size for internal buffer
- wrong internal buffer order

**sdpa_micro**
- add INPUTx_OFFSET for Q/K/V.


### Tickets:
 - 149933

---------

Co-authored-by: Pavel Durandin <pavel.durandin@intel.com>
---
 .../kernel_selector/cl_kernels/sdpa_micro.cl  |   6 +-
 .../resample/resample_kernel_pil_ref.cpp      | 118 +++++++++++-------
 .../tests/functional/subgraph_tests/sdpa.cpp  |  68 ++++++++++
 3 files changed, 142 insertions(+), 50 deletions(-)
 create mode 100644 src/plugins/intel_gpu/tests/functional/subgraph_tests/sdpa.cpp

diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/sdpa_micro.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/sdpa_micro.cl
index 9824c994790211..1584dffe95a3c3 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/sdpa_micro.cl
+++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/sdpa_micro.cl
@@ -183,9 +183,9 @@ KERNEL(micro_sdpa)(OPTIONAL_SHAPE_INFO_ARG
     const bool need_sum_barrier = (ugemm_vs_barrier_count == 0);
 
     /* Locate K/Q/V/A matrices within batch */
-    K += KEY_OFF(b1, (b0 / KV_GROUP_SIZE), 0, 0);
-    Q += QRY_OFF(b1, b0, 0, 0);
-    V += VAL_OFF(b1, (b0 / KV_GROUP_SIZE), 0, 0);
+    K += KEY_OFF(b1, (b0 / KV_GROUP_SIZE), 0, 0) + INPUT1_OFFSET;
+    Q += QRY_OFF(b1, b0, 0, 0) + INPUT0_OFFSET;
+    V += VAL_OFF(b1, (b0 / KV_GROUP_SIZE), 0, 0) + INPUT2_OFFSET;
     A += DST_OFF(b1, b0, 0, 0, 0);
 
     __builtin_assume_aligned(K, K_ALIGN);
diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/resample/resample_kernel_pil_ref.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/resample/resample_kernel_pil_ref.cpp
index ee1a77b42c2f2d..5bf490a83a3146 100644
--- a/src/plugins/intel_gpu/src/kernel_selector/kernels/resample/resample_kernel_pil_ref.cpp
+++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/resample/resample_kernel_pil_ref.cpp
@@ -52,18 +52,38 @@ Tensor::Dim ExtractDim(const DataTensor& tensor, InterpolateAxis axis) {
     return tensor.Extract(tensor.GetLayout(), Convert(axis), tensor.GetDims());
 }
 
+size_t getInputHorizontalSize(const resample_params& params, bool with_pad = false) {
+    auto inputHorizontalSize = ExtractDim(params.inputs[0], params.axes[eHorizontal]).v;
+    if (with_pad) {
+        inputHorizontalSize += params.pads_begin[ConvertToNCHW(params.axes[eHorizontal])];
+        inputHorizontalSize += params.pads_end[ConvertToNCHW(params.axes[eHorizontal])];
+    }
+    return inputHorizontalSize;
+}
+
+size_t getInputVerticalSize(const resample_params& params, bool with_pad = false) {
+    auto inputVerticalSize = ExtractDim(params.inputs[0], params.axes[eVertical]).v;
+    if (with_pad) {
+        inputVerticalSize += params.pads_begin[ConvertToNCHW(params.axes[eVertical])];
+        inputVerticalSize += params.pads_end[ConvertToNCHW(params.axes[eVertical])];
+    }
+    return inputVerticalSize;
+}
+
+size_t getOutputHorizontalSize(const resample_params& params) {
+    return ExtractDim(params.outputs[0], params.axes[eHorizontal]).v;
+}
+
+size_t getOutputVerticalSize(const resample_params& params) {
+    return ExtractDim(params.outputs[0], params.axes[eVertical]).v;
+}
+
 bool NeedHorizontalPass(const resample_params& params) {
-    auto inputHorizontalSize = ExtractDim(params.inputs[0], params.axes[eHorizontal]).v +
-        params.pads_begin[ConvertToNCHW(params.axes[eHorizontal])] + params.pads_end[ConvertToNCHW(params.axes[eHorizontal])];
-    auto outputHorizontalSize = ExtractDim(params.outputs[0], params.axes[eHorizontal]).v;
-    return inputHorizontalSize != outputHorizontalSize;
+    return getInputHorizontalSize(params, true) != getOutputHorizontalSize(params);
 }
 
 bool NeedVerticalPass(const resample_params& params) {
-    auto inputVerticalSize = ExtractDim(params.inputs[0], params.axes[eVertical]).v +
-        params.pads_begin[ConvertToNCHW(params.axes[eVertical])] + params.pads_end[ConvertToNCHW(params.axes[eVertical])];
-    auto outputVerticalSize = ExtractDim(params.outputs[0], params.axes[eVertical]).v;
-    return inputVerticalSize != outputVerticalSize;
+    return getInputVerticalSize(params, true) != getOutputVerticalSize(params);
 }
 
 std::size_t GetKernelsNum(const resample_params& params) {
@@ -73,10 +93,7 @@ std::size_t GetKernelsNum(const resample_params& params) {
 }
 
 std::size_t GetFirstRow(const resample_params& params) {
-    auto inputVerticalSize = ExtractDim(params.inputs[0], params.axes[eVertical]).v +
-        params.pads_begin[ConvertToNCHW(params.axes[eVertical])] + params.pads_end[ConvertToNCHW(params.axes[eVertical])];
-    auto outputVerticalSize = ExtractDim(params.outputs[0], params.axes[eVertical]).v;
-    float scale = static_cast<float>(inputVerticalSize) / outputVerticalSize;
+    float scale = static_cast<float>(getInputVerticalSize(params, true)) / getOutputVerticalSize(params);
     float filter_scale = std::max(1.f, scale);
     float support = params.resampleType == ResampleType::BILINEAR_PILLOW ? 1.f : 2.f * filter_scale;
     float center = 0.5 * scale;
@@ -85,9 +102,8 @@ std::size_t GetFirstRow(const resample_params& params) {
 }
 
 std::size_t GetLastRow(const resample_params& params) {
-    auto inputVerticalSize = ExtractDim(params.inputs[0], params.axes[eVertical]).v +
-        params.pads_begin[ConvertToNCHW(params.axes[eVertical])] + params.pads_end[ConvertToNCHW(params.axes[eVertical])];
-    auto outputVerticalSize = ExtractDim(params.outputs[0], params.axes[eVertical]).v;
+    auto inputVerticalSize = getInputVerticalSize(params, true);
+    auto outputVerticalSize = getOutputVerticalSize(params);
     float scale = static_cast<float>(inputVerticalSize) / outputVerticalSize;
     float filter_scale = std::max(1.f, scale);
     float support = params.resampleType == ResampleType::BILINEAR_PILLOW ? 1.f : 2.f * filter_scale;
@@ -179,23 +195,26 @@ ResampleKernelBase::DispatchData ResampleKernelPilRef::SetDefaultForKernel(Kerne
 static void SetKernelArguments(const resample_params& params, ResampleKernelPilRef::KernelId kernelId,
                                cldnn::arguments_desc& arguments,
                                std::vector<std::size_t>& internalBufferSizes) {
-    std::size_t out_horizontal_size = ExtractDim(params.outputs[0], params.axes[eHorizontal]).v;
-    std::size_t out_vertical_size = ExtractDim(params.outputs[0], params.axes[eVertical]).v;
-    double support = 1.0;
     /* maximum number of coeffs */
-    int ksize = static_cast<int>(ceil(support)) * 2 + 1;
     switch (kernelId) {
     case ResampleKernelPilRef::eCalcHorizontalCoefficients: {
-        arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 0}); // bounds
-        internalBufferSizes.push_back(out_horizontal_size * 2 * sizeof(int));
-        arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 1}); // coefficients
-        internalBufferSizes.push_back(out_horizontal_size * ksize * sizeof(float));
+        auto inputHorizontalSizeWithPadding = getInputHorizontalSize(params, true);
+        auto outputHorizontalSize = getOutputHorizontalSize(params);
+        float scale = static_cast<float>(inputHorizontalSizeWithPadding) / outputHorizontalSize;
+        float filter_scale = std::max(1.f, scale);
+        float support = params.resampleType == ResampleType::BILINEAR_PILLOW ? 1.f : 2.f * filter_scale;
+        int ksize = static_cast<int>(std::ceil(support)) * 2 + 1;
+
+        arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 0}); // coefficients
+        internalBufferSizes.push_back(outputHorizontalSize * ksize * sizeof(float));
+        arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 1}); // bounds
+        internalBufferSizes.push_back(outputHorizontalSize * 2 * sizeof(int));
         break;
     }
     case ResampleKernelPilRef::eResampleHorizontal: {
         arguments.push_back({ArgumentDescriptor::Types::INPUT, 0});           // input image
-        arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 0}); // bounds
-        arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 1}); // coefficients
+        arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 0}); // coefficients
+        arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 1}); // bounds
         if (NeedVerticalPass(params)) {
             arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 2}); // output
             auto intermediateBufferTensor = GetIntermediateBufferSize(params);
@@ -207,28 +226,35 @@ static void SetKernelArguments(const resample_params& params, ResampleKernelPilR
         break;
     }
     case ResampleKernelPilRef::eCalcVerticalCoefficients: {
-        internalBufferSizes.push_back(out_vertical_size * 2 * sizeof(int));
-        internalBufferSizes.push_back(out_vertical_size * ksize * sizeof(float));
+        auto inputVerticalSizeWithPadding = getInputVerticalSize(params, true);
+        auto outputVerticalSize = getOutputVerticalSize(params);
+        float scale = static_cast<float>(inputVerticalSizeWithPadding) / outputVerticalSize;
+        float filter_scale = std::max(1.f, scale);
+        float support = params.resampleType == ResampleType::BILINEAR_PILLOW ? 1.f : 2.f * filter_scale;
+        int ksize = static_cast<int>(std::ceil(support)) * 2 + 1;
+
+        internalBufferSizes.push_back(outputVerticalSize * ksize * sizeof(float)); // coefficients
+        internalBufferSizes.push_back(outputVerticalSize * 2 * sizeof(int));       // bounds
         if (NeedHorizontalPass(params)) {
-            arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 3}); // bounds
-            arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 4}); // coefficients
+            arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 3}); // coefficients
+            arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 4}); // bounds
         } else {
-            arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 0}); // bounds
-            arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 1}); // coefficients
+            arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 0}); // coefficients
+            arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 1}); // bounds
         }
         break;
     }
     case ResampleKernelPilRef::eResampleVertical: {
         if (NeedHorizontalPass(params)) {
-            arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 2});           // input image
-            arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 3}); // bounds
-            arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 4}); // coefficients
+            arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 2}); // input image
+            arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 3}); // coefficients
+            arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 4}); // bounds
             arguments.push_back({ArgumentDescriptor::Types::OUTPUT, 0}); // output
         } else {
             arguments.push_back({ArgumentDescriptor::Types::INPUT, 0});           // input image
-            arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 0}); // bounds
-            arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 1}); // coefficients
-            arguments.push_back({ArgumentDescriptor::Types::OUTPUT, 0}); // output
+            arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 0}); // coefficients
+            arguments.push_back({ArgumentDescriptor::Types::INTERNAL_BUFFER, 1}); // bounds
+            arguments.push_back({ArgumentDescriptor::Types::OUTPUT, 0});          // output
         }
         break;
     }
@@ -248,17 +274,10 @@ JitConstants ResampleKernelPilRef::GetJitConstantsForKernel(KernelId id, const r
         MakeJitConstant("STAGE_CALC_VERTICAL_COEFFICIENTS", static_cast<int>(eCalcVerticalCoefficients)),
         MakeJitConstant("STAGE_RESAMPLE_VERTICAL", static_cast<int>(eResampleVertical)),
     });
-    auto inputHorizontalSize = ExtractDim(params.inputs[0], params.axes[eHorizontal]).v;
-    auto inputHorizontalSizeWithPadding = inputHorizontalSize +
-        params.pads_begin[ConvertToNCHW(params.axes[eHorizontal])] +
-        params.pads_end[ConvertToNCHW(params.axes[eHorizontal])];
-    auto inputVerticalSize = ExtractDim(params.inputs[0], params.axes[eVertical]).v;
-    auto inputVerticalSizeWithPadding = inputVerticalSize + params.pads_begin[ConvertToNCHW(params.axes[eVertical])] +
-        params.pads_end[ConvertToNCHW(params.axes[eVertical])];
-    auto outputHorizontalSize = ExtractDim(params.outputs[0], params.axes[eHorizontal]).v;
-    auto outputVerticalSize = ExtractDim(params.outputs[0], params.axes[eVertical]).v;
     switch (id) {
         case eCalcHorizontalCoefficients: {
+            auto inputHorizontalSizeWithPadding = getInputHorizontalSize(params, true);
+            auto outputHorizontalSize = getOutputHorizontalSize(params);
             float scale = static_cast<float>(inputHorizontalSizeWithPadding) / outputHorizontalSize;
             float filter_scale = std::max(1.f, scale);
             float support = params.resampleType == ResampleType::BILINEAR_PILLOW ? 1.f : 2.f * filter_scale;
@@ -276,6 +295,8 @@ JitConstants ResampleKernelPilRef::GetJitConstantsForKernel(KernelId id, const r
             break;
         }
         case eResampleHorizontal: {
+            auto inputHorizontalSizeWithPadding = getInputHorizontalSize(params, true);
+            auto outputHorizontalSize = getOutputHorizontalSize(params);
             float scale = static_cast<float>(inputHorizontalSizeWithPadding) / outputHorizontalSize;
             float filter_scale = std::max(1.f, scale);
             float support = params.resampleType == ResampleType::BILINEAR_PILLOW ? 1.f : 2.f * filter_scale;
@@ -301,11 +322,12 @@ JitConstants ResampleKernelPilRef::GetJitConstantsForKernel(KernelId id, const r
             break;
         }
         case eCalcVerticalCoefficients: {
+            auto inputVerticalSizeWithPadding = getInputVerticalSize(params, true);
+            auto outputVerticalSize = getOutputVerticalSize(params);
             float scale = static_cast<float>(inputVerticalSizeWithPadding) / outputVerticalSize;
             float filter_scale = std::max(1.f, scale);
             float support = params.resampleType == ResampleType::BILINEAR_PILLOW ? 1.f : 2.f * filter_scale;
             int ksize = static_cast<int>(std::ceil(support)) * 2 + 1;
-            auto outputVerticalSize = ExtractDim(params.outputs[0], params.axes[eVertical]).v;
             jit_constants.AddConstants({MakeJitConstant("IN_DIM_BEGIN", 0.f),
                 MakeJitConstant("IN_DIM_END", static_cast<float>(inputVerticalSizeWithPadding)),
                 MakeJitConstant("IN_DIM_SIZE", static_cast<int>(inputVerticalSizeWithPadding)),
@@ -319,6 +341,8 @@ JitConstants ResampleKernelPilRef::GetJitConstantsForKernel(KernelId id, const r
             break;
         }
         case eResampleVertical: {
+            auto inputVerticalSizeWithPadding = getInputVerticalSize(params, true);
+            auto outputVerticalSize = getOutputVerticalSize(params);
             float scale = static_cast<float>(inputVerticalSizeWithPadding) / outputVerticalSize;
             float filter_scale = std::max(1.f, scale);
             float support = params.resampleType == ResampleType::BILINEAR_PILLOW ? 1.f : 2.f * filter_scale;
diff --git a/src/plugins/intel_gpu/tests/functional/subgraph_tests/sdpa.cpp b/src/plugins/intel_gpu/tests/functional/subgraph_tests/sdpa.cpp
new file mode 100644
index 00000000000000..676d6f4b1ada54
--- /dev/null
+++ b/src/plugins/intel_gpu/tests/functional/subgraph_tests/sdpa.cpp
@@ -0,0 +1,68 @@
+// Copyright (C) 2018-2024 Intel Corporation
+// SPDX-License-Identifier: Apache-2.0
+//
+
+#include "shared_test_classes/base/ov_subgraph.hpp"
+#include "openvino/core/coordinate_diff.hpp"
+#include "openvino/core/strides.hpp"
+
+#include "openvino/op/parameter.hpp"
+#include "openvino/op/constant.hpp"
+#include "openvino/op/result.hpp"
+#include "openvino/op/convolution.hpp"
+#include "openvino/op/group_conv.hpp"
+#include "openvino/op/multiply.hpp"
+#include "openvino/op/split.hpp"
+
+#include "openvino/opsets/opset13.hpp"
+#include "transformations/op_conversions/scaled_dot_product_attention_decomposition.hpp"
+#include "openvino/pass/manager.hpp"
+
+namespace {
+// validate the batch axis padding for sdpa_micro kernel.
+class SDPA : virtual public ov::test::SubgraphBaseStaticTest {
+protected:
+    void SetUp() override {
+        targetDevice = ov::test::utils::DEVICE_GPU;
+        auto inType = ov::element::f16;
+        ov::Shape inputShape{3, 4, 8, 16};
+        auto constant1 = ov::op::v0::Constant::create(ov::element::i32, {4}, {1, 4, 8, 16});
+        auto constant2 = ov::op::v0::Constant::create(ov::element::i32, {4}, {1, 4, 8, 16});
+        auto constant3 = ov::op::v0::Constant::create(ov::element::i32, {4}, {1, 4, 8, 16});
+        auto input = std::make_shared<ov::op::v0::Parameter>(inType, inputShape);
+        auto split_axis_op = std::make_shared<ov::op::v0::Constant>(ov::element::Type_t::i32, ov::Shape{}, std::vector<int64_t>{0});
+        auto split = std::make_shared<ov::op::v1::Split>(input, split_axis_op, 3);
+
+        auto reshape1 = std::make_shared<ov::op::v1::Reshape>(split->output(0), constant1, false);
+        auto reshape2 = std::make_shared<ov::op::v1::Reshape>(split->output(1), constant2, false);
+        auto reshape3 = std::make_shared<ov::op::v1::Reshape>(split->output(2), constant3, false);
+        auto sdpa = std::make_shared<ov::opset13::ScaledDotProductAttention>(reshape1, reshape2, reshape3, false);
+        sdpa->set_friendly_name("sdpa");
+
+        auto output = std::make_shared<ov::op::v0::Result>(sdpa->output(0));
+        function = std::make_shared<ov::Model>(ov::OutputVector{output}, ov::ParameterVector{input}, "sdpa_model");
+
+        functionRefs = function->clone();
+        ov::pass::Manager manager;
+
+        // Decompose ScaledDotProductAttention
+        manager.register_pass<ov::pass::ScaledDotProductAttentionDecomposition>();
+        manager.run_passes(functionRefs);
+
+        bool has_long_seq = inputShape[2] >= 384 || inputShape[3] >= 128;
+        if (inType == ov::element::f16) {
+            if (has_long_seq) {
+                abs_threshold = 0.025;
+                rel_threshold = 0.025;
+            } else {
+                abs_threshold = 0.005;
+                rel_threshold = 0.005;
+            }
+        }
+    }
+};
+
+TEST_F(SDPA, Inference) {
+    run();
+}
+}  // namespace

From 4758030e8e2c13383229c438aba945cdf86ad7a0 Mon Sep 17 00:00:00 2001
From: Anastasia Kuporosova <anastasia.kuporosova@intel.com>
Date: Fri, 20 Sep 2024 20:14:47 +0200
Subject: [PATCH 37/49] [PyOV] return back testing of PyAPI tests for py3.12
 (#26682)

### Details:
- problem with tests was caused due-to old pybind11 version is installed
((2.11.1-2)).

### Tickets:
 - CVS-152242
---
 .github/workflows/job_python_unit_tests.yml                     | 2 +-
 src/bindings/python/CMakeLists.txt                              | 2 +-
 .../mock_mo_frontend/mock_mo_python_api/CMakeLists.txt          | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/job_python_unit_tests.yml b/.github/workflows/job_python_unit_tests.yml
index 862ade24810e8c..4074fe53a6eab3 100644
--- a/.github/workflows/job_python_unit_tests.yml
+++ b/.github/workflows/job_python_unit_tests.yml
@@ -299,7 +299,7 @@ jobs:
           python3 ${OPENVINO_REPO}/docs/articles_en/assets/snippets/main.py
 
       - name: Python API Tests -- numpy>=2.0.0
-        if: ${{ fromJSON(inputs.affected-components).Python_API.test && inputs.python-version != '3.12' }} # Ticket: 152242
+        if: ${{ fromJSON(inputs.affected-components).Python_API.test }}
         run: |
           python3 -m pip uninstall -y numpy
           python3 -m pip install "numpy>=2.0.0,<2.1.0"
diff --git a/src/bindings/python/CMakeLists.txt b/src/bindings/python/CMakeLists.txt
index 871eed07ab17d0..a0424b38bea407 100644
--- a/src/bindings/python/CMakeLists.txt
+++ b/src/bindings/python/CMakeLists.txt
@@ -189,7 +189,7 @@ endif()
 if(CMAKE_CROSSCOMPILING)
     set(pybind11_min_version 2.12.0)
 else()
-    set(pybind11_min_version 2.10.0)
+    set(pybind11_min_version 2.12.0)
 endif()
 # search for FindPython3.cmake instead of legacy modules
 set(PYBIND11_FINDPYTHON ON)
diff --git a/tools/mo/unit_tests/mock_mo_frontend/mock_mo_python_api/CMakeLists.txt b/tools/mo/unit_tests/mock_mo_frontend/mock_mo_python_api/CMakeLists.txt
index 48ced7c862483d..1c5210cda2875a 100644
--- a/tools/mo/unit_tests/mock_mo_frontend/mock_mo_python_api/CMakeLists.txt
+++ b/tools/mo/unit_tests/mock_mo_frontend/mock_mo_python_api/CMakeLists.txt
@@ -28,7 +28,7 @@ source_group("src" FILES ${PYBIND_FE_SRC})
 if(CMAKE_CROSSCOMPILING)
     set(pybind11_min_version 2.12.0)
 else()
-    set(pybind11_min_version 2.10.0)
+    set(pybind11_min_version 2.12.0)
 endif()
 # search for FindPython3.cmake instead of legacy modules
 set(PYBIND11_FINDPYTHON ON)

From a683ae60ae2671edb0032c90e30710e385e204d3 Mon Sep 17 00:00:00 2001
From: Alexandra Sidorova <alexandra.sidorova@intel.com>
Date: Sat, 21 Sep 2024 20:28:05 +0400
Subject: [PATCH 38/49] [RISC-V][Tests] Fixed ov_cpu_unit_tests linking with
 SHL (#26721)

### Details:
 - *Fixed "ov_cpu_unit_tests" linking with SHL for RISC-V devices*
 - *Before the changes:*
 ```
[build] [100%] Linking CXX executable
/openvino/bin/riscv64/Release/ov_cpu_unit_tests
[build]
/Xuantie-900-gcc-linux-5.15.0-glibc-x86_64-V2.8.1/lib/gcc/riscv64-unknown-linux-gnu/10.4.0/../../../../riscv64-unknown-linux-gnu/bin/ld:
../../CMakeFiles/openvino_intel_cpu_plugin_obj.dir/src/nodes/executors/shl/shl_eltwise.cpp.o:(.text._ZN2ov9intel_cpu18ShlStructureTraitsIP13csinn_sessionE10destructorES3_[_ZN2ov9intel_cpu18ShlStructureTraitsIP13csinn_sessionE10destructorES3_]+0x0):
undefined reference to `csinn_free_session'
[build]
/Xuantie-900-gcc-linux-5.15.0-glibc-x86_64-V2.8.1/lib/gcc/riscv64-unknown-linux-gnu/10.4.0/../../../../riscv64-unknown-linux-gnu/bin/ld:
../../CMakeFiles/openvino_intel_cpu_plugin_obj.dir/src/nodes/executors/shl/shl_fullyconnected.cpp.o:
in function `.LEHE110':
[build]
shl_fullyconnected.cpp:(.text._ZN2ov9intel_cpu13ShlFCExecutor6updateERKSt13unordered_mapIiSt10shared_ptrINS0_7IMemoryEESt4hashIiESt8equal_toIiESaISt4pairIKiS5_EEE+0x24a):
undefined reference to `csinn_tensor_copy'
  ```
  - *After the changes:*
  ```
[build] [ 94%] Linking CXX executable
/openvino/bin/riscv64/Release/ov_cpu_unit_tests
[build] [100%] Built target ov_cpu_unit_tests
  ```

### Tickets:
 - *N/A*
---
 src/plugins/intel_cpu/tests/unit/CMakeLists.txt | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/src/plugins/intel_cpu/tests/unit/CMakeLists.txt b/src/plugins/intel_cpu/tests/unit/CMakeLists.txt
index a3cf33c347ffb8..63441b504735b0 100644
--- a/src/plugins/intel_cpu/tests/unit/CMakeLists.txt
+++ b/src/plugins/intel_cpu/tests/unit/CMakeLists.txt
@@ -34,6 +34,10 @@ else()
     set(MLAS_LIBRARY "mlas")
 endif()
 
+if (ENABLE_SHL_FOR_CPU)
+    set(SHL_LIBRARY "shl")
+endif()
+
 ov_add_test_target(
         NAME ${TARGET_NAME}
         ROOT ${CMAKE_CURRENT_SOURCE_DIR}
@@ -61,6 +65,7 @@ ov_add_test_target(
             ov_snippets_models
             snippets_test_utils
             ${MLAS_LIBRARY}
+            ${SHL_LIBRARY}
         ADD_CPPLINT
         LABELS
             OV UNIT CPU

From 5033754807132826b94cc5a753d2c3b27cf09ab2 Mon Sep 17 00:00:00 2001
From: Pawel Raasz <pawel.raasz@intel.com>
Date: Sat, 21 Sep 2024 18:28:27 +0200
Subject: [PATCH 39/49] [core] Add preprocessor symbol to enable/disable Xbyak
 JIT in core (#26712)

### Details:
- When OV built with defined symbol `OS_CHROMEOS` (defined by ChromOS
enviroment or user) the Xbyak JIT code will be disabled
- Temporary use standard implementation instead Xbyak code to enable use
OpenVINO on ChromOS.
 - Optimized version using intrinsic code in next PR.

### Tickets:
 - CVS-152707
---
 .../include/openvino/reference/convert.hpp    | 15 ++++--
 src/core/reference/src/op/convert.cpp         | 46 +++++++++----------
 2 files changed, 34 insertions(+), 27 deletions(-)

diff --git a/src/core/reference/include/openvino/reference/convert.hpp b/src/core/reference/include/openvino/reference/convert.hpp
index 867679885337f8..926af31dbf5130 100644
--- a/src/core/reference/include/openvino/reference/convert.hpp
+++ b/src/core/reference/include/openvino/reference/convert.hpp
@@ -13,6 +13,12 @@
 #include "openvino/core/type/float16.hpp"
 #include "openvino/core/type/nf4.hpp"
 
+#if !defined(OS_CHROMEOS) && (defined(OPENVINO_ARCH_X86) || defined(OPENVINO_ARCH_X86_64))
+#    define OV_CORE_USE_XBYAK_JIT 1
+#else
+#    define OV_CORE_USE_XBYAK_JIT 0
+#endif
+
 namespace ov {
 
 template <class ElementIter>
@@ -56,7 +62,7 @@ void convert(const TI* arg, TO* out, const size_t count) {
     std::transform(arg, arg + count, out, detail::convert<TI, TO>);
 }
 
-#if defined(OPENVINO_ARCH_X86) || defined(OPENVINO_ARCH_X86_64)
+#if OV_CORE_USE_XBYAK_JIT
 
 template <>
 void convert<uint8_t, float16>(const uint8_t* arg, float16* out, size_t count);
@@ -65,8 +71,6 @@ void convert<float16, float>(const float16* arg, float* out, size_t count);
 template <>
 void convert<float, float16>(const float* arg, float16* out, size_t count);
 template <>
-void convert<int32_t, float16>(const int32_t* arg, float16* out, size_t count);
-template <>
 void convert<float, int8_t>(const float* arg, int8_t* out, size_t count);
 template <>
 void convert<float16, int8_t>(const float16* arg, int8_t* out, size_t count);
@@ -75,7 +79,10 @@ void convert<bfloat16, float16>(const bfloat16* arg, float16* out, size_t count)
 template <>
 void convert<bfloat16, float>(const bfloat16* arg, float* out, size_t count);
 
-#endif  // OPENVINO_ARCH_X86 || OPENVINO_ARCH_X86_64
+#endif  // OV_CORE_USE_XBYAK_JIT
+
+template <>
+void convert<int32_t, float16>(const int32_t* arg, float16* out, size_t count);
 
 // Count how many f32 values is out of normal finite numbers range when converted to f16
 size_t count_out_of_f16_range(const float* arg, size_t count);
diff --git a/src/core/reference/src/op/convert.cpp b/src/core/reference/src/op/convert.cpp
index 856ec55a3428b2..18ad7a6f4717e5 100644
--- a/src/core/reference/src/op/convert.cpp
+++ b/src/core/reference/src/op/convert.cpp
@@ -4,15 +4,15 @@
 
 #include "openvino/reference/convert.hpp"
 
-#if defined(OPENVINO_ARCH_X86) || defined(OPENVINO_ARCH_X86_64)
+#if OV_CORE_USE_XBYAK_JIT
 #    include "jit_generator.hpp"
 
 using namespace ov::runtime;
-#endif
+#endif  // OV_CORE_USE_XBYAK_JIT
 
 namespace ov {
 namespace reference {
-#if defined(OPENVINO_ARCH_X86) || defined(OPENVINO_ARCH_X86_64)
+#if OV_CORE_USE_XBYAK_JIT
 namespace {
 template <typename src_t, typename dst_t, bool clamp = false>
 void jit_convert_vec(jit::Generator&, const Xbyak::RegExp&, const Xbyak::RegExp&);
@@ -521,19 +521,6 @@ void convert<float, float16>(const float* arg, float16* out, size_t count) {
     convert_impl(arg, out, count);
 }
 
-template <>
-void convert<int32_t, float16>(const int32_t* arg, float16* out, size_t count) {
-    for (size_t i = 0; i < count; ++i) {
-        if (arg[i] > std::numeric_limits<ov::float16>::max()) {
-            out[i] = std::numeric_limits<ov::float16>::max();
-        } else if (arg[i] < std::numeric_limits<ov::float16>::lowest()) {
-            out[i] = std::numeric_limits<ov::float16>::lowest();
-        } else {
-            out[i] = static_cast<ov::float16>(arg[i]);
-        }
-    }
-}
-
 template <>
 void convert<float, int8_t>(const float* arg, int8_t* out, size_t count) {
     convert_impl(arg, out, count);
@@ -554,10 +541,10 @@ void convert<bfloat16, float>(const bfloat16* arg, float* out, size_t count) {
     convert_impl(arg, out, count);
 }
 
-#endif  // OPENVINO_ARCH_X86 || OPENVINO_ARCH_X86_64
+#endif  // OV_CORE_USE_XBYAK_JIT
 
 void convert_from_f32_to_f16_with_clamp(const float* arg, float16* out, size_t count) {
-#if defined(OPENVINO_ARCH_X86) || defined(OPENVINO_ARCH_X86_64)
+#if OV_CORE_USE_XBYAK_JIT
     convert_impl<float, float16, true>(arg, out, count);
 #else
     // FIXME CVS-125496: duplicate and stub for ARM, provide optimized solution
@@ -570,11 +557,24 @@ void convert_from_f32_to_f16_with_clamp(const float* arg, float16* out, size_t c
             out[i] = static_cast<ov::float16>(arg[i]);
         }
     }
-#endif  // defined(OPENVINO_ARCH_X86) || defined(OPENVINO_ARCH_X86_64)
+#endif  // OV_CORE_USE_XBYAK_JIT
+}
+
+template <>
+void convert<int32_t, float16>(const int32_t* arg, float16* out, size_t count) {
+    for (size_t i = 0; i < count; ++i) {
+        if (arg[i] > std::numeric_limits<ov::float16>::max()) {
+            out[i] = std::numeric_limits<ov::float16>::max();
+        } else if (arg[i] < std::numeric_limits<ov::float16>::lowest()) {
+            out[i] = std::numeric_limits<ov::float16>::lowest();
+        } else {
+            out[i] = static_cast<ov::float16>(arg[i]);
+        }
+    }
 }
 
 void convert_from_bf16_to_f16_with_clamp(const bfloat16* arg, float16* out, size_t count) {
-#if defined(OPENVINO_ARCH_X86) || defined(OPENVINO_ARCH_X86_64)
+#if OV_CORE_USE_XBYAK_JIT
     convert_impl<bfloat16, float16, true>(arg, out, count);
 #else
     // FIXME CVS-125496: duplicate and stub for ARM, provide optimized solution
@@ -587,20 +587,20 @@ void convert_from_bf16_to_f16_with_clamp(const bfloat16* arg, float16* out, size
             out[i] = static_cast<ov::float16>(arg[i]);
         }
     }
-#endif  // defined(OPENVINO_ARCH_X86) || defined(OPENVINO_ARCH_X86_64)
+#endif  // OV_CORE_USE_XBYAK_JIT
 }
 
 size_t count_out_of_f16_range(const float* arg, size_t count) {
     size_t num_out_of_range = 0;
 
-#if defined(OPENVINO_ARCH_X86) || defined(OPENVINO_ARCH_X86_64)
+#if OV_CORE_USE_XBYAK_JIT
     auto converter = jit_count_out_of_range::get<float, float16>();
     if (converter) {
         jit_count_out_of_range::args_t args = {arg, &num_out_of_range, count};
         converter(&args);
         return num_out_of_range;
     }
-#endif
+#endif  // OV_CORE_USE_XBYAK_JIT
     for (size_t i = 0; i < count; ++i) {
         // if abs value is smaller than the smallest positive fp16, but not zero
         if (std::abs(arg[i]) < ov::float16::from_bits(0x0001) && arg[i] != 0.0f) {

From 538374f94d73a3dcd2393bfb0b0b11a9e2130472 Mon Sep 17 00:00:00 2001
From: Andrii Staikov <andrii.staikov@intel.com>
Date: Sun, 22 Sep 2024 00:21:38 +0200
Subject: [PATCH 40/49] Enable models regressed because of frontend type
 alignment changes (#26661)

Enable models regressed by frontend type alignment changes

Enable models regressed by frontend type alignment changes aeaa4f7 &
e732ade that insert additional ConvertLike operations that prevent
SDPAToPagedAttention transformation to work smoothly as expected.

Fix it by moving ConvertConvertLike after type resolution in
FrontEnd::normalize() to turn ConvertLike into Convert only after types
are resolved hence nothing prevents SDPAToPagedAttention transformation
to do its job.

Therefore, enable katuni4ka/tiny-random-chatglm2 &
katuni4ka/tiny-random-glm4 in precommit tests.

List of enabled models:
* THUDM/chatglm2-6b
* THUDM/chatglm3-6b
* katuni4ka/tiny-random-chatglm2
* katuni4ka/tiny-random-glm4

Ticket:
	* CVS-152286
---
 src/frontends/pytorch/src/frontend.cpp                      | 1 +
 .../models/hf-tiny-random-models-precommit                  | 6 +++---
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/src/frontends/pytorch/src/frontend.cpp b/src/frontends/pytorch/src/frontend.cpp
index ff32aa9aab155a..a5baa167db887f 100644
--- a/src/frontends/pytorch/src/frontend.cpp
+++ b/src/frontends/pytorch/src/frontend.cpp
@@ -317,6 +317,7 @@ void FrontEnd::normalize(const std::shared_ptr<ov::Model>& model) const {
     manager.register_pass<ov::pass::RemoveMultiSubGraphOpDanglingParamsResults>();
     manager.register_pass<ov::pass::ReverseShapeAndTypeInfer>();
     manager.register_pass<ov::pass::ResolveNameCollisions>(true);
+    manager.register_pass<ov::pass::ConvertConvertLike>();
     manager.run_passes(model);
 
     // Usually if nn.Module.forward is given as a source model for conversion, there is the first Parameter
diff --git a/tests/model_hub_tests/transformation_tests/models/hf-tiny-random-models-precommit b/tests/model_hub_tests/transformation_tests/models/hf-tiny-random-models-precommit
index 912b4d7d1517bb..c3ec331fcda0bc 100644
--- a/tests/model_hub_tests/transformation_tests/models/hf-tiny-random-models-precommit
+++ b/tests/model_hub_tests/transformation_tests/models/hf-tiny-random-models-precommit
@@ -39,8 +39,8 @@ fxmarty/really-tiny-falcon-testing,https://huggingface.co/fxmarty/really-tiny-fa
 Xenova/tiny-random-Phi3ForCausalLM,https://huggingface.co/Xenova/tiny-random-Phi3ForCausalLM
 facebook/opt-125m,https://huggingface.co/facebook/opt-125m
 facebook/opt-350m,https://huggingface.co/facebook/opt-350m
+katuni4ka/tiny-random-chatglm2,https://huggingface.co/katuni4ka/tiny-random-chatglm2
+katuni4ka/tiny-random-glm4,https://huggingface.co/katuni4ka/tiny-random-glm4
 hf-internal-testing/tiny-random-BioGptForCausalLM,https://huggingface.co/hf-internal-testing/tiny-random-BioGptForCausalLM,xfail,No ScaledDotProductAttention operation observed in the graph CVS-145820
 hf-internal-testing/tiny-random-XGLMForCausalLM,https://huggingface.co/hf-tiny-model-private/tiny-random-XGLMForCausalLM,xfail,No ScaledDotProductAttention operation observed in the graph CVS-145820
-katuni4ka/tiny-random-orion,https://huggingface.co/katuni4ka/tiny-random-orion,xfail,No ScaledDotProductAttention operation observed in the graph CVS-145820
-katuni4ka/tiny-random-chatglm2,https://huggingface.co/katuni4ka/tiny-random-chatglm2,xfail,Model references undeclared parameters: beam_idx () CVS-145820
-katuni4ka/tiny-random-glm4,https://huggingface.co/katuni4ka/tiny-random-glm4,xfail,Model references undeclared parameters beam_idx () attention_mask () CVS-145820
\ No newline at end of file
+katuni4ka/tiny-random-orion,https://huggingface.co/katuni4ka/tiny-random-orion,xfail,No ScaledDotProductAttention operation observed in the graph CVS-145820
\ No newline at end of file

From 94749db75e0d06b9691f8ab3361a0a93fe435538 Mon Sep 17 00:00:00 2001
From: Piotr Kowalczyk <piotr.kowalczyk@intel.com>
Date: Sun, 22 Sep 2024 14:14:22 +0200
Subject: [PATCH 41/49] [TF][GPU]: Enable bitwise shift test in TF. (#26194)

### Details:
 - Enabled TF tests for Bitwise shift ops on GPU

This PR depends on #26251, which adds support for int16 and uint16 data
types in elementwise shift op in gpu plugin.
Also depends on #26383, which fixes a bug related to i16 support for
elementwise ops in GPU.

---------

Co-authored-by: Roman Kazantsev <roman.kazantsev@intel.com>
---
 tests/layer_tests/tensorflow_tests/test_tf_BitwiseShift.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/tests/layer_tests/tensorflow_tests/test_tf_BitwiseShift.py b/tests/layer_tests/tensorflow_tests/test_tf_BitwiseShift.py
index cfef63bcc58f6f..ee001710bd16bc 100644
--- a/tests/layer_tests/tensorflow_tests/test_tf_BitwiseShift.py
+++ b/tests/layer_tests/tensorflow_tests/test_tf_BitwiseShift.py
@@ -68,8 +68,8 @@ def create_bitwise_net(self, x_shape, y_shape, is_y_const, input_type, op_type):
     @pytest.mark.nightly
     def test_bitwise(self, x_shape, y_shape, is_y_const, input_type, op_type, ie_device, precision, ir_version,
                      temp_dir, use_legacy_frontend):
-        if ie_device == 'GPU':
-            pytest.skip("149424: Bitwise ops are not supported on GPU")
+        if ie_device == 'GPU' and input_type in [np.uint64]:
+            pytest.skip("149424: uint64 type is not supported on GPU")
         if use_legacy_frontend:
             pytest.skip("BitwiseShift ops are supported only by new TF FE")
         self._test(*self.create_bitwise_net(x_shape=x_shape, y_shape=y_shape, is_y_const=is_y_const,

From f00ac415cf1bd3f7896076aba4f89b8fb827077e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Hubert=20B=C5=82aszczyk?=
 <56601011+hub-bla@users.noreply.github.com>
Date: Sun, 22 Sep 2024 17:25:03 +0200
Subject: [PATCH 42/49] [JAX FE]: Support jax.lax.eq and jax.lax.ne operation
 for JAX (#26719)

### Details:
 - support jax.lax.eq and jax.lax.ne operation
 - enable unit tests for new operations
### Tickets:
 - [None](https://github.com/openvinotoolkit/openvino/issues/26571)
---
 src/frontends/jax/src/op/binary_op.cpp                | 4 ++++
 src/frontends/jax/src/op_table.cpp                    | 4 ++++
 tests/layer_tests/jax_tests/test_binary_comparison.py | 6 ++++--
 3 files changed, 12 insertions(+), 2 deletions(-)

diff --git a/src/frontends/jax/src/op/binary_op.cpp b/src/frontends/jax/src/op/binary_op.cpp
index 9fd5cc12525b89..11e26166cfcf47 100644
--- a/src/frontends/jax/src/op/binary_op.cpp
+++ b/src/frontends/jax/src/op/binary_op.cpp
@@ -3,8 +3,10 @@
 //
 
 #include "openvino/frontend/jax/node_context.hpp"
+#include "openvino/op/equal.hpp"
 #include "openvino/op/greater.hpp"
 #include "openvino/op/greater_eq.hpp"
+#include "openvino/op/not_equal.hpp"
 #include "utils.hpp"
 
 using namespace std;
@@ -25,8 +27,10 @@ OutputVector translate_binary_op(const NodeContext& context) {
     return {binary_op};
 }
 
+template OutputVector translate_binary_op<v1::Equal>(const NodeContext& context);
 template OutputVector translate_binary_op<v1::GreaterEqual>(const NodeContext& context);
 template OutputVector translate_binary_op<v1::Greater>(const NodeContext& context);
+template OutputVector translate_binary_op<v1::NotEqual>(const NodeContext& context);
 
 }  // namespace op
 }  // namespace jax
diff --git a/src/frontends/jax/src/op_table.cpp b/src/frontends/jax/src/op_table.cpp
index c0bcb85c724dbb..dcd54203f5b9bc 100644
--- a/src/frontends/jax/src/op_table.cpp
+++ b/src/frontends/jax/src/op_table.cpp
@@ -6,12 +6,14 @@
 
 #include "openvino/op/add.hpp"
 #include "openvino/op/divide.hpp"
+#include "openvino/op/equal.hpp"
 #include "openvino/op/erf.hpp"
 #include "openvino/op/exp.hpp"
 #include "openvino/op/greater.hpp"
 #include "openvino/op/greater_eq.hpp"
 #include "openvino/op/maximum.hpp"
 #include "openvino/op/multiply.hpp"
+#include "openvino/op/not_equal.hpp"
 #include "openvino/op/reduce_max.hpp"
 #include "openvino/op/reduce_sum.hpp"
 #include "openvino/op/sqrt.hpp"
@@ -63,6 +65,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops_jaxpr() {
             {"device_put", op::skip_node},
             {"div", op::translate_1to1_match_2_inputs<v1::Divide>},
             {"dot_general", op::translate_dot_general},
+            {"eq", op::translate_binary_op<v1::Equal>},
             {"erf", op::translate_1to1_match_1_input<v0::Erf>},
             {"exp", op::translate_1to1_match_1_input<v0::Exp>},
             {"ge", op::translate_binary_op<v1::GreaterEqual>},
@@ -70,6 +73,7 @@ const std::map<std::string, CreatorFunction> get_supported_ops_jaxpr() {
             {"integer_pow", op::translate_integer_pow},
             {"max", op::translate_1to1_match_2_inputs<v1::Maximum>},
             {"mul", op::translate_1to1_match_2_inputs<v1::Multiply>},
+            {"ne", op::translate_binary_op<v1::NotEqual>},
             {"reduce_max", op::translate_reduce_op<v1::ReduceMax>},
             {"reduce_sum", op::translate_reduce_op<v1::ReduceSum>},
             {"reduce_window_max", op::translate_reduce_window_max},
diff --git a/tests/layer_tests/jax_tests/test_binary_comparison.py b/tests/layer_tests/jax_tests/test_binary_comparison.py
index 0a85b1e4a551c2..2e2f7d917d68a7 100644
--- a/tests/layer_tests/jax_tests/test_binary_comparison.py
+++ b/tests/layer_tests/jax_tests/test_binary_comparison.py
@@ -27,8 +27,10 @@ def _prepare_input(self):
 
     def create_model(self, input_shapes, binary_op, input_type):
         reduce_map = {
+            'eq': lax.eq,
             'ge': lax.ge,
-            'gt': lax.gt
+            'gt': lax.gt,
+            'ne': lax.ne
         }
 
         self.input_shapes = input_shapes
@@ -42,7 +44,7 @@ def jax_binary(x, y):
     @pytest.mark.parametrize('input_shapes', [[[5], [1]], [[1], [5]], [[2, 2, 4], [1, 1, 4]],
                                               [[5, 10], [5, 10]], [[2, 4, 6], [1, 4, 6]],
                                               [[5, 8, 10, 128], [5, 1, 10, 128]]])
-    @pytest.mark.parametrize('binary_op', ['ge', 'gt'])
+    @pytest.mark.parametrize('binary_op', ['eq', 'ge', 'gt', 'ne'])
     @pytest.mark.parametrize('input_type', [np.int8, np.uint8, np.int16, np.uint16,
                                             np.int32, np.uint32, np.int64, np.uint64,
                                             np.float16, np.float32, np.float64])

From c8058739aa53f0ad0c8a5804106b89a7a1f72bf6 Mon Sep 17 00:00:00 2001
From: Vladimir Paramuzov <vladimir.paramuzov@intel.com>
Date: Mon, 23 Sep 2024 10:02:40 +0400
Subject: [PATCH 43/49] [GPU] Fix lockable requirement check for shape_of
 consumer (#26723)

### Details:
- We had an exception on attempt to set remote tensor if input had
multiple GPU users and ShapeOf node with CPU impl. This patch adds
special handling for shape_of node to avoid it's requirement for
lockable inputs as it doesn't touch tensor.
- Note: Likely it will still fail for other CPU node types, so need to
investigate that further.
---
 .../intel_gpu/src/graph/program_node.cpp      |  2 +-
 .../gpu_remote_tensor_tests.cpp               | 43 +++++++++++++++++++
 2 files changed, 44 insertions(+), 1 deletion(-)

diff --git a/src/plugins/intel_gpu/src/graph/program_node.cpp b/src/plugins/intel_gpu/src/graph/program_node.cpp
index 3c21800c66d938..28b8973b108e13 100644
--- a/src/plugins/intel_gpu/src/graph/program_node.cpp
+++ b/src/plugins/intel_gpu/src/graph/program_node.cpp
@@ -528,7 +528,7 @@ bool program_node::is_fused_dep(size_t dep_idx) const {
 std::set<size_t> program_node::get_lockable_input_ids() const {
     const auto impl = get_selected_impl();
     const bool has_cpu_impl = get_preferred_impl_type() == impl_types::cpu || (impl && impl->is_cpu());
-    if (has_cpu_impl) {
+    if (has_cpu_impl && !is_type<shape_of>()) {
         std::set<size_t> dependencies_indexes;
         for (size_t i = 0; i < get_dependencies().size(); i++)
             dependencies_indexes.insert(i);
diff --git a/src/plugins/intel_gpu/tests/functional/remote_tensor_tests/gpu_remote_tensor_tests.cpp b/src/plugins/intel_gpu/tests/functional/remote_tensor_tests/gpu_remote_tensor_tests.cpp
index ee6486f432a380..baad7361425cca 100644
--- a/src/plugins/intel_gpu/tests/functional/remote_tensor_tests/gpu_remote_tensor_tests.cpp
+++ b/src/plugins/intel_gpu/tests/functional/remote_tensor_tests/gpu_remote_tensor_tests.cpp
@@ -3,6 +3,10 @@
 //
 
 #include "openvino/core/preprocess/pre_post_process.hpp"
+#include "openvino/op/add.hpp"
+#include "openvino/op/constant.hpp"
+#include "openvino/op/parameter.hpp"
+#include "openvino/op/shape_of.hpp"
 #include "openvino/runtime/intel_gpu/ocl/ocl.hpp"
 #include "openvino/runtime/intel_gpu/properties.hpp"
 #include "openvino/runtime/remote_tensor.hpp"
@@ -2616,6 +2620,45 @@ ov::RemoteTensor create_tensor(ov::intel_gpu::ocl::ClContext context,
 }
 }  // namespace
 
+TEST(RemoteTensor, smoke_LockableHandling) {
+#if defined(ANDROID)
+    GTEST_SKIP();
+#endif
+
+    auto core = ov::Core();
+    auto remote_context = core.get_default_context(ov::test::utils::DEVICE_GPU);
+    auto gpu_context = remote_context.as<ov::intel_gpu::ocl::ClContext>();
+    auto type = ov::element::f32;
+    ov::Shape shape = {4};
+
+    auto remote_tensor = gpu_context.create_tensor(type, shape);
+
+    auto host_tensor_in = ov::Tensor(type, shape);
+    init_tensor(host_tensor_in);
+    remote_tensor.copy_from(host_tensor_in);
+
+    auto param_node = std::make_shared<ov::op::v0::Parameter>(type, ov::PartialShape{-1});
+    auto const_node = std::make_shared<ov::op::v0::Constant>(host_tensor_in);
+    auto add_node = std::make_shared<ov::op::v1::Add>(param_node, const_node);
+    auto shape_of_node = std::make_shared<ov::op::v3::ShapeOf>(param_node);
+    auto res1 = std::make_shared<ov::op::v0::Result>(add_node);
+    auto res2 = std::make_shared<ov::op::v0::Result>(shape_of_node);
+    auto model = std::make_shared<ov::Model>(ov::ResultVector{res1, res2}, ov::ParameterVector{param_node});
+
+    auto compiled_model = core.compile_model(model, ov::test::utils::DEVICE_GPU, ov::hint::inference_precision(ov::element::f32));
+    auto request = compiled_model.create_infer_request();
+    request.set_input_tensor(remote_tensor);
+
+    request.infer();
+    auto res = request.get_output_tensor(0);
+    auto host_res = ov::Tensor(type, shape);
+    res.copy_to(host_res);
+
+    for (size_t i = 0; i < ov::shape_size(host_tensor_in.get_shape()); i++) {
+        ASSERT_EQ(host_res.data<float>()[i], host_tensor_in.data<float>()[i] * 2);
+    }
+}
+
 TEST_P(RemoteTensor, smoke_CopyFrom) {
 #if defined(ANDROID)
     GTEST_SKIP();

From e934d83d6030dc1ac2f5a8e5356ad7aec617808c Mon Sep 17 00:00:00 2001
From: Vladimir Paramuzov <vladimir.paramuzov@intel.com>
Date: Mon, 23 Sep 2024 10:39:21 +0400
Subject: [PATCH 44/49] [Build] Move level zero submodule from npu to common
 (#26626)

### Details:
- L0 submodule can be reused between GPU and NPU plugins, so moved to
common thirdparty dependencies
---
 .gitmodules                                   |  4 +--
 .../intel_npu/src/plugin/CMakeLists.txt       |  4 +--
 .../src/utils/src/zero/CMakeLists.txt         |  4 +--
 .../intel_npu/thirdparty/CMakeLists.txt       | 31 +++--------------
 thirdparty/dependencies.cmake                 | 10 ++++++
 thirdparty/level_zero/CMakeLists.txt          | 34 +++++++++++++++++++
 .../level_zero}/level-zero                    |  0
 7 files changed, 54 insertions(+), 33 deletions(-)
 create mode 100644 thirdparty/level_zero/CMakeLists.txt
 rename {src/plugins/intel_npu/thirdparty => thirdparty/level_zero}/level-zero (100%)

diff --git a/.gitmodules b/.gitmodules
index df0401a04ca44f..a9cad1dee5f494 100644
--- a/.gitmodules
+++ b/.gitmodules
@@ -72,8 +72,8 @@
 [submodule "src/plugins/intel_cpu/thirdparty/mlas"]
 	path = src/plugins/intel_cpu/thirdparty/mlas
 	url = https://github.com/openvinotoolkit/mlas.git
-[submodule "src/plugins/intel_npu/thirdparty/level-zero"]
-	path = src/plugins/intel_npu/thirdparty/level-zero
+[submodule "thirdparty/level_zero/level-zero"]
+	path = thirdparty/level_zero/level-zero
 	url = https://github.com/oneapi-src/level-zero.git
 [submodule "src/plugins/intel_npu/thirdparty/level-zero-ext"]
 	path = src/plugins/intel_npu/thirdparty/level-zero-ext
diff --git a/src/plugins/intel_npu/src/plugin/CMakeLists.txt b/src/plugins/intel_npu/src/plugin/CMakeLists.txt
index 5287e487bf96c8..4b91e6d594cc20 100644
--- a/src/plugins/intel_npu/src/plugin/CMakeLists.txt
+++ b/src/plugins/intel_npu/src/plugin/CMakeLists.txt
@@ -62,8 +62,8 @@ target_include_directories(${TARGET_NAME}
 )
 target_include_directories(${TARGET_NAME}
     SYSTEM PRIVATE
-        ${NPU_PLUGIN_SOURCE_DIR}/thirdparty/level-zero/include
-        ${NPU_PLUGIN_SOURCE_DIR}/thirdparty/level-zero-ext
+        $<TARGET_PROPERTY:LevelZero::LevelZero,INTERFACE_INCLUDE_DIRECTORIES>
+        $<TARGET_PROPERTY:LevelZero::NPUExt,INTERFACE_INCLUDE_DIRECTORIES>
 )
 
 if(ENABLE_AVX2)
diff --git a/src/plugins/intel_npu/src/utils/src/zero/CMakeLists.txt b/src/plugins/intel_npu/src/utils/src/zero/CMakeLists.txt
index 3b1786bd92a70d..74072d21581445 100644
--- a/src/plugins/intel_npu/src/utils/src/zero/CMakeLists.txt
+++ b/src/plugins/intel_npu/src/utils/src/zero/CMakeLists.txt
@@ -14,8 +14,8 @@ ov_add_clang_format_target(${TARGET_NAME}_clang FOR_TARGETS ${TARGET_NAME})
 target_include_directories(
   ${TARGET_NAME}
   PUBLIC
-    $<BUILD_INTERFACE:${NPU_PLUGIN_SOURCE_DIR}/thirdparty/level-zero/include>
-    $<BUILD_INTERFACE:${NPU_PLUGIN_SOURCE_DIR}/thirdparty/level-zero-ext>
+    $<TARGET_PROPERTY:LevelZero::LevelZero,INTERFACE_INCLUDE_DIRECTORIES>
+    $<TARGET_PROPERTY:LevelZero::NPUExt,INTERFACE_INCLUDE_DIRECTORIES>
     $<BUILD_INTERFACE:${NPU_UTILS_SOURCE_DIR}/include>)
 
 target_link_libraries(
diff --git a/src/plugins/intel_npu/thirdparty/CMakeLists.txt b/src/plugins/intel_npu/thirdparty/CMakeLists.txt
index c2edf548c2917e..4d0c66beeb7520 100644
--- a/src/plugins/intel_npu/thirdparty/CMakeLists.txt
+++ b/src/plugins/intel_npu/thirdparty/CMakeLists.txt
@@ -2,36 +2,13 @@
 # SPDX-License-Identifier: Apache-2.0
 #
 
-set(BUILD_SHARED_LIBS OFF)
-
 #
 # zeroApi
 #
 
 if(ENABLE_ZEROAPI_BACKEND)
-    # We have to avoid linking against loader with debug postfix due it's a part of driver
-    # and the name will be the same for release and debug configurations
-    set(CMAKE_DEBUG_POSTFIX "")
-
-    # Skip warnings as errors for thirdparty
-    if(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
-        ov_add_compiler_flags(/WX-)
-        # solve pdb access issue
-        set(USE_Z7 ON)
-        # Close spectre for ze loader
-        add_compile_options("/Qspectre-")
-    elseif(CMAKE_COMPILER_IS_GNUCXX OR OV_COMPILER_IS_CLANG)
-        ov_add_compiler_flags(-Wno-error)
-        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} \
-            -Wno-undef \
-            -Wno-missing-declarations")
-        if(UNUSED_BUT_SET_VARIABLE_SUPPORTED)
-            set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-unused-but-set-variable")
-        endif()
-    endif()
-    set(CMAKE_COMPILE_WARNING_AS_ERROR OFF)
-    add_subdirectory(level-zero EXCLUDE_FROM_ALL)
-
-    # This VERSION file created by L0 may cause compilation issue of oneTBB headers, so remove it
-    file(REMOVE "${CMAKE_BINARY_DIR}/VERSION")
+    add_library(level-zero-ext INTERFACE)
+    set_property(TARGET level-zero-ext APPEND PROPERTY INTERFACE_INCLUDE_DIRECTORIES $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/level-zero-ext/>)
+    add_library(LevelZero::NPUExt ALIAS level-zero-ext)
+    install(TARGETS level-zero-ext EXPORT "${PROJECT_NAME}Targets")
 endif()
diff --git a/thirdparty/dependencies.cmake b/thirdparty/dependencies.cmake
index e8f28deb539efb..8313ca73178283 100644
--- a/thirdparty/dependencies.cmake
+++ b/thirdparty/dependencies.cmake
@@ -64,6 +64,16 @@ if(X86_64 OR X86 OR UNIVERSAL2)
     endif()
 endif()
 
+#
+# LevelZero
+#
+
+if(ENABLE_INTEL_NPU)
+    add_subdirectory(thirdparty/level_zero EXCLUDE_FROM_ALL)
+
+    add_library(LevelZero::LevelZero ALIAS ze_loader)
+endif()
+
 #
 # OpenCL
 #
diff --git a/thirdparty/level_zero/CMakeLists.txt b/thirdparty/level_zero/CMakeLists.txt
new file mode 100644
index 00000000000000..4bd953e812f07f
--- /dev/null
+++ b/thirdparty/level_zero/CMakeLists.txt
@@ -0,0 +1,34 @@
+# Copyright (C) 2018-2024 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+#
+
+set(BUILD_SHARED_LIBS OFF)
+
+
+# We have to avoid linking against loader with debug postfix due it's a part of driver
+# and the name will be the same for release and debug configurations
+set(CMAKE_DEBUG_POSTFIX "")
+
+# Skip warnings as errors for thirdparty
+if(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC")
+    ov_add_compiler_flags(/WX-)
+    # solve pdb access issue
+    set(USE_Z7 ON)
+    # Close spectre for ze loader
+    add_compile_options("/Qspectre-")
+elseif(CMAKE_COMPILER_IS_GNUCXX OR OV_COMPILER_IS_CLANG)
+    ov_add_compiler_flags(-Wno-error)
+    set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} \
+        -Wno-undef \
+        -Wno-missing-declarations")
+    if(UNUSED_BUT_SET_VARIABLE_SUPPORTED)
+        set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -Wno-unused-but-set-variable")
+    endif()
+endif()
+set(CMAKE_COMPILE_WARNING_AS_ERROR OFF)
+add_subdirectory(level-zero EXCLUDE_FROM_ALL)
+
+set_property(TARGET ze_loader APPEND PROPERTY INTERFACE_INCLUDE_DIRECTORIES $<BUILD_INTERFACE:${CMAKE_CURRENT_SOURCE_DIR}/level-zero/include>)
+
+# This VERSION file created by L0 may cause compilation issue of oneTBB headers, so remove it
+file(REMOVE "${CMAKE_BINARY_DIR}/VERSION")
diff --git a/src/plugins/intel_npu/thirdparty/level-zero b/thirdparty/level_zero/level-zero
similarity index 100%
rename from src/plugins/intel_npu/thirdparty/level-zero
rename to thirdparty/level_zero/level-zero

From bc29a580a108aae7e255c2be60eeeeb3c7db688d Mon Sep 17 00:00:00 2001
From: Alicja Miloszewska <alicja.miloszewska@intel.com>
Date: Mon, 23 Sep 2024 09:52:21 +0200
Subject: [PATCH 45/49] [Py OV] Fix issue with passing (h, w) sequence to
 PreprocessConverter.Resize (#26687)

### Details:
- The output image size after resize should match the dimensions
specified in the sequence (h, w) passed as `size` argument. Adjustments
should be done if `size` is an int.
[docs](https://pytorch.org/vision/stable/generated/torchvision.transforms.Resize.html)
- Add more combinations of parameters to
`test_torchvision_preprocessor/test_resize`

### Tickets:
 - [CVS-149919](https://jira.devtools.intel.com/browse/CVS-149919)

---------

Co-authored-by: Anastasia Kuporosova <anastasia.kuporosova@intel.com>
---
 .../torchvision/torchvision_preprocessing.py  | 13 +++++-----
 .../test_torchvision_preprocessor.py          | 26 ++++++++++++-------
 2 files changed, 23 insertions(+), 16 deletions(-)

diff --git a/src/bindings/python/src/openvino/preprocess/torchvision/torchvision_preprocessing.py b/src/bindings/python/src/openvino/preprocess/torchvision/torchvision_preprocessing.py
index af0f5bb24b385d..f8b51afd546f57 100644
--- a/src/bindings/python/src/openvino/preprocess/torchvision/torchvision_preprocessing.py
+++ b/src/bindings/python/src/openvino/preprocess/torchvision/torchvision_preprocessing.py
@@ -294,12 +294,13 @@ def convert(self, input_idx: int, ppp: PrePostProcessor, transform: Callable, me
 
         target_h, target_w = _setup_size(transform.size, "Incorrect size type for Resize operation")
 
-        # rescale the smaller image edge
-        current_h, current_w = meta["image_dimensions"]
-        if current_h > current_w:
-            target_h = int(transform.size * (current_h / current_w))
-        elif current_w > current_h:
-            target_w = int(transform.size * (current_w / current_h))
+        if isinstance(transform.size, int):
+            # rescale the smaller image edge
+            current_h, current_w = meta["image_dimensions"]
+            if current_h > current_w:
+                target_h = int(transform.size * (current_h / current_w))
+            elif current_w > current_h:
+                target_w = int(transform.size * (current_w / current_h))
 
         ppp.input(input_idx).tensor().set_layout(Layout("NCHW"))
 
diff --git a/tests/layer_tests/py_frontend_tests/test_torchvision_preprocessor.py b/tests/layer_tests/py_frontend_tests/test_torchvision_preprocessor.py
index bfaa782ab59d7d..78bd4d526dddb3 100644
--- a/tests/layer_tests/py_frontend_tests/test_torchvision_preprocessor.py
+++ b/tests/layer_tests/py_frontend_tests/test_torchvision_preprocessor.py
@@ -87,21 +87,27 @@ def test_normalize():
     reason="Ticket: 114816",
 )
 @pytest.mark.parametrize(
-    ("target_size", "interpolation", "tolerance"),
+    ("target_size", "current_size", "interpolation", "tolerance"),
     [
-        ((220, 220, 3), transforms.InterpolationMode.NEAREST, 4e-05),
-        ((200, 240, 3), transforms.InterpolationMode.NEAREST, 0.3),  # Ticket 127670
-        ((220, 220, 3), transforms.InterpolationMode.BILINEAR, 4e-03),
-        ((200, 240, 3), transforms.InterpolationMode.BILINEAR, 4e-03),
-        ((220, 220, 3), transforms.InterpolationMode.BICUBIC, 4e-03),
-        ((200, 240, 3), transforms.InterpolationMode.BICUBIC, 4e-03),
+        (224, (220, 220, 3), transforms.InterpolationMode.NEAREST, 4e-05),
+        (224, (200, 240, 3), transforms.InterpolationMode.NEAREST, 0.3),  # Ticket 127670
+        (224, (220, 220, 3), transforms.InterpolationMode.BILINEAR, 4e-03),
+        (224, (200, 240, 3), transforms.InterpolationMode.BILINEAR, 4e-03),
+        (224, (220, 220, 3), transforms.InterpolationMode.BICUBIC, 4e-03),
+        (224, (200, 240, 3), transforms.InterpolationMode.BICUBIC, 4e-03),
+        ((224, 224), (220, 220, 3), transforms.InterpolationMode.NEAREST, 4e-05),
+        ((224, 224), (200, 240, 3), transforms.InterpolationMode.NEAREST, 4e-05),
+        ((224, 224), (220, 220, 3), transforms.InterpolationMode.BILINEAR, 4e-03),
+        ((224, 224), (200, 240, 3), transforms.InterpolationMode.BILINEAR, 4e-03),
+        ((224, 224), (220, 220, 3), transforms.InterpolationMode.BICUBIC, 4e-03),
+        ((224, 224), (200, 240, 3), transforms.InterpolationMode.BICUBIC, 4e-03),
     ],
 )
-def test_resize(target_size, interpolation, tolerance):
-    test_input = np.random.randint(255, size=target_size, dtype=np.uint8)
+def test_resize(target_size, current_size, interpolation, tolerance):
+    test_input = np.random.randint(255, size=current_size, dtype=np.uint8)
     preprocess_pipeline = transforms.Compose(
         [
-            transforms.Resize(224, interpolation=interpolation),
+            transforms.Resize(target_size, interpolation=interpolation),
             transforms.ToTensor(),
         ],
     )

From e062d5c372010c5c6188a6b1742b5622fbc276e4 Mon Sep 17 00:00:00 2001
From: Anatoliy Talamanov <anatoliy.talamanov@intel.com>
Date: Mon, 23 Sep 2024 09:53:41 +0100
Subject: [PATCH 46/49] Update GenAI NPU documentation (#26612)

### Details:
 - *item1*
 - *...*

### Tickets:
 - *ticket-id*

---------

Co-authored-by: Sebastian Golebiewski <sebastianx.golebiewski@intel.com>
Co-authored-by: Karol Blaszczak <karol.blaszczak@intel.com>
---
 .../llm_inference_guide/genai-guide-npu.rst   | 31 ++++++++++++++-----
 1 file changed, 23 insertions(+), 8 deletions(-)

diff --git a/docs/articles_en/learn-openvino/llm_inference_guide/genai-guide-npu.rst b/docs/articles_en/learn-openvino/llm_inference_guide/genai-guide-npu.rst
index 8a07b0dc05e73e..4315bc15419fc4 100644
--- a/docs/articles_en/learn-openvino/llm_inference_guide/genai-guide-npu.rst
+++ b/docs/articles_en/learn-openvino/llm_inference_guide/genai-guide-npu.rst
@@ -29,9 +29,17 @@ A chat-tuned TinyLlama model is used in this example. The following conversion &
 
    optimum-cli export openvino -m TinyLlama/TinyLlama-1.1B-Chat-v1.0 --weight-format int4 --sym --group-size 128 --ratio 1.0 TinyLlama
 
+**For models exceeding 1 billion parameters, it is recommended to use remarkably effective channel-wise quantization.** For example, you can try the approach with the llama-2-7b-chat-hf model:
+
+.. code-block:: python
+
+   optimum-cli export openvino -m meta-llama/Llama-2-7b-chat-hf --weight-format int4 --sym --group-size -1 --ratio 1.0 Llama-2-7b-chat-hf
+
 Run generation using OpenVINO GenAI
 ###################################
 
+It is recommended to install the latest available `driver <https://www.intel.com/content/www/us/en/download/794734/intel-npu-driver-windows.html>`__.
+
 Use the following code snippet to perform generation with OpenVINO GenAI API:
 
 .. tab-set::
@@ -42,6 +50,7 @@ Use the following code snippet to perform generation with OpenVINO GenAI API:
       .. code-block:: python
 
          import openvino_genai as ov_genai
+         model_path = "TinyLlama"
          pipe = ov_genai.LLMPipeline(model_path, "NPU")
          print(pipe.generate("The Sun is yellow because", max_new_tokens=100))
 
@@ -54,7 +63,7 @@ Use the following code snippet to perform generation with OpenVINO GenAI API:
          #include <iostream>
 
          int main(int argc, char* argv[]) {
-            std::string model_path = argv[1];
+            std::string model_path = "TinyLlama";
             ov::genai::LLMPipeline pipe(model_path, "NPU");
             std::cout << pipe.generate("The Sun is yellow because", ov::genai::max_new_tokens(100));
          }
@@ -62,9 +71,17 @@ Use the following code snippet to perform generation with OpenVINO GenAI API:
 Additional configuration options
 ################################
 
-Compiling models for NPU may take a while. By default, the LLMPipeline for the NPU
-is configured for faster compilation, but it may result in lower performance.
-To achieve better performance at the expense of compilation time, you may try these settings:
+Prompt and response length options
+++++++++++++++++++++++++++++++++++
+
+The LLM pipeline for NPUs leverages the static shape approach, optimizing execution performance, while potentially introducing certain usage limitations. By default, the LLM pipeline supports input prompts up to 1024 tokens in length. It also ensures that the generated response contains at least 150 tokens, unless the generation encounters the end-of-sequence (EOS) token or the user explicitly sets a lower length limit for the response.
+
+You may configure both the 'maximum input prompt length' and 'minimum response length' using the following parameters:
+
+- ``MAX_PROMPT_LEN``: Defines the maximum number of tokens that the LLM pipeline can process for the input prompt (default: 1024).
+- ``MIN_RESPONSE_LEN``: Defines the minimum number of tokens that the LLM pipeline will generate in its response (default: 150).
+
+Use the following code snippet to change the default settings:
 
 .. tab-set::
 
@@ -73,8 +90,7 @@ To achieve better performance at the expense of compilation time, you may try th
 
       .. code-block:: python
 
-         plugin_config = { "NPU_COMPILATION_MODE_PARAMS": "compute-layers-with-higher-precision=Sqrt,Power,ReduceMean,Add_RMSNorm" }
-         pipeline_config = { "PREFILL_CONFIG": plugin_config, "GENERATE_CONFIG": plugin_config }
+         pipeline_config = { "MAX_PROMPT_LEN": 1500, "MIN_RESPONSE_LEN": 500 }
          pipe = ov_genai.LLMPipeline(model_path, "NPU", pipeline_config)
 
    .. tab-item:: C++
@@ -82,8 +98,7 @@ To achieve better performance at the expense of compilation time, you may try th
 
       .. code-block:: cpp
 
-         ov::AnyMap plugin_config = { { "NPU_COMPILATION_MODE_PARAMS", "compute-layers-with-higher-precision=Sqrt,Power,ReduceMean,Add_RMSNorm" } };
-         ov::AnyMap pipeline_config = { { "PREFILL_CONFIG",  plugin_config }, { "GENERATE_CONFIG", plugin_config } };
+         ov::AnyMap pipeline_config = { { "MAX_PROMPT_LEN",  1500 }, { "MIN_RESPONSE_LEN", 500 } };
          ov::genai::LLMPipeline pipe(model_path, "NPU", pipeline_config);
 
 

From b487f7faa7e2f41b4bc779f3692e8f1d5b37bb6f Mon Sep 17 00:00:00 2001
From: cecilia peng <cecilia.peng@intel.com>
Date: Mon, 23 Sep 2024 17:29:14 +0800
Subject: [PATCH 47/49] [GPU] pass::FullyConnectedConvertFusion support
 symmetric quantized FC. (#26698)

### Details:
- *pass::FullyConnectedConvertFusion support symmetric quantized FC...
This removes a bunch of type-converting Reorder nodes that follows
FullyConnectedCompress node in QWen decoder layers.*

### Tickets:
 - *143169*
---
 .../transformations/fc_convert_fusion.cpp     | 23 +++++++++++++------
 1 file changed, 16 insertions(+), 7 deletions(-)

diff --git a/src/plugins/intel_gpu/src/plugin/transformations/fc_convert_fusion.cpp b/src/plugins/intel_gpu/src/plugin/transformations/fc_convert_fusion.cpp
index e5f992ad9cd8b4..00bfa55d11e9f8 100644
--- a/src/plugins/intel_gpu/src/plugin/transformations/fc_convert_fusion.cpp
+++ b/src/plugins/intel_gpu/src/plugin/transformations/fc_convert_fusion.cpp
@@ -22,7 +22,9 @@ FullyConnectedConvertFusion::FullyConnectedConvertFusion() {
     auto weights = any_input();
     auto bias = any_input();
     auto fully_connected = wrap_type<op::FullyConnected>({data, weights, bias}, consumers_count(1));
-    auto fully_connected_compressed = wrap_type<op::FullyConnectedCompressed>({data, weights, bias, any_input(), any_input()}, consumers_count(1));
+    auto fully_connected_compressed1 = wrap_type<op::FullyConnectedCompressed>({data, weights, bias, any_input()}, consumers_count(1));
+    auto fully_connected_compressed2 = wrap_type<op::FullyConnectedCompressed>({data, weights, bias, any_input(), any_input()}, consumers_count(1));
+    auto fully_connected_compressed = std::make_shared<ov::pass::pattern::op::Or>(OutputVector{fully_connected_compressed1, fully_connected_compressed2});
     auto fc = std::make_shared<ov::pass::pattern::op::Or>(OutputVector{fully_connected, fully_connected_compressed});
     auto convert = wrap_type<ov::op::v0::Convert>({fc}, type_matches(element::f32));
 
@@ -43,12 +45,19 @@ FullyConnectedConvertFusion::FullyConnectedConvertFusion() {
             new_fc = std::make_shared<op::FullyConnected>(m_data, m_weights, m_bias, output_type);
         } else {
             m_fc = pattern_map.at(fully_connected_compressed).get_node_shared_ptr();
-            new_fc = std::make_shared<op::FullyConnectedCompressed>(m_data,
-                                                                    m_weights,
-                                                                    m_bias,
-                                                                    m_fc->input_value(3),
-                                                                    m_fc->input_value(4),
-                                                                    output_type);
+            if (m_fc->input_values().size() == 4)
+                new_fc = std::make_shared<op::FullyConnectedCompressed>(m_data,
+                                                                        m_weights,
+                                                                        m_bias,
+                                                                        m_fc->input_value(3),
+                                                                        output_type);
+            else
+                new_fc = std::make_shared<op::FullyConnectedCompressed>(m_data,
+                                                                        m_weights,
+                                                                        m_bias,
+                                                                        m_fc->input_value(3),
+                                                                        m_fc->input_value(4),
+                                                                        output_type);
         }
         new_fc->set_friendly_name(m_convert->get_friendly_name());
         copy_runtime_info(m.get_matched_nodes(), new_fc);

From cd0ce9b76c4948f1736b64ad8041096b80f8c1d4 Mon Sep 17 00:00:00 2001
From: Vladimir Paramuzov <vladimir.paramuzov@intel.com>
Date: Mon, 23 Sep 2024 13:40:00 +0400
Subject: [PATCH 48/49] [PyOV] Add set_tensor binding for RemoteTensor (#26726)

### Details:
- Add binding for set_tensor with RemoteTensorWrapper arg to have a way
to set remote tensors in python API.
- This solution may be changes in the future, but currently it's
impossible to set remote tensor from python and this patch enables it.
---
 .../src/pyopenvino/core/infer_request.cpp     | 20 ++++++++++++++++++-
 1 file changed, 19 insertions(+), 1 deletion(-)

diff --git a/src/bindings/python/src/pyopenvino/core/infer_request.cpp b/src/bindings/python/src/pyopenvino/core/infer_request.cpp
index 9f572d273dc5f3..eed93db8db7ea3 100644
--- a/src/bindings/python/src/pyopenvino/core/infer_request.cpp
+++ b/src/bindings/python/src/pyopenvino/core/infer_request.cpp
@@ -10,6 +10,7 @@
 #include <string>
 
 #include "pyopenvino/core/common.hpp"
+#include "pyopenvino/core/remote_tensor.hpp"
 #include "pyopenvino/utils/utils.hpp"
 
 namespace py = pybind11;
@@ -464,12 +465,29 @@ void regclass_InferRequest(py::module m) {
         },
         R"(
             Gets output tensor of InferRequest.
-            
+
             :return: An output Tensor for the model.
                      If model has several outputs, an exception is thrown.
             :rtype: openvino.runtime.Tensor
         )");
 
+    cls.def(
+        "set_tensor",
+        [](InferRequestWrapper& self, const std::string& name, const RemoteTensorWrapper& tensor) {
+            self.m_request->set_tensor(name, tensor.tensor);
+        },
+        py::arg("name"),
+        py::arg("tensor"),
+        R"(
+            Sets input/output tensor of InferRequest.
+
+            :param name: Name of input/output tensor.
+            :type name: str
+            :param tensor: RemoteTensor object. The element_type and shape of a tensor
+                           must match the model's input/output element_type and shape.
+            :type tensor: openvino.runtime.RemoteTensor
+        )");
+
     cls.def(
         "set_tensor",
         [](InferRequestWrapper& self, const std::string& name, const ov::Tensor& tensor) {

From 8fab3c6657e538b6339f9704b17b11662bcce7c4 Mon Sep 17 00:00:00 2001
From: Andrzej Kopytko <andrzejx.kopytko@intel.com>
Date: Mon, 23 Sep 2024 11:51:11 +0200
Subject: [PATCH 49/49] [DOCS] Fixed path to banner_data json (#26738)

### Details:
 - *item1*
 - *...*

### Tickets:
 - *ticket-id*
---
 docs/sphinx_setup/_static/js/hide_banner.js | 4 +++-
 docs/sphinx_setup/_templates/baner.html     | 2 +-
 2 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/docs/sphinx_setup/_static/js/hide_banner.js b/docs/sphinx_setup/_static/js/hide_banner.js
index 2ee7a1cd992c37..8e631aeddbd309 100644
--- a/docs/sphinx_setup/_static/js/hide_banner.js
+++ b/docs/sphinx_setup/_static/js/hide_banner.js
@@ -1,6 +1,8 @@
 var cookieVersion = 0;
+
+var dataUrl = document.getElementById("hidebanner").getAttribute( "data-url" );
 window.onload = (e) => {
-  fetch('../_static/html/banner_data.json').then((data) => {
+  fetch(dataUrl).then((data) => {
     data.json().then((item) => {
       if(item != null && item.p != "" && item.version != "" && item.style != "" ) {
         cookieVersion = item.version;
diff --git a/docs/sphinx_setup/_templates/baner.html b/docs/sphinx_setup/_templates/baner.html
index 8e35e2118ddd0c..82fd55f3b114b6 100644
--- a/docs/sphinx_setup/_templates/baner.html
+++ b/docs/sphinx_setup/_templates/baner.html
@@ -1,2 +1,2 @@
 <div id="info-banner" class="transition-banner"></div>
-<script src="{{ pathto('_static/js/hide_banner.js', 1) }}"></script>
+<script src="{{ pathto('_static/js/hide_banner.js', 1) }}" id="hidebanner" data-url="{{ pathto('_static/html/banner_data.json', 1) }}"></script>