Merge pull request #21036 from fengyuentau:timvx_backend_support

dnn: TIM-VX NPU backend support

* Add TimVX NPU backend for DNN module.

* use official branch from tim-vx repo; fix detecting viv sdk

Co-authored-by: fytao <yuantao.feng@outlook.com>

3rdparty/libtim-vx/tim-vx.cmake

@@ -0,0 +1,73 @@
+set(TIMVX_COMMIT_HASH "1d9c7ab941b3d8d9c4d28d80058402725731e3d6")
+set(OCV_TIMVX_DIR "${OpenCV_BINARY_DIR}/3rdparty/libtim-vx")
+set(OCV_TIMVX_SOURCE_PATH "${OCV_TIMVX_DIR}/TIM-VX-${TIMVX_COMMIT_HASH}")
+
+# Download TIM-VX source code
+if(EXISTS "${OCV_TIMVX_SOURCE_PATH}")
+    message(STATUS "TIM-VX: Use cache of TIM-VX source code at ${OCV_TIMVX_SOURCE_PATH}")
+    set(TIMVX_FOUND ON)
+else()
+    set(OCV_TIMVX_FILENAME "${TIMVX_COMMIT_HASH}.zip")
+    set(OCV_TIMVX_URL "https://github.com/VeriSilicon/TIM-VX/archive/")
+    set(timvx_zip_md5sum 92619cc4498014ac7a09834d5e33ebd5)
+
+    ocv_download(FILENAME ${OCV_TIMVX_FILENAME}
+                 HASH ${timvx_zip_md5sum}
+                 URL "${OCV_TIMVX_URL}"
+                 DESTINATION_DIR "${OCV_TIMVX_DIR}"
+                 ID "TIM-VX"
+                 STATUS res
+                 UNPACK RELATIVE_URL)
+    if(res)
+        set(TIMVX_FOUND ON)
+        message(STATUS "TIM-VX: Source code downloaded at ${OCV_TIMVX_SOURCE_PATH}.") 
+    else()
+        set(TIMVX_FOUND OFF)
+        message(STATUS "TIM-VX: Failed to download source code from github. Turning off TIMVX_FOUND")
+        return()
+    endif()
+endif()
+
+# set VIVANTE SDK especially for x86_64 which comes along with TIM-VX source code
+if(CMAKE_SYSTEM_PROCESSOR STREQUAL x86_64)
+    set(VIVANTE_SDK_DIR "${OCV_TIMVX_SOURCE_PATH}/prebuilt-sdk/x86_64_linux")
+    message(STATUS "TIM-VX: Build from source using prebuilt x86_64 VIVANTE SDK.")
+endif()
+
+# Verify if requested VIVANTE SDK libraries are all found
+find_vivante_sdk_libs(missing ${VIVANTE_SDK_DIR})
+if(missing)
+    message(STATUS "TIM-VX: Failed to find ${missing} in ${VIVANTE_SDK_DIR}/lib. Turning off TIMVX_VIV_FOUND")
+    set(TIMVX_VIV_FOUND OFF)
+else()
+    message(STATUS "TIM-VX: dependent VIVANTE SDK libraries are found at ${VIVANTE_SDK_DIR}/lib.")
+    set(TIMVX_VIV_FOUND ON)
+endif()
+
+if(TIMVX_VIV_FOUND)
+    # vars used by TIM-VX CMake scripts
+    set(EXTERNAL_VIV_SDK "${VIVANTE_SDK_DIR}" CACHE INTERNAL "" FORCE)
+    set(VIV_SDK_DRIVER_PREFIX "lib" CACHE INTERNAL "" FORCE)
+endif()
+
+if(TIMVX_FOUND AND TIMVX_VIV_FOUND)
+    set(BUILD_TIMVX ON)
+else()
+    return()
+endif()
+
+if(BUILD_TIMVX)
+    set(HAVE_TIMVX 1)
+
+    ocv_warnings_disable(CMAKE_C_FLAGS -Wunused-parameter -Wstrict-prototypes -Wundef -Wsign-compare -Wmissing-prototypes -Wmissing-declarations -Wstrict-aliasing -Wunused-but-set-variable -Wmaybe-uninitialized -Wshadow -Wsuggest-override -Wswitch)
+    ocv_warnings_disable(CMAKE_CXX_FLAGS -Wunused-parameter -Wstrict-prototypes -Wundef -Wsign-compare -Wunused-but-set-variable -Wshadow -Wsuggest-override -Wmissing-declarations -Wswitch)
+
+    set(TIMVX_INC_DIR "${OCV_TIMVX_SOURCE_PATH}/include" CACHE INTERNAL "TIM-VX include directory")
+    if(EXISTS "${OCV_TIMVX_SOURCE_PATH}/CMakeLists.txt")
+        add_subdirectory("${OCV_TIMVX_SOURCE_PATH}" "${OCV_TIMVX_DIR}/build")
+    else()
+        message(WARNING "TIM-VX: Missing 'CMakeLists.txt' in the source code: ${OCV_TIMVX_SOURCE_PATH}")
+    endif()
+    ocv_install_target(tim-vx EXPORT OpenCVModules ARCHIVE DESTINATION ${OPENCV_3P_LIB_INSTALL_PATH} COMPONENT dev)
+    set(TIMVX_LIB "tim-vx")
+endif()

CMakeLists.txt

@@ -453,6 +453,9 @@ OCV_OPTION(WITH_TENGINE "Include Arm Inference Tengine support" OFF
 OCV_OPTION(WITH_ONNX "Include Microsoft ONNX Runtime support" OFF
   VISIBLE_IF TRUE
   VERIFY HAVE_ONNX)
+OCV_OPTION(WITH_TIMVX "Include Tim-VX support" OFF
+  VISIBLE_IF TRUE
+  VERIFY HAVE_TIMVX)
 
 # OpenCV build components
 # ===================================================
@@ -733,6 +736,9 @@ include(cmake/OpenCVFindProtobuf.cmake)
 if(WITH_TENGINE)
   include(cmake/OpenCVFindTengine.cmake)
 endif()
+if(WITH_TIMVX)
+  include(cmake/OpenCVFindTIMVX.cmake)
+endif()
 
 # ----------------------------------------------------------------------------
 #  Detect other 3rd-party libraries/tools
@@ -1645,6 +1651,16 @@ if(WITH_WEBNN OR HAVE_WEBNN)
   endif()
 endif()
 
+if(WITH_TIMVX)
+  status("")
+  status("  Tim-VX:"     HAVE_TIMVX THEN "YES" ELSE "NO")
+  if(HAVE_TIMVX)
+    status("    Include path"  TIMVX_INCLUDE_DIR THEN "${TIMVX_INCLUDE_DIR}" ELSE "NO")
+    status("    Link libraries:" TIMVX_LIBRARY THEN "${TIMVX_LIBRARY}" ELSE "NO")
+    status("    VIVANTE SDK path" VIVANTE_SDK_DIR THEN "${VIVANTE_SDK_DIR}" ELSE "NO")
+  endif()
+endif()
+
 if(WITH_OPENCL OR HAVE_OPENCL)
   ocv_build_features_string(opencl_features
     IF HAVE_OPENCL_SVM THEN "SVM"

cmake/OpenCVFindTIMVX.cmake

@@ -0,0 +1,69 @@
+set(TIMVX_INSTALL_DIR "" CACHE PATH "Path to libtim-vx installation")
+set(VIVANTE_SDK_DIR "" CACHE PATH "Path to VIVANTE SDK needed by TIM-VX.")
+set(VIVANTE_SDK_LIB_CANDIDATES "OpenVX;VSC;GAL;ArchModelSw;NNArchPerf" CACHE STRING "VIVANTE SDK library candidates")
+
+# Ensure VIVANTE SDK library candidates are present in given search path
+function(find_vivante_sdk_libs _viv_notfound _viv_search_path)
+    foreach(one ${VIVANTE_SDK_LIB_CANDIDATES})
+        #NO_DEFAULT_PATH is used to ensure VIVANTE SDK libs are from one only source
+        find_library(VIV_${one}_LIB ${one} PATHS "${_viv_search_path}/lib" NO_DEFAULT_PATH)
+        if(NOT VIV_${one}_LIB)
+            list(APPEND _viv_notfound_list ${one})
+        endif()
+    endforeach()
+    set(${_viv_notfound} ${_viv_notfound_list} PARENT_SCOPE)
+endfunction()
+# Default value for VIVANTE_SDK_DIR: /usr
+if(NOT VIVANTE_SDK_DIR)
+    set(VIVANTE_SDK_DIR "/usr")
+endif()
+# Environment variable VIVANTE_SDK_DIR overrides the one in this script
+if(DEFINED ENV{VIVANTE_SDK_DIR})
+    set(VIVANTE_SDK_DIR $ENV{VIVANTE_SDK_DIR})
+    message(STATUS "TIM-VX: Load VIVANTE_SDK_DIR from system environment: ${VIVANTE_SDK_DIR}")
+endif()
+
+
+# Compile with pre-installed TIM-VX; Or compile together with TIM-VX from source
+if(TIMVX_INSTALL_DIR AND NOT BUILD_TIMVX)
+    message(STATUS "TIM-VX: Use binaries at ${TIMVX_INSTALL_DIR}")
+    set(BUILD_TIMVX OFF)
+
+    set(TIMVX_INC_DIR "${TIMVX_INSTALL_DIR}/include" CACHE INTERNAL "TIM-VX include directory")
+    find_library(TIMVX_LIB "tim-vx" PATHS "${TIMVX_INSTALL_DIR}/lib")
+    if(TIMVX_LIB)
+        set(TIMVX_FOUND ON)
+    else()
+        set(TIMVX_FOUND OFF)
+    endif()
+
+    # Verify if requested VIVANTE SDK libraries are all found
+    find_vivante_sdk_libs(missing ${VIVANTE_SDK_DIR})
+    if(missing)
+        message(STATUS "TIM-VX: Failed to find ${missing} in ${VIVANTE_SDK_DIR}/lib. Turning off TIMVX_VIV_FOUND")
+        set(TIMVX_VIV_FOUND OFF)
+    else()
+        message(STATUS "TIM-VX: dependent VIVANTE SDK libraries are found at ${VIVANTE_SDK_DIR}/lib.")
+        set(TIMVX_VIV_FOUND ON)
+    endif()
+else()
+    message(STATUS "TIM-VX: Build from source")
+    include("${OpenCV_SOURCE_DIR}/3rdparty/libtim-vx/tim-vx.cmake")
+endif()
+
+if(TIMVX_FOUND AND TIMVX_VIV_FOUND)
+    set(HAVE_TIMVX 1)
+
+    message(STATUS "TIM-VX: Found TIM-VX includes: ${TIMVX_INC_DIR}")
+    message(STATUS "TIM-VX: Found TIM-VX library: ${TIMVX_LIB}")
+    set(TIMVX_LIBRARY   ${TIMVX_LIB})
+    set(TIMVX_INCLUDE_DIR   ${TIMVX_INC_DIR})
+
+    message(STATUS "TIM-VX: Found VIVANTE SDK libraries: ${VIVANTE_SDK_DIR}/lib")
+    link_directories(${VIVANTE_SDK_DIR}/lib)
+endif()
+
+MARK_AS_ADVANCED(
+	TIMVX_INC_DIR
+	TIMVX_LIB
+)

modules/dnn/CMakeLists.txt

@@ -23,6 +23,10 @@ if(WITH_WEBNN AND HAVE_WEBNN)
   add_definitions(-DHAVE_WEBNN=1)
 endif()
 
+if(HAVE_TIMVX)
+  add_definitions(-DHAVE_TIMVX=1)
+endif()
+
 ocv_option(OPENCV_DNN_CUDA "Build with CUDA support"
     HAVE_CUDA
     AND HAVE_CUBLAS
@@ -146,6 +150,11 @@ if(HAVE_TENGINE)
 	list(APPEND libs -Wl,--whole-archive ${TENGINE_LIBRARIES} -Wl,--no-whole-archive)
 endif()
 
+if(HAVE_TIMVX)
+    list(APPEND include_dirs ${TIMVX_INCLUDE_DIR})
+    list(APPEND libs -Wl,--whole-archive ${TIMVX_LIBRARY} -Wl,--no-whole-archive)
+endif()
+
 set(webnn_srcs "")
 if(NOT EMSCRIPTEN)
   if(HAVE_WEBNN)

modules/dnn/include/opencv2/dnn/all_layers.hpp

@@ -262,7 +262,7 @@ CV__DNN_INLINE_NS_BEGIN
     {
     public:
         int input_zp, output_zp;
-        float output_sc;
+        float input_sc, output_sc;
         static Ptr<BaseConvolutionLayer> create(const LayerParams& params);
     };
 
@@ -322,6 +322,7 @@ CV__DNN_INLINE_NS_BEGIN
     {
     public:
         int input_zp, output_zp;
+        float input_sc, output_sc;
         static Ptr<PoolingLayerInt8> create(const LayerParams& params);
     };
 
@@ -365,7 +366,8 @@ CV__DNN_INLINE_NS_BEGIN
     class CV_EXPORTS InnerProductLayerInt8 : public InnerProductLayer
     {
     public:
-        int output_zp;
+        int input_zp, output_zp;
+        float input_sc, output_sc;
         static Ptr<InnerProductLayerInt8> create(const LayerParams& params);
     };
 

modules/dnn/include/opencv2/dnn/dnn.hpp

@@ -75,6 +75,7 @@ CV__DNN_INLINE_NS_BEGIN
         DNN_BACKEND_VKCOM,
         DNN_BACKEND_CUDA,
         DNN_BACKEND_WEBNN,
+        DNN_BACKEND_TIMVX,
 #ifdef __OPENCV_BUILD
         DNN_BACKEND_INFERENCE_ENGINE_NGRAPH = 1000000,     // internal - use DNN_BACKEND_INFERENCE_ENGINE + setInferenceEngineBackendType()
         DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019,      // internal - use DNN_BACKEND_INFERENCE_ENGINE + setInferenceEngineBackendType()
@@ -95,7 +96,8 @@ CV__DNN_INLINE_NS_BEGIN
         DNN_TARGET_FPGA,  //!< FPGA device with CPU fallbacks using Inference Engine's Heterogeneous plugin.
         DNN_TARGET_CUDA,
         DNN_TARGET_CUDA_FP16,
-        DNN_TARGET_HDDL
+        DNN_TARGET_HDDL,
+        DNN_TARGET_NPU,
     };
 
     CV_EXPORTS std::vector< std::pair<Backend, Target> > getAvailableBackends();
@@ -321,6 +323,19 @@ CV__DNN_INLINE_NS_BEGIN
             const std::vector<Ptr<BackendWrapper>>& outputs
         );
 
+        /**
+         * @brief Returns a TimVX backend node
+         *
+         * @param   timVxInfo  void pointer to CSLContext object
+         * @param   inputsWrapper   layer inputs
+         * @param   outputsWrapper  layer outputs
+         * @param   isLast if the node is the last one of the TimVX Graph.
+         */
+        virtual Ptr<BackendNode> initTimVX(void* timVxInfo,
+                                           const std::vector<Ptr<BackendWrapper> > &inputsWrapper,
+                                           const std::vector<Ptr<BackendWrapper> > &outputsWrapper,
+                                           bool isLast);
+
        /**
         * @brief Automatic Halide scheduling based on layer hyper-parameters.
         * @param[in] node Backend node with Halide functions.

modules/dnn/src/int8layers/batch_norm_layer.cpp

@@ -4,6 +4,8 @@
 
 #include "../precomp.hpp"
 #include "layers_common.hpp"
+#include "../op_timvx.hpp"
+
 #include <opencv2/dnn/shape_utils.hpp>
 
 namespace cv
@@ -103,6 +105,11 @@ public:
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
+        if (backendId == DNN_BACKEND_TIMVX && haveTimVX())
+        {
+            return true;
+        }
+
         return backendId == DNN_BACKEND_OPENCV;
     }
 
@@ -116,6 +123,121 @@ public:
         return false;
     }
 
+    virtual Ptr<BackendNode> initTimVX(void* timVXInfo_,
+                                       const std::vector<Ptr<BackendWrapper> > &inputsWrapper,
+                                       const std::vector<Ptr<BackendWrapper> > &outputsWrapper,
+                                       bool isLast) CV_OVERRIDE
+    {
+#ifdef HAVE_TIMVX
+        // tvGraph Initialization.
+        auto timVxInfo = reinterpret_cast<TimVXInfo *>(timVXInfo_);
+        CV_Assert(timVxInfo);
+        Ptr<TimVXGraph> tvGraph = timVxInfo->getGraph();
+        CV_Assert(tvGraph);
+        Ptr<tim::vx::Graph> graph = tvGraph->graph;
+
+        const int numChannels = (int)origin_bias.total();
+        Mat tvGamma = origin_weights.reshape(1, numChannels);
+        Mat tvBeta = origin_bias.reshape(1, numChannels);
+
+        std::vector<int> inputsIndex;
+        std::vector<int> outputsIndex;
+
+        Mat tvMean = Mat::zeros(1, numChannels, CV_32F);
+        tvMean = tvMean.reshape(1, numChannels);
+        Mat tvVar = Mat::ones(1, numChannels, CV_32F);
+        tvVar = tvVar.reshape(1, numChannels);
+
+        CV_Assert(inputsWrapper.size() == 1);
+        if (outputsWrapper.size() > 1)
+            return Ptr<BackendNode>();
+
+        Ptr<tim::vx::Quantization> tvInputQuant = Ptr<tim::vx::Quantization>(
+                new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, input_sc, input_zp));
+
+        // input Tensor
+        auto inputWrapper = inputsWrapper[0].dynamicCast<TimVXBackendWrapper>();
+        Mat tmpInput = inputWrapper->getMat();
+
+        if (tmpInput.dims != 4)  // Only support 4 dim input.
+            return Ptr<BackendNode>();
+
+        int input_index = -1, mean_index = -1, var_index = -1, gamma_index = -1, beta_index = -1, output_index = -1;
+
+        if (inputWrapper->isTensor())
+        {
+            input_index = tvGraph->getTensorIndex(inputWrapper->getTensor());
+            if (input_index == -1)
+            {
+                // Copy To New inputWrapper
+                Mat tmp = inputWrapper->getMat();
+                inputWrapper = Ptr<TimVXBackendWrapper>(new TimVXBackendWrapper(tmp));
+            }
+        }
+
+        if (!inputWrapper->isTensor())
+        {
+            inputWrapper->createTensor(graph,tim::vx::TensorAttribute::INPUT, tvInputQuant);
+            input_index = tvGraph->addWrapper(inputWrapper);
+        }
+        inputsIndex.push_back(input_index);
+
+        // Mean tensor
+        Ptr<TimVXBackendWrapper> meanWrapper = Ptr<TimVXBackendWrapper>(new TimVXBackendWrapper(tvMean));
+        Ptr<tim::vx::Quantization> meanQuant;
+        meanWrapper->createTensor(graph, tim::vx::TensorAttribute::CONSTANT);
+        mean_index = tvGraph->addWrapper(meanWrapper);
+        inputsIndex.push_back(mean_index);
+
+        // Var tensor
+        Ptr<TimVXBackendWrapper> varWrapper = Ptr<TimVXBackendWrapper>(new TimVXBackendWrapper(tvVar));
+        varWrapper->createTensor(graph,tim::vx::TensorAttribute::CONSTANT);
+        var_index = tvGraph->addWrapper(varWrapper);
+        inputsIndex.push_back(var_index);
+
+        // Gamma tensor
+        Ptr<TimVXBackendWrapper> gammaWrapper = Ptr<TimVXBackendWrapper>(new TimVXBackendWrapper(tvGamma));
+        gammaWrapper->createTensor(graph,tim::vx::TensorAttribute::CONSTANT);
+        gamma_index = tvGraph->addWrapper(gammaWrapper);
+        inputsIndex.push_back(gamma_index);
+
+        // Beta tensor
+        Ptr<TimVXBackendWrapper> betaWrapper = Ptr<TimVXBackendWrapper>(new TimVXBackendWrapper(tvBeta));
+        betaWrapper->createTensor(graph,tim::vx::TensorAttribute::CONSTANT);
+        beta_index = tvGraph->addWrapper(betaWrapper);
+        inputsIndex.push_back(beta_index);
+
+        // Output tensor
+        CV_Assert(outputsWrapper.size() == 1);
+        Ptr<TimVXBackendWrapper> outputWrapper = outputsWrapper[0].dynamicCast<TimVXBackendWrapper>();
+        Ptr<tim::vx::Quantization> outputQuant = Ptr<tim::vx::Quantization>(
+                new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, output_sc, output_zp));
+
+        if (isLast)
+        {
+            auto shapeType = getShapeTypeFromMat(outputWrapper->getMat());
+
+            // For Graph Output tensor, we need to set tensor shape before createTensor().
+            outputWrapper->setTensorShape(shapeType);
+            outputWrapper->createTensor(graph, tim::vx::TensorAttribute::OUTPUT, outputQuant);
+        }
+        else
+        {
+            outputWrapper->createTensor(graph, tim::vx::TensorAttribute::TRANSIENT, outputQuant);
+        }
+
+        output_index = tvGraph->addWrapper(outputWrapper);
+        outputsIndex.push_back(output_index);
+
+        std::shared_ptr<tim::vx::Operation> tvBatchNorm = graph->CreateOperation<tim::vx::ops::BatchNorm>(0.f);
+
+        Ptr<TimVXBackendNode> tvBackendNode = new TimVXBackendNode(tvGraph, tvBatchNorm, inputsIndex, outputsIndex);
+
+        return tvBackendNode;
+#endif  // HAVE_TIMVX
+        return Ptr<BackendNode>();
+    }
+
     void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
     {
         CV_TRACE_FUNCTION();

modules/dnn/src/int8layers/convolution_layer.cpp

@@ -9,6 +9,7 @@
 
 #include "opencv2/core/hal/hal.hpp"
 #include "opencv2/core/hal/intrin.hpp"
+#include "../op_timvx.hpp"
 #include <iostream>
 #include <numeric>
 
@@ -46,6 +47,7 @@ public:
         int ngroups = params.get<int>("group", 1);
         CV_Assert(numOutput % ngroups == 0);
 
+        input_sc = params.get<float>("input_scale");
         input_zp = params.get<int>("input_zeropoint");
         output_zp = params.get<int>("zeropoints");
         output_sc = params.get<float>("scales");
@@ -181,6 +183,16 @@ public:
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
         size_t ksize = kernel_size.size();
+
+#ifdef HAVE_TIMVX
+        if (backendId == DNN_BACKEND_TIMVX)
+        {
+            /* only Conv1d and Conv2d supported. */
+            if (ksize == 2 || ksize == 1)
+                return true;
+            return false;
+        }
+#endif
         // Only default backend and Conv1D/Conv2D/Conv3D are supported
         return backendId == DNN_BACKEND_OPENCV && ksize >= 1 && ksize <= 3;
     }
@@ -261,6 +273,11 @@ public:
 
     bool setActivation(const Ptr<ActivationLayer>& layer) CV_OVERRIDE
     {
+        // TODO! add activation in convolution.
+#ifdef HAVE_TIMVX
+        if (preferableTarget == DNN_TARGET_NPU)
+            return false;
+#endif
         Ptr<ActivationLayerInt8> activ_int8 = layer.dynamicCast<ActivationLayerInt8>();
         if (!activ_int8.empty())
         {
@@ -300,6 +317,249 @@ public:
         outputMultiplier[outCn] = outputMultiplier[outCn+1] = outputMultiplier[outCn-1];
     }
 
+    virtual Ptr<BackendNode> initTimVX(void* timVXInfo_,
+                                       const std::vector<Ptr<BackendWrapper> > &inputsWrapper,
+                                       const std::vector<Ptr<BackendWrapper> > &outputsWrapper,
+                                       bool isLast) CV_OVERRIDE
+    {
+#ifdef HAVE_TIMVX
+        /* TODO :support GroupConv;
+        Ref:
+        https://github.com/VeriSilicon/TIM-VX/blob/main/docs/Operators.md#conv2d
+        Link Reference: https://github.com/VeriSilicon/TIM-VX/blob/main/src/tim/vx/ops/conv1d_test.cc
+        */
+
+        // tvGraph Initialization.
+        auto timVxInfo = reinterpret_cast<TimVXInfo *>(timVXInfo_);
+        CV_Assert(timVxInfo);
+        Ptr<TimVXGraph> tvGraph = timVxInfo->getGraph();
+        CV_Assert(tvGraph);
+        Ptr<tim::vx::Graph> graph = tvGraph->graph;
+
+        Mat tvWeightMat = blobs[0];
+
+        std::vector<int> tvBiasVec;
+        tvBiasVec.assign(biasvec.begin(), biasvec.end() - 2);
+        Mat tvBiasMat(tvBiasVec);
+
+        for (int i = 0; i < numOutput; i++)
+        {
+            tvBiasVec[i] += input_zp * (cv::sum(blobs[0].row(i))[0]);
+        }
+
+        // Padding Type
+        tim::vx::PadType tvPadType;
+
+        if (padMode.empty())
+        {
+            tvPadType = tim::vx::PadType::AUTO; // TODO! check the padding type.
+        }
+        else if(padMode == "VALID")
+        {
+            tvPadType = tim::vx::PadType::VALID;
+        }
+        else if (padMode == "SAME")
+        {
+            tvPadType = tim::vx::PadType::SAME;
+        }
+        else
+        {
+            CV_Error(Error::StsError, "Unsupported padding mode in TimVXBackend!");
+        }
+
+        size_t ksize = kernel_size.size();
+
+        std::vector<int> inputsIndex;
+        std::vector<int> outputsIndex;
+
+        CV_Assert(inputsWrapper.size() == 1);
+        CV_Assert(ksize == 2 || ksize == 1);
+
+        std::vector<float> weight_scs, bias_scs;
+        std::vector<int32_t> weight_zps, bias_zps;
+
+        weight_scs.resize(numOutput);
+        bias_scs.resize(numOutput);
+
+        for (int i = 0; i < numOutput; i++)
+        {
+            bias_scs[i] = outputMultiplier[i] * output_sc;
+            weight_scs[i] = bias_scs[i] / input_sc;
+        }
+
+        weight_zps.assign(numOutput, 0);
+        bias_zps.assign(numOutput, 0);
+
+        bool tvSymmetric;
+        tvSymmetric = getQuantType(weight_scs, numOutput);
+
+        // input Tensor
+        auto inputWrapper = inputsWrapper[0].dynamicCast<TimVXBackendWrapper>();
+        int input_index = -1, weight_index = -1, bias_index = -1, output_index = -1;
+
+        if (inputWrapper->isTensor())
+        {
+            input_index = tvGraph->getTensorIndex(inputWrapper->getTensor());
+            if (input_index == -1)
+            {
+                // Copy To New inputWrapper
+                Mat tmp = inputWrapper->getMat();
+                inputWrapper = Ptr<TimVXBackendWrapper>(new TimVXBackendWrapper(tmp));
+            }
+        }
+
+        if (!inputWrapper->isTensor())
+        {
+            Ptr<tim::vx::Quantization> tvInputQuant = Ptr<tim::vx::Quantization>(
+                    new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, input_sc, input_zp));
+            inputWrapper->createTensor(graph, tim::vx::TensorAttribute::INPUT, tvInputQuant);
+            input_index = tvGraph->addWrapper(inputWrapper);
+        }
+        inputsIndex.push_back(input_index);
+
+        // weight Tensor
+        auto tvConvWeightShape = shape(tvWeightMat);
+        Mat tvInputMat = inputWrapper->getMat();
+        // calculate group value.
+        int group = tvInputMat.size[1] / tvWeightMat.size[1];
+
+        // TODO! It will be supported in future.
+        if (tvSymmetric && tvWeightMat.total() == tvConvWeightShape[0])
+            return Ptr<TimVXBackendNode>();
+        // Reverse weight shape From OpenCV NCHW to TimVX WHCN.
+        std::reverse(tvConvWeightShape.begin(), tvConvWeightShape.end());
+
+        Ptr<TimVXBackendWrapper> weightWrapper = Ptr<TimVXBackendWrapper>(new TimVXBackendWrapper(tvWeightMat));
+        Ptr<tim::vx::Quantization> weightQuant;
+
+        if (tvSymmetric)
+        {
+            int wtChanneldim = tvWeightMat.dims - 1;
+            weightQuant = Ptr<tim::vx::Quantization>(
+                    new tim::vx::Quantization(tim::vx::QuantType::SYMMETRIC_PER_CHANNEL, wtChanneldim,
+                                              weight_scs, weight_zps));
+        }
+        else
+        {
+            weightQuant = Ptr<tim::vx::Quantization>(
+                    new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, weight_scs[0], 0));
+        }
+        weightWrapper->createTensor(graph,tim::vx::TensorAttribute::CONSTANT, weightQuant);
+
+        weight_index = tvGraph->addWrapper(weightWrapper);
+        inputsIndex.push_back(weight_index);
+
+        // Bias Tensor
+        Ptr<TimVXBackendWrapper> biasWrapper = Ptr<TimVXBackendWrapper>(new TimVXBackendWrapper(tvBiasMat));
+        Ptr<tim::vx::Quantization> biasQuant;
+
+        if (tvSymmetric)
+        {
+            biasQuant = Ptr<tim::vx::Quantization>(
+                    new tim::vx::Quantization(tim::vx::QuantType::SYMMETRIC_PER_CHANNEL, 0,
+                                              bias_scs, bias_zps));
+        }
+        else
+        {
+            biasQuant = Ptr<tim::vx::Quantization>(
+                    new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, weight_scs[0] * input_sc, 0));
+        }
+
+        biasWrapper->createTensor(graph, tim::vx::TensorAttribute::CONSTANT, biasQuant);
+        bias_index = tvGraph->addWrapper(biasWrapper);
+        inputsIndex.push_back(bias_index);
+        // Output tensor
+        CV_Assert(outputsWrapper.size() == 1);
+        auto outputWrapper = outputsWrapper[0].dynamicCast<TimVXBackendWrapper>();
+        Ptr<tim::vx::Quantization> outputQuant = Ptr<tim::vx::Quantization>(
+                new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, output_sc, output_zp));
+
+        if (isLast)
+        {
+            // From OpenCV NCHW, to TimVX WHCN
+            auto shapeType = getShapeTypeFromMat(outputWrapper->getMat());
+
+            // For Graph Output tensor, we need to set tensor shape before createTensor().
+            outputWrapper->setTensorShape(shapeType);
+            outputWrapper->createTensor(graph, tim::vx::TensorAttribute::OUTPUT, outputQuant);
+        }
+        else
+        {
+            outputWrapper->createTensor(graph, tim::vx::TensorAttribute::TRANSIENT, outputQuant);
+        }
+
+        output_index = tvGraph->addWrapper(outputWrapper);
+        outputsIndex.push_back(output_index);
+
+        std::shared_ptr<tim::vx::Operation> tvConv;
+
+        if (ksize == 2)  // for conv2d
+        {
+            int multiplier = 0;
+            if(group == tvConvWeightShape[3] && group != 1)
+                multiplier = 1;
+            if (group == 1 || (group == tvConvWeightShape[3] && group != 1)) // Conv2D || DeConv2D
+            {
+                if (tvPadType == tim::vx::PadType::AUTO) {
+                    tvConv = graph->CreateOperation<tim::vx::ops::Conv2d>(
+                            tvConvWeightShape[3], tvPadType,
+                            std::array<uint32_t, 2>({(uint32_t) kernel_size[1], (uint32_t) kernel_size[0]}),
+                            std::array<uint32_t, 2>({(uint32_t) strides[1], (uint32_t) strides[0]}),
+                            std::array<uint32_t, 2>({(uint32_t) dilations[1], (uint32_t) dilations[0]}),
+                            std::array<uint32_t, 4>({(uint32_t) pads_begin[1], (uint32_t) pads_end[1],
+                                                     (uint32_t) pads_begin[0], (uint32_t) pads_end[0]}),
+                            multiplier);
+                }
+                else
+                {
+                    tvConv = graph->CreateOperation<tim::vx::ops::Conv2d>(
+                            tvPadType,
+                            std::array<uint32_t, 2>({(uint32_t) strides[1], (uint32_t) strides[0]}),
+                            std::array<uint32_t, 2>({(uint32_t) dilations[1], (uint32_t) dilations[0]}),
+                            multiplier);
+                }
+            }
+            else
+            {
+                // GroupedConv2d
+                if (tvPadType == tim::vx::PadType::AUTO)
+                {
+                    tvConv = graph->CreateOperation<tim::vx::ops::GroupedConv2d>(
+                            std::array<uint32_t, 4>({(uint32_t) pads_begin[1], (uint32_t) pads_end[1],
+                                                     (uint32_t) pads_begin[0], (uint32_t) pads_end[0]}),
+                            std::array<uint32_t, 2>({(uint32_t)strides[1], (uint32_t)strides[0]}),
+                            std::array<uint32_t, 2>({(uint32_t)dilations[1], (uint32_t)dilations[0]}),
+                            group);
+                }
+                else
+                {
+                    tvConv = graph->CreateOperation<tim::vx::ops::GroupedConv2d>(
+                            tvPadType,
+                            std::array<uint32_t, 2>({(uint32_t)strides[1], (uint32_t)strides[0]}),
+                            std::array<uint32_t, 2>({(uint32_t)dilations[1], (uint32_t)dilations[0]}),
+                            group);
+                }
+            }
+        }
+        else
+        {
+            // for Conv1d
+            if (group != 1)
+                CV_Error( CV_StsNotImplemented, " Grouped Conv1d or Depth-Wise Conv1d are not supported by "
+                                                "TimVX Backend. Please try OpenCV Backend.");
+            tvConv = graph->CreateOperation<tim::vx::ops::Conv1d>(
+                    tvConvWeightShape[2], tvPadType, (uint32_t)kernel_size[0],
+                    (uint32_t)strides[0],(uint32_t)dilations[0],
+                    std::array<uint32_t, 2>({(uint32_t)pads_begin[0], (uint32_t)pads_end[0]}));
+        }
+        // Create TimVXBackendNode
+        Ptr<TimVXBackendNode> tvBackendNode = new TimVXBackendNode(tvGraph, tvConv, inputsIndex, outputsIndex);
+
+        return tvBackendNode;
+#endif  // HAVE_TIMVX
+        return Ptr<BackendNode>();
+    }
+
     class ParallelConv : public cv::ParallelLoopBody
     {
     public:

modules/dnn/src/int8layers/elementwise_layers.cpp

@@ -4,6 +4,7 @@
 
 #include "../precomp.hpp"
 #include "layers_common.hpp"
+#include "../op_timvx.hpp"
 
 #include <opencv2/dnn/shape_utils.hpp>
 #include <iostream>
@@ -16,14 +17,45 @@ namespace dnn
 class ActivationLayerInt8Impl CV_FINAL : public ActivationLayerInt8
 {
 public:
+    int input_zp, output_zp;
+    float input_sc, output_sc;
+    float slope = 0.0f;
+
+#ifdef HAVE_TIMVX
+    tvActivationType tvActType;
+#endif
     ActivationLayerInt8Impl(const LayerParams &params)
     {
         setParamsFrom(params);
         activationLUT = !blobs.empty() ? blobs[0] : Mat();
+
+        input_zp = params.get<int>("input_zeropoint");
+        input_sc = params.get<float>("input_scale");
+        output_zp = params.get<int>("zeropoints");
+        output_sc = params.get<float>("scales");
+
+        if (params.has("slope"))
+        {
+            slope = params.get<float>("slope");
+        }
+
+#ifdef HAVE_TIMVX
+        tvActType = getTimVXActType(type);
+#endif
+
     }
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
+#ifdef HAVE_TIMVX
+        if (backendId == DNN_BACKEND_TIMVX)
+        {
+            // TODO!: Leaky ReLU will be supported in future.
+            if (tvActType == tvActReLU && slope != 0.f)
+                return false;
+            return tvActType != tvActNotSupported;
+        }
+#endif
         return backendId == DNN_BACKEND_OPENCV;
     }
 
@@ -106,6 +138,112 @@ public:
         }
     };
 
+    virtual Ptr<BackendNode> initTimVX(void* timVXInfo_,
+                                       const std::vector<Ptr<BackendWrapper> > &inputsWrapper,
+                                       const std::vector<Ptr<BackendWrapper> > &outputsWrapper,
+                                       bool isLast) CV_OVERRIDE
+    {
+#ifdef HAVE_TIMVX
+        // tvGraph Initialization.
+        auto timVxInfo = reinterpret_cast<TimVXInfo *>(timVXInfo_);
+        CV_Assert(timVxInfo);
+        Ptr<TimVXGraph> tvGraph = timVxInfo->getGraph();
+        CV_Assert(tvGraph);
+        Ptr<tim::vx::Graph> graph = tvGraph->graph;
+
+        std::vector<int> inputsIndex, outputsIndex;
+        int input_index, output_index;
+        CV_Assert(inputsWrapper.size() == 1);
+
+        // input Tensor
+        Ptr<TimVXBackendWrapper> inputWrapper = inputsWrapper[0].dynamicCast<TimVXBackendWrapper>();
+
+        if (inputWrapper->isTensor())
+        {
+            input_index = tvGraph->getTensorIndex(inputWrapper->getTensor());
+            if(input_index == -1)
+            {
+                // Copy To New inputWrapper
+                Mat tmp = inputWrapper->getMat();
+                inputWrapper = Ptr<TimVXBackendWrapper>(new TimVXBackendWrapper(tmp));
+            }
+        }
+
+        if (!inputWrapper->isTensor())
+        {
+            Ptr<tim::vx::Quantization> tvInputQuant = Ptr<tim::vx::Quantization>(
+                    new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, input_sc, input_zp));
+            inputWrapper->createTensor(graph, tim::vx::TensorAttribute::INPUT, tvInputQuant);
+            input_index = tvGraph->addWrapper(inputWrapper);
+        }
+
+        inputsIndex.push_back(input_index);
+
+        // output tensor
+        CV_Assert(outputsWrapper.size() == 1);
+        Ptr<TimVXBackendWrapper> outputWrapper = outputsWrapper[0].dynamicCast<TimVXBackendWrapper>();
+        Ptr<tim::vx::Quantization> outputQuant = Ptr<tim::vx::Quantization>(
+                new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, output_sc, output_zp));
+
+        Ptr<tim::vx::Tensor> outputTensor;
+
+        if (isLast)
+        {
+            auto shapeType = getShapeTypeFromMat(outputWrapper->getMat());
+
+            // For Graph Output tensor, we need to set tensor shape before createTensor().
+            outputWrapper->setTensorShape(shapeType);
+            outputWrapper->createTensor(graph, tim::vx::TensorAttribute::OUTPUT, outputQuant);
+        }
+        else
+        {
+            outputWrapper->createTensor(graph, tim::vx::TensorAttribute::TRANSIENT, outputQuant);
+        }
+        output_index = tvGraph->addWrapper(outputWrapper);
+        outputsIndex.push_back(output_index);
+
+        std::shared_ptr<tim::vx::Operation> tvAct;
+
+        switch(tvActType) {
+            case tvActReLU:
+            {
+                if (slope != 0.f)
+                    tvAct = graph->CreateOperation<tim::vx::ops::LeakyRelu>(slope);
+                else
+                    tvAct = graph->CreateOperation<tim::vx::ops::Relu>();
+                break;
+            }
+            case tvActReLU6:
+                tvAct = graph->CreateOperation<tim::vx::ops::Relu6>();
+                break;
+            case tvActTanH:
+                tvAct = graph->CreateOperation<tim::vx::ops::Tanh>();
+                break;
+            case tvActSwish:
+                tvAct = graph->CreateOperation<tim::vx::ops::Swish>();
+                break;
+            case tvActMish:
+                tvAct = graph->CreateOperation<tim::vx::ops::Mish>();
+                break;
+            case tvActSigmoid:
+                tvAct = graph->CreateOperation<tim::vx::ops::Sigmoid>();
+                break;
+            case tvActELU:
+                tvAct = graph->CreateOperation<tim::vx::ops::Elu>();
+                break;
+            default:
+                // TODO! check the default function.
+                tvAct = graph->CreateOperation<tim::vx::ops::Relu>();
+                break;
+        }
+
+        Ptr<TimVXBackendNode> tvBackendNode = new TimVXBackendNode(tvGraph, tvAct, inputsIndex, outputsIndex);
+
+        return tvBackendNode;
+#endif  // HAVE_TIMVX
+        return Ptr<BackendNode>();
+    }
+
     void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
     {
         CV_TRACE_FUNCTION();

modules/dnn/src/int8layers/eltwise_layer.cpp

@@ -4,6 +4,7 @@
 
 #include "../precomp.hpp"
 #include "layers_common.hpp"
+#include "../op_timvx.hpp"
 #include <opencv2/dnn/shape_utils.hpp>
 
 namespace cv
@@ -22,6 +23,10 @@ public:
     } op;
     std::vector<float> coeffs;
     std::vector<int> zeropoints;
+    std::vector<float> scales;
+
+    int output_zp;
+    float output_sc;
 
     enum OutputChannelsMode
     {
@@ -84,6 +89,20 @@ public:
             }
         }
 
+        if (params.has("input_scales"))
+        {
+            DictValue sc = params.get("input_scales");
+            int i, n = sc.size();
+            scales.resize(n);
+            for (i = 0; i < n; i++)
+            {
+                scales[i] = sc.get<float>(i);
+            }
+        }
+
+        output_zp = params.get<int>("zeropoints");
+        output_sc = params.get<float>("scales");
+
         channelsModeInput = ELTWISE_CHANNNELS_SAME;
         if (params.has("output_channels_mode"))
         {
@@ -116,6 +135,9 @@ public:
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
+        // For TimVX Backend, only ELTWISE_CHANNNELS_SAME was supported.
+        if (backendId == DNN_BACKEND_TIMVX && haveTimVX())
+            return channelsModeInput == ELTWISE_CHANNNELS_SAME;
         return backendId == DNN_BACKEND_OPENCV;
     }
 
@@ -219,6 +241,134 @@ public:
         }
     }
 
+    virtual Ptr<BackendNode> initTimVX(void* timVXInfo_,
+                                       const std::vector<Ptr<BackendWrapper> > &inputsWrapper,
+                                       const std::vector<Ptr<BackendWrapper> > &outputsWrapper,
+                                       bool isLast) CV_OVERRIDE
+    {
+#ifdef HAVE_TIMVX
+        // tvGraph Initialization.
+        if (inputsWrapper.size() != 2)
+            return Ptr<BackendNode>();
+
+        auto timVxInfo = reinterpret_cast<TimVXInfo *>(timVXInfo_);
+        CV_Assert(timVxInfo);
+        Ptr<TimVXGraph> tvGraph = timVxInfo->getGraph();
+        CV_Assert(tvGraph);
+        Ptr<tim::vx::Graph> graph = tvGraph->graph;
+
+        bool isSub = false;
+        // TODO: support variable coeffs.
+        if (op == SUM)
+        {
+            CV_Assert(coeffs.size() == scales.size());
+            std::vector<float> originalCoeffs;
+
+            for (int i = 0; i < coeffs.size(); i++)
+            {
+                originalCoeffs.push_back(coeffs[i] * output_sc / scales[i]);
+            }
+
+            float eps = std::numeric_limits<float>::epsilon();
+            if (std::fabs(originalCoeffs[0] - 1.0f) <= eps * std::fabs(originalCoeffs[0] + 1.0f) &&
+                std::fabs(originalCoeffs[1] + 1.0f) <= eps * std::fabs(originalCoeffs[1] - 1.0f))
+            {
+                // Sub, if coeffs = {1., -1.}, isSub = true.
+                isSub = true;
+            }
+            else if (std::fabs(originalCoeffs[0] - 1.0f) <= eps * std::fabs(originalCoeffs[0] + 1.0f) &&
+                std::abs(originalCoeffs[1] - 1.0f) <= eps * std::abs(originalCoeffs[1] + 1.0f))
+            {
+                // Sum, if coeff = {1., 1.}, isSub = false.
+                isSub = false;
+            }
+            else
+            {
+                return Ptr<BackendNode>();
+            }
+        }
+
+        std::vector<int> inputsIndex, outputsIndex;
+        int input_index = -1, output_index = -1;
+        CV_Assert(channelsModeInput == ELTWISE_CHANNNELS_SAME);
+
+        // Input
+        Ptr<TimVXBackendWrapper> inputWrapper;
+
+        CV_Assert(!scales.empty() && !zeropoints.empty());
+
+        for (int i = 0; i<inputsWrapper.size(); i++){
+            inputWrapper = inputsWrapper[i].dynamicCast<TimVXBackendWrapper>();
+
+            if (inputWrapper->isTensor())
+            {
+                input_index = tvGraph->getTensorIndex(inputWrapper->getTensor());
+                if (input_index == -1)
+                {
+                    // Copy To New inputWrapper
+                    Mat tmp = inputWrapper->getMat();
+                    inputWrapper = Ptr<TimVXBackendWrapper>(new TimVXBackendWrapper(tmp));
+                }
+            }
+
+            if (!inputWrapper->isTensor())
+            {
+                Ptr<tim::vx::Quantization> tvInputQuant = Ptr<tim::vx::Quantization>(
+                        new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, scales[i], zeropoints[i]));
+                inputWrapper->createTensor(graph,tim::vx::TensorAttribute::INPUT, tvInputQuant);
+                input_index = tvGraph->addWrapper(inputWrapper);
+            }
+
+            inputsIndex.push_back(input_index);
+        }
+
+        // Output
+        CV_Assert(outputsWrapper.size() == 1);
+        Ptr<TimVXBackendWrapper> outputWrapper = outputsWrapper[0].dynamicCast<TimVXBackendWrapper>();
+        Ptr<tim::vx::Quantization> outputQuant = Ptr<tim::vx::Quantization>(
+                new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, output_sc, output_zp));
+
+        if (isLast)
+        {
+            auto shapeType = getShapeTypeFromMat(outputWrapper->getMat());
+
+            // For Graph Output tensor, we need to set tensor shape before createTensor().
+            outputWrapper->setTensorShape(shapeType);
+            outputWrapper->createTensor(graph, tim::vx::TensorAttribute::OUTPUT, outputQuant);
+        }
+        else
+        {
+            outputWrapper->createTensor(graph, tim::vx::TensorAttribute::TRANSIENT, outputQuant);
+        }
+        output_index = tvGraph->addWrapper(outputWrapper);
+        outputsIndex.push_back(output_index);
+
+        std::shared_ptr<tim::vx::Operation> tvEltwise;
+
+        switch (op) {
+            case SUM:
+                if (isSub)
+                    tvEltwise = graph->CreateOperation<tim::vx::ops::Sub>();
+                else
+                    tvEltwise = graph->CreateOperation<tim::vx::ops::Add>();
+                break;
+            case PROD:
+                tvEltwise = graph->CreateOperation<tim::vx::ops::Multiply>();
+                break;
+            case MAX:
+                tvEltwise = graph->CreateOperation<tim::vx::ops::Maximum>();
+                break;
+            default:
+                CV_Error(Error::StsNotImplemented, "Unsupported eltwise operation");
+        }
+
+        Ptr<TimVXBackendNode> tvBackendNode = new TimVXBackendNode(tvGraph, tvEltwise, inputsIndex, outputsIndex);
+
+        return tvBackendNode;
+#endif  // HAVE_TIMVX
+        return Ptr<BackendNode>();
+    }
+
     class EltwiseInvoker : public ParallelLoopBody
     {
         EltwiseLayerInt8Impl& self;

modules/dnn/src/int8layers/fully_connected_layer.cpp

@@ -4,6 +4,7 @@
 
 #include "../precomp.hpp"
 #include "layers_common.hpp"
+#include "../op_timvx.hpp"
 
 #include <opencv2/dnn/shape_utils.hpp>
 
@@ -19,7 +20,11 @@ public:
     FullyConnectedLayerInt8Impl(const LayerParams& params)
     {
         setParamsFrom(params);
+
+        input_sc = params.get<float>("input_scale");
+        input_zp = params.get<int>("input_zeropoint");
         output_zp = params.get<int>("zeropoints");
+        output_sc = params.get<float>("scales");
         axis = params.get<int>("axis", 1);
         if (blobs.size() == 3)
         {
@@ -71,11 +76,25 @@ public:
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
+        if (backendId == DNN_BACKEND_TIMVX && haveTimVX())
+        {
+           if (biasMat.empty())
+               return true;
+           else
+               return false;
+        }
+
         return backendId == DNN_BACKEND_OPENCV;
     }
 
     virtual bool setActivation(const Ptr<ActivationLayer>& layer) CV_OVERRIDE
     {
+        // TODO! add activation in Fully connection.
+#ifdef HAVE_TIMVX
+        if(preferableTarget == DNN_TARGET_NPU)
+            return false;
+#endif
+
         Ptr<ActivationLayerInt8> activ_int8 = layer.dynamicCast<ActivationLayerInt8>();
         if (!activ_int8.empty())
         {
@@ -87,6 +106,120 @@ public:
         return false;
     }
 
+
+    virtual Ptr<BackendNode> initTimVX(void* timVXInfo_,
+                                       const std::vector<Ptr<BackendWrapper> > &inputsWrapper,
+                                       const std::vector<Ptr<BackendWrapper> > &outputsWrapper,
+                                       bool isLast) CV_OVERRIDE
+    {
+#ifdef HAVE_TIMVX
+        // tvGraph Initialization.
+        auto timVxInfo = reinterpret_cast<TimVXInfo *>(timVXInfo_);
+        CV_Assert(timVxInfo);
+        Ptr<TimVXGraph> tvGraph = timVxInfo->getGraph();
+        CV_Assert(tvGraph);
+        Ptr<tim::vx::Graph> graph = tvGraph->graph;
+
+        int numOutput = blobs[0].size[0];
+        Mat weightMat = blobs[0];
+
+        std::vector<int> inputsIndex;
+        std::vector<int> outputsIndex;
+
+        std::vector<float> weight_scs, bias_scs;
+        std::vector<int32_t> weight_zps;
+
+        bias_scs.resize(numOutput);
+        weight_scs.resize(numOutput);
+
+        for (int i = 0; i < numOutput; i++)
+        {
+            bias_scs[i] = outputMultiplier.at<float>(i) * output_sc;
+            weight_scs[i] = bias_scs[i] / input_sc;
+        }
+
+        weight_zps.assign(numOutput, 0);
+
+        // input Tensor
+        auto inputWrapper = inputsWrapper[0].dynamicCast<TimVXBackendWrapper>();
+        int input_index = -1, weight_index = -1, output_index = -1;
+
+        if (inputWrapper->isTensor())
+        {
+            input_index = tvGraph->getTensorIndex(inputWrapper->getTensor());
+            if (input_index == -1)
+            {
+                // Copy To New inputWrapper
+                Mat tmp = inputWrapper->getMat();
+                inputWrapper = Ptr<TimVXBackendWrapper>(new TimVXBackendWrapper(tmp));
+            }
+        }
+
+        if (!inputWrapper->isTensor() || input_index == -1)
+        {
+            Ptr<tim::vx::Quantization> tvInputQuant = Ptr<tim::vx::Quantization>(
+                    new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, input_sc, input_zp));
+            inputWrapper->createTensor(graph,tim::vx::TensorAttribute::INPUT, tvInputQuant);
+            input_index = tvGraph->addWrapper(inputWrapper);
+        }
+        inputsIndex.push_back(input_index);
+
+        // weight tensor
+        Ptr<TimVXBackendWrapper> weightWrapper = Ptr<TimVXBackendWrapper>(new TimVXBackendWrapper(weightMat));
+        Ptr<tim::vx::Quantization> weightQuant;
+
+        bool tvSymmetric;
+        tvSymmetric = getQuantType(weight_scs, numOutput);
+
+        if (tvSymmetric)
+        {
+            // TODO! fix the following issue.
+            // TimVX does not support the SYMMETRIC PER CHANNEL MatMul.
+            return Ptr<BackendNode>();
+        }
+        else
+        {
+            weightQuant = Ptr<tim::vx::Quantization>(
+                    new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC,  weight_scs[0], 0));
+        }
+        weightWrapper->createTensor(graph,tim::vx::TensorAttribute::CONSTANT, weightQuant);
+
+        weight_index = tvGraph->addWrapper(weightWrapper);
+        inputsIndex.push_back(weight_index);
+
+        // Output tensor
+        CV_Assert(outputsWrapper.size() == 1);
+        Ptr<TimVXBackendWrapper> outputWrapper = outputsWrapper[0].dynamicCast<TimVXBackendWrapper>();
+        Ptr<tim::vx::Quantization> outputQuant = Ptr<tim::vx::Quantization>(
+                new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, output_sc, output_zp));
+
+        if (isLast)
+        {
+            auto shapeType = getShapeTypeFromMat(outputWrapper->getMat());
+
+            // For Graph Output tensor, we need to set tensor shape before createTensor().
+            outputWrapper->setTensorShape(shapeType);
+            outputWrapper->createTensor(graph, tim::vx::TensorAttribute::OUTPUT, outputQuant);
+        }
+        else
+        {
+            outputWrapper->createTensor(graph, tim::vx::TensorAttribute::TRANSIENT, outputQuant);
+        }
+
+        output_index = tvGraph->addWrapper(outputWrapper);
+        outputsIndex.push_back(output_index);
+
+        std::shared_ptr<tim::vx::Operation> tvMatmul;
+
+        tvMatmul = graph->CreateOperation<tim::vx::ops::Matmul>(false, true);
+
+        Ptr<TimVXBackendNode> tvBackendNode = new TimVXBackendNode(tvGraph, tvMatmul, inputsIndex, outputsIndex);
+
+        return tvBackendNode;
+#endif  // HAVE_TIMVX
+        return Ptr<BackendNode>();
+    }
+
     class FullyConnected : public ParallelLoopBody
     {
     public:

modules/dnn/src/int8layers/pooling_layer.cpp

@@ -4,6 +4,7 @@
 
 #include "../precomp.hpp"
 #include "layers_common.hpp"
+#include "../op_timvx.hpp"
 #include "opencv2/core/hal/intrin.hpp"
 
 #include <float.h>
@@ -26,9 +27,12 @@ public:
         globalPooling = false;
         isGlobalPooling = std::vector<bool>(3, false);
         output_zp = params.get<int>("zeropoints");
-        input_zp = params.get<int>("input_zeropoint", 0);
+        input_zp = params.get<int>("input_zeropoint", output_zp);
         multiplier = params.get<float>("multiplier", 1.f);
 
+        output_sc = params.get<float>("scales");
+        input_sc =  multiplier * output_sc;
+
         hasDynamicShapes = params.get<bool>("has_dynamic_shapes", false);
         shapesInitialized = !hasDynamicShapes;
 
@@ -103,6 +107,24 @@ public:
             else
                 return false;
         }
+        else if (backendId == DNN_BACKEND_TIMVX && haveTimVX())
+        {
+            // Only pool 2d and pool 1d were supported.
+            if (kernel_size.size() == 3)
+            {
+                // fallback to CPU implementation.
+                preferableTarget = DNN_TARGET_CPU;
+                return false;
+            }
+            if (!avePoolPaddedArea) // TimVX does not support exclude padding.
+                return false;
+            if (globalPooling) // TODO support globalPooling in TimVX backend.
+                return false;
+            if (kernel_size.size() == 2)
+                return type == MAX || type == AVE;
+            return false;
+        }
+
         return false;
     }
 
@@ -116,6 +138,139 @@ public:
         return false;
     }
 
+
+    virtual Ptr<BackendNode> initTimVX(void* timVXInfo_,
+                                       const std::vector<Ptr<BackendWrapper> > &inputsWrapper,
+                                       const std::vector<Ptr<BackendWrapper> > &outputsWrapper,
+                                       bool isLast) CV_OVERRIDE
+    {
+#ifdef HAVE_TIMVX
+        // tvGraph Initialization.
+        auto timVxInfo = reinterpret_cast<TimVXInfo *>(timVXInfo_);
+        CV_Assert(timVxInfo);
+        Ptr<TimVXGraph> tvGraph = timVxInfo->getGraph();
+        CV_Assert(tvGraph);
+        Ptr<tim::vx::Graph> graph = tvGraph->graph;
+
+        tim::vx::PoolType tvPoolType;
+        tim::vx::RoundType tvRoundType;
+        size_t ksize = kernel_size.size();
+        if (ksize != 2)
+            return Ptr<BackendNode>();
+
+        // type Change from OpenCV to TimVX only MAX and AVG are supported.
+        switch (type) {
+            case MAX: {
+                tvPoolType = tim::vx::PoolType::MAX;
+                break;
+            }
+            case AVE:{
+                tvPoolType = tim::vx::PoolType::AVG;
+                break;
+            }
+            default:
+                CV_Error(Error::StsNotImplemented, "Not implemented Pooling type in TimVX Backend.");
+        }
+
+        // Padding Type
+        tim::vx::PadType tvPadType;
+        if (padMode.empty())
+        {
+            tvPadType = tim::vx::PadType::AUTO; // TODO! check the padding type.
+        }
+        else if(padMode == "VALID")
+        {
+            tvPadType = tim::vx::PadType::VALID;
+        }
+        else if (padMode == "SAME")
+        {
+            tvPadType = tim::vx::PadType::SAME;
+        }
+        else
+        {
+            CV_Error(Error::StsError, "Unsupported padding mode in TimVXBackend!");
+        }
+
+        if (ceilMode)
+            tvRoundType = tim::vx::RoundType::CEILING;
+        else
+            tvRoundType = tim::vx::RoundType::FLOOR;
+
+        auto input = inputsWrapper[0];
+        std::vector<int> inputsIndex;
+        std::vector<int> outputsIndex;
+
+        // input Tensor
+        auto inputWrapper = inputsWrapper[0].dynamicCast<TimVXBackendWrapper>();
+        int input_index, output_index;
+
+        if (inputWrapper->isTensor())
+        {
+            input_index = tvGraph->getTensorIndex(inputWrapper->getTensor());
+            if (input_index == -1)
+            {
+                // Copy To New inputWrapper
+                Mat tmp = inputWrapper->getMat();
+                inputWrapper = Ptr<TimVXBackendWrapper>(new TimVXBackendWrapper(tmp));
+            }
+        }
+
+        if (!inputWrapper->isTensor())
+        {
+            Ptr<tim::vx::Quantization> tvInputQuant = Ptr<tim::vx::Quantization>(
+                    new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, input_sc, input_zp));
+            inputWrapper->createTensor(graph,tim::vx::TensorAttribute::INPUT, tvInputQuant);
+            input_index = tvGraph->addWrapper(inputWrapper);
+        }
+        inputsIndex.push_back(input_index);
+
+        // Output tensor
+        CV_Assert(outputsWrapper.size() == 1);
+        auto outputWrapper = outputsWrapper[0].dynamicCast<TimVXBackendWrapper>();
+        Ptr<tim::vx::Quantization> outputQuant = Ptr<tim::vx::Quantization>(
+                new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, output_sc, output_zp));
+
+        if (isLast)
+        {
+            auto shapeType = getShapeTypeFromMat(outputWrapper->getMat());
+            // For Graph Output tensor, we need to set tensor shape before createTensor().
+            outputWrapper->setTensorShape(shapeType);
+            outputWrapper->createTensor(graph, tim::vx::TensorAttribute::OUTPUT, outputQuant);
+        }
+        else
+        {
+            outputWrapper->createTensor(graph, tim::vx::TensorAttribute::TRANSIENT, outputQuant);
+        }
+
+        output_index = tvGraph->addWrapper(outputWrapper);
+        outputsIndex.push_back(output_index);
+        std::shared_ptr<tim::vx::Operation> tvPool;
+
+        if (tvPadType == tim::vx::PadType::AUTO)
+        {
+            tvPool = graph->CreateOperation<tim::vx::ops::Pool2d>( tvPoolType,
+                       std::array<uint32_t, 4>({(uint32_t) pads_begin[1], (uint32_t) pads_end[1],
+                                                (uint32_t) pads_begin[0], (uint32_t) pads_end[0]}),
+                       std::array<uint32_t, 2>({(uint32_t)kernel_size[1], (uint32_t)kernel_size[0]}),
+                       std::array<uint32_t, 2>({(uint32_t)strides[1], (uint32_t)strides[0]}),
+                       tvRoundType);
+        }
+        else
+        {
+            tvPool = graph->CreateOperation<tim::vx::ops::Pool2d>(
+                    tvPoolType, tvPadType,
+                    std::array<uint32_t, 2>({(uint32_t)kernel_size[1], (uint32_t)kernel_size[0]}),
+                    std::array<uint32_t, 2>({(uint32_t)strides[1], (uint32_t)strides[0]}),
+                    tvRoundType);
+        }
+
+        Ptr<TimVXBackendNode> tvBackendNode = new TimVXBackendNode(tvGraph, tvPool, inputsIndex, outputsIndex);
+
+        return tvBackendNode;
+#endif  // HAVE_TIMVX
+        return Ptr<BackendNode>();
+    }
+
     void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
     {
         CV_TRACE_FUNCTION();

modules/dnn/src/int8layers/quantization_utils.cpp

@@ -4,6 +4,7 @@
 
 #include "../precomp.hpp"
 #include "layers_common.hpp"
+#include "../op_timvx.hpp"
 
 namespace cv
 {
@@ -149,15 +150,21 @@ public:
 class RequantizeLayerImpl CV_FINAL : public RequantizeLayer
 {
 public:
+    bool isEltwise;
     RequantizeLayerImpl(const LayerParams& params)
     {
         scale = params.get<float>("scale", 1.f);
         shift = params.get<float>("shift", 0.f);
+        isEltwise = params.get<bool>("isEltwise", false);
         setParamsFrom(params);
     }
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
+        if (backendId == DNN_BACKEND_TIMVX && haveTimVX() && !isEltwise)
+        {
+            return true;
+        }
         return backendId == DNN_BACKEND_OPENCV;
     }
 
@@ -178,6 +185,82 @@ public:
         outputs_arr.getMatVector(outputs);
     }
 
+    virtual Ptr<BackendNode> initTimVX(void* timVXInfo_,
+                                       const std::vector<Ptr<BackendWrapper> > &inputsWrapper,
+                                       const std::vector<Ptr<BackendWrapper> > &outputsWrapper,
+                                       bool isLast) CV_OVERRIDE
+    {
+#ifdef HAVE_TIMVX
+        // preprocessing
+        // Check if data is 8-bit.
+        CV_Assert(inputsWrapper.size() == 1 && outputsWrapper.size() == 1);
+        Ptr<TimVXBackendWrapper> inputWrapper = inputsWrapper[0].dynamicCast<TimVXBackendWrapper>();
+
+        if (!inputWrapper->isTensor())
+        {
+            return Ptr<BackendNode>();
+        }
+
+         auto timVxInfo = reinterpret_cast<TimVXInfo *>(timVXInfo_);
+         CV_Assert(timVxInfo);
+         Ptr<TimVXGraph> tvGraph = timVxInfo->getGraph();
+         CV_Assert(tvGraph);
+         Ptr<tim::vx::Graph> graph = tvGraph->graph;
+
+        std::vector<int> inputsIndex, outputsIndex;
+        int input_index = -1, output_index = -1;
+
+        // Input
+        std::shared_ptr<tim::vx::Tensor> inputTensor = inputWrapper->getTensor();
+        input_index = tvGraph->getTensorIndex(inputTensor);
+        if (input_index == -1)
+            return Ptr<BackendNode>();
+
+        inputsIndex.push_back(input_index);
+
+        Ptr<tim::vx::Quantization> inputQuant = inputWrapper->getTensorQuantization();
+
+        tim::vx::QuantType quanType = inputQuant->Type();
+        CV_Assert(quanType == tim::vx::QuantType::ASYMMETRIC);
+
+        std::vector<float> scales = inputQuant->Scales();
+        std::vector<int32_t> zeropoints = inputQuant->ZeroPoints();
+        CV_Assert(!scales.empty() && !zeropoints.empty());
+        int input_zp = int(zeropoints[0]);
+        float input_scale = scales[0];
+
+        float  tmpOut_sc = input_scale/scale;
+        int tmpOut_zp = int(shift + scale * input_zp);
+
+        // Output
+        Ptr<TimVXBackendWrapper> outputWrapper = outputsWrapper[0].dynamicCast<TimVXBackendWrapper>();
+        Ptr<tim::vx::Quantization> outputQuant = Ptr<tim::vx::Quantization>(
+                new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, tmpOut_sc, tmpOut_zp));
+
+        if (isLast)
+        {
+            auto shapeType = getShapeTypeFromMat(outputWrapper->getMat());
+
+            // For Graph Output tensor, we need to set tensor shape before createTensor().
+            outputWrapper->setTensorShape(shapeType);
+            outputWrapper->createTensor(graph, tim::vx::TensorAttribute::OUTPUT, outputQuant);
+        }
+        else
+        {
+            outputWrapper->createTensor(graph, tim::vx::TensorAttribute::TRANSIENT, outputQuant);
+        }
+        output_index = tvGraph->addWrapper(outputWrapper);
+        outputsIndex.push_back(output_index);
+
+        std::shared_ptr<tim::vx::Operation> tvRequantize = graph->CreateOperation<tim::vx::ops::DataConvert>();
+
+        Ptr<TimVXBackendNode> tvBackendNode = new TimVXBackendNode(tvGraph, tvRequantize, inputsIndex, outputsIndex);
+
+        return tvBackendNode;
+#endif  // HAVE_TIMVX
+        return Ptr<BackendNode>();
+    }
+
     void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
     {
         CV_TRACE_FUNCTION();

modules/dnn/src/int8layers/softmax_layer.cpp

@@ -4,6 +4,7 @@
 
 #include "../precomp.hpp"
 #include "layers_common.hpp"
+#include "../op_timvx.hpp"
 
 #include <algorithm>
 #include <stdlib.h>
@@ -16,11 +17,17 @@ namespace dnn
 class SoftMaxLayerInt8Impl CV_FINAL : public SoftmaxLayerInt8
 {
 public:
+    float input_sc;
+    int input_zp;
 
     SoftMaxLayerInt8Impl(const LayerParams& params)
     {
         axisRaw = params.get<int>("axis", 1);
         logSoftMax = params.get<bool>("log_softmax", false);
+
+        input_sc = params.get<float>("input_scale");
+        input_zp = params.get<int>("input_zeropoint");
+
         output_sc = params.get<float>("scales");
         output_zp = params.get<int>("zeropoints");
         setParamsFrom(params);
@@ -41,7 +48,8 @@ public:
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
-        return backendId == DNN_BACKEND_OPENCV;
+        return backendId == DNN_BACKEND_OPENCV ||
+            (backendId == DNN_BACKEND_TIMVX && haveTimVX());
     }
 
     virtual bool tryFuse(Ptr<Layer>& top) CV_OVERRIDE
@@ -50,6 +58,102 @@ public:
         return !dequantize_layer.empty() && preferableTarget != DNN_TARGET_OPENCL_FP16;
     }
 
+   virtual Ptr<BackendNode> initTimVX(void* timVXInfo_,
+                                       const std::vector<Ptr<BackendWrapper> > &inputsWrapper,
+                                       const std::vector<Ptr<BackendWrapper> > &outputsWrapper,
+                                       bool isLast) CV_OVERRIDE
+    {
+#ifdef HAVE_TIMVX
+        // tvGraph Initialization.
+        auto timVxInfo = reinterpret_cast<TimVXInfo *>(timVXInfo_);
+        CV_Assert(timVxInfo);
+        Ptr<TimVXGraph> tvGraph = timVxInfo->getGraph();
+        CV_Assert(tvGraph);
+        Ptr<tim::vx::Graph> graph = tvGraph->graph;
+
+        std::vector<int> inputsIndex, outputsIndex;
+        int input_index, output_index;
+
+        // input Tensor
+        CV_Assert(inputsWrapper.size() == 1);
+        Ptr<TimVXBackendWrapper> inputWrapper = inputsWrapper[0].dynamicCast<TimVXBackendWrapper>();
+        const Mat &src = inputWrapper->getMat();
+
+        // convert axis from OpenCV NCHW toTimVX WHCN.
+        int axis = normalize_axis(axisRaw, src.dims);
+        int tvAxis = src.dims - 1 - axis;
+        if(tvAxis < 0)
+            tvAxis = 0; // default value is 0.
+
+        if (inputWrapper->isTensor())
+        {
+            input_index = tvGraph->getTensorIndex(inputWrapper->getTensor());
+            if (input_index == -1)
+            {
+                // Copy To New inputWrapper
+                Mat tmp = inputWrapper->getMat();
+                inputWrapper = Ptr<TimVXBackendWrapper>(new TimVXBackendWrapper(tmp));
+            }
+        }
+
+        if (!inputWrapper->isTensor())
+        {
+            Ptr<tim::vx::Quantization> tvInputQuant = Ptr<tim::vx::Quantization>(
+                    new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, input_sc, input_zp));
+            inputWrapper->createTensor(graph,tim::vx::TensorAttribute::INPUT, tvInputQuant);
+            input_index = tvGraph->addWrapper(inputWrapper);
+        }
+        inputsIndex.push_back(input_index);
+
+        // output tensor
+        CV_Assert(outputsWrapper.size() == 1);
+        Ptr<TimVXBackendWrapper> outputWrapper = outputsWrapper[0].dynamicCast<TimVXBackendWrapper>();
+        Mat dstMat = outputWrapper->getMat();
+        Ptr<tim::vx::Quantization> outputQuant = Ptr<tim::vx::Quantization>(
+                new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, output_sc, output_zp));
+
+        Ptr<tim::vx::Tensor> outputTensor;
+
+        if (isLast)
+        {
+            auto shapeType = getShapeTypeFromMat(outputWrapper->getMat());
+
+            // For Graph Output tensor, we need to set tensor shape before createTensor().
+            outputWrapper->setTensorShape(shapeType);
+            if (dstMat.type() == CV_32F)
+                outputWrapper->createTensor(graph, tim::vx::TensorAttribute::OUTPUT);
+            else
+                outputWrapper->createTensor(graph, tim::vx::TensorAttribute::OUTPUT, outputQuant);
+        }
+        else
+        {
+            if (dstMat.type() == CV_32F)
+                outputWrapper->createTensor(graph, tim::vx::TensorAttribute::TRANSIENT);
+            else
+                outputWrapper->createTensor(graph, tim::vx::TensorAttribute::TRANSIENT, outputQuant);
+        }
+        output_index = tvGraph->addWrapper(outputWrapper);
+        outputsIndex.push_back(output_index);
+
+        std::shared_ptr<tim::vx::Operation> tvSoftmax;
+
+        if (logSoftMax)
+        {
+            tvSoftmax = graph->CreateOperation<tim::vx::ops::LogSoftmax>(tvAxis);
+
+        }
+        else
+        {
+            tvSoftmax = graph->CreateOperation<tim::vx::ops::Softmax>(1.0f, tvAxis);
+        }
+
+        Ptr<TimVXBackendNode> tvBackendNode = new TimVXBackendNode(tvGraph, tvSoftmax, inputsIndex, outputsIndex);
+
+        return tvBackendNode;
+#endif  // HAVE_TIMVX
+        return Ptr<BackendNode>();
+    }
+
     void forward(InputArrayOfArrays inputs_arr, OutputArrayOfArrays outputs_arr, OutputArrayOfArrays internals_arr) CV_OVERRIDE
     {
         CV_TRACE_FUNCTION();

modules/dnn/src/layer.cpp

@@ -74,6 +74,16 @@ Ptr<BackendNode> Layer::initWebnn(const std::vector<Ptr<BackendWrapper>>& inputs
     return Ptr<BackendNode>();
 }
 
+Ptr<BackendNode> Layer::initTimVX(void* timVxInfo,
+                                  const std::vector<Ptr<BackendWrapper> > & inputsWrapper,
+                                  const std::vector<Ptr<BackendWrapper> > & outputsWrapper,
+                                  bool isLast)
+{
+    CV_Error(Error::StsNotImplemented, "TimVX pipeline of " + type +
+                                       " layers is not defined.");
+    return Ptr<BackendNode>();
+}
+
 Ptr<BackendNode> Layer::tryAttach(const Ptr<BackendNode>& node)
 {
     return Ptr<BackendNode>();

modules/dnn/src/layers/batch_norm_layer.cpp

@@ -409,6 +409,7 @@ public:
     {
         params.set("input_scale", scales[0][0]);
         params.set("input_zeropoint", zeropoints[0][0]);
+        params.set("eps", epsilon);
 
         params.blobs.clear();
         params.blobs.push_back(origin_weights);

modules/dnn/src/layers/concat_layer.cpp

@@ -48,6 +48,7 @@
 #include "../ie_ngraph.hpp"
 #include "../op_vkcom.hpp"
 #include "../op_webnn.hpp"
+#include "../op_timvx.hpp"
 
 #ifdef HAVE_OPENCL
 #include "opencl_kernels_dnn.hpp"
@@ -72,6 +73,9 @@ public:
         axis = params.get<int>("axis", 1);
         padding = params.get<bool>("padding", false);
         paddingValue = params.get<int>("padding_value", 0);
+
+        zeropoint = params.get<int>("zeropoints", 0);
+        scale = params.get<float>("scales", 1.0f);
     }
 
     virtual bool getMemoryShapes(const std::vector<MatShape> &inputs,
@@ -113,6 +117,21 @@ public:
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
+#ifdef HAVE_TIMVX
+        if (backendId == DNN_BACKEND_TIMVX && haveTimVX() && !padding)
+        {
+            if (axis == -1)
+                return false;
+            int len = this->type.length();
+            if (len <= 4)
+                return false;
+            if (this->type.substr(len - 4) == "Int8")
+                return true;
+            else
+                return false;
+        }
+#endif
+
 #ifdef HAVE_INF_ENGINE
         if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
             return true;
@@ -393,6 +412,86 @@ public:
     }
 #endif  // HAVE_DNN_NGRAPH
 
+#ifdef HAVE_TIMVX
+    virtual Ptr<BackendNode> initTimVX(void* timVXInfo_,
+                                       const std::vector<Ptr<BackendWrapper> > &inputsWrapper,
+                                       const std::vector<Ptr<BackendWrapper> > &outputsWrapper,
+                                       bool isLast) CV_OVERRIDE
+    {
+        // tvGraph Initialization.
+        auto timVxInfo = reinterpret_cast<TimVXInfo *>(timVXInfo_);
+        CV_Assert(timVxInfo);
+        Ptr<TimVXGraph> tvGraph = timVxInfo->getGraph();
+        CV_Assert(tvGraph);
+        Ptr<tim::vx::Graph> graph = tvGraph->graph;
+
+        Ptr<TimVXBackendWrapper> inputWrapper = inputsWrapper[0].dynamicCast<TimVXBackendWrapper>();
+        // convert axis from OpenCV NCHW toTimVX WHCN.
+        Mat blob0 = inputWrapper->getMat();
+
+        // TODO! support TimVX 5 dim in future.
+        if(blob0.dims >4)
+            return Ptr<TimVXBackendNode>();
+
+        int cAxis = normalize_axis(axis, blob0.dims);
+        int tvAxis = blob0.dims - 1 - cAxis;
+        CV_Assert(tvAxis>= 0);
+        std::vector<int> inputsIndex, outputsIndex;
+        int input_index = -1, output_index = -1;
+
+        // Input
+        Ptr<tim::vx::Quantization> tvQuant = Ptr<tim::vx::Quantization>(
+                new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, scale, zeropoint));
+
+        for (int i = 0; i<inputsWrapper.size(); i++)
+        {
+            inputWrapper = inputsWrapper[i].dynamicCast<TimVXBackendWrapper>();
+            if (inputWrapper->isTensor())
+            {
+                input_index = tvGraph->getTensorIndex(inputWrapper->getTensor());
+                if (input_index == -1)
+                {
+                    // Copy To New inputWrapper
+                    Mat tmp = inputWrapper->getMat();
+                    inputWrapper = Ptr<TimVXBackendWrapper>(new TimVXBackendWrapper(tmp));
+                }
+            }
+
+            if (!inputWrapper->isTensor())
+            {
+                inputWrapper->createTensor(graph,tim::vx::TensorAttribute::INPUT, tvQuant);
+                input_index = tvGraph->addWrapper(inputWrapper);
+            }
+            inputsIndex.push_back(input_index);
+        }
+
+        //Output
+        CV_Assert(outputsWrapper.size() == 1);
+        Ptr<TimVXBackendWrapper> outputWrapper = outputsWrapper[0].dynamicCast<TimVXBackendWrapper>();
+
+        if (isLast)
+        {
+            auto shapeType = getShapeTypeFromMat(outputWrapper->getMat());
+
+            // For Graph Output tensor, we need to set tensor shape before createTensor().
+            outputWrapper->setTensorShape(shapeType);
+            outputWrapper->createTensor(graph, tim::vx::TensorAttribute::OUTPUT, tvQuant);
+        }
+        else
+        {
+            outputWrapper->createTensor(graph, tim::vx::TensorAttribute::TRANSIENT, tvQuant);
+        }
+        output_index = tvGraph->addWrapper(outputWrapper);
+        outputsIndex.push_back(output_index);
+
+        std::shared_ptr<tim::vx::Operation> tvConcate = graph->CreateOperation<tim::vx::ops::Concat>(tvAxis, inputsWrapper.size());
+
+        Ptr<TimVXBackendNode> tvBackendNode = new TimVXBackendNode(tvGraph, tvConcate, inputsIndex, outputsIndex);
+
+        return tvBackendNode;
+    }
+#endif // HAVE_TIMVX
+
     virtual bool tryQuantize(const std::vector<std::vector<float> > &scales,
                              const std::vector<std::vector<int> > &zeropoints, LayerParams& params) CV_OVERRIDE
     {
@@ -416,6 +515,8 @@ public:
     }
 #endif
 
+    int zeropoint;
+    float scale;
 };
 
 Ptr<ConcatLayer> ConcatLayer::create(const LayerParams& params)

modules/dnn/src/layers/convolution_layer.cpp

@@ -2168,6 +2168,7 @@ public:
         float inputScale = scales[0][0], outputScale = scales[1][0];
         int inputZp = zeropoints[0][0];
         params.set("input_zeropoint", inputZp);
+        params.set("input_scale", inputScale);
 
         Mat weightsQuantized(weightsMat.rows, weightsMat.cols, CV_8S);
         Mat biasQuantized(1, numOutput, CV_32S);

modules/dnn/src/layers/elementwise_layers.cpp

@@ -496,6 +496,9 @@ struct ReLUFunctor : public BaseFunctor
             params.blobs.clear();
             params.blobs.push_back(lookUpTable);
         }
+        params.set("input_scale", scales[0][0]);
+        params.set("input_zeropoint", zeropoints[0][0]);
+        params.set("slope", slope);
         return true;
     }
 
@@ -635,6 +638,8 @@ struct ReLU6Functor : public BaseFunctor
     bool tryQuantize(const std::vector<std::vector<float> > &scales,
                      const std::vector<std::vector<int> > &zeropoints, LayerParams& params)
     {
+        params.set("input_scale", scales[0][0]);
+        params.set("input_zeropoint", zeropoints[0][0]);
         return true;
     }
 
@@ -704,6 +709,8 @@ struct BaseDefaultFunctor : public BaseFunctor
         }
         params.blobs.clear();
         params.blobs.push_back(lookUpTable);
+        params.set("input_scale", scales[0][0]);
+        params.set("input_zeropoint", zeropoints[0][0]);
         return true;
     }
 

modules/dnn/src/layers/eltwise_layer.cpp

@@ -875,6 +875,8 @@ public:
     virtual bool tryQuantize(const std::vector<std::vector<float> > &scales,
                              const std::vector<std::vector<int> > &zeropoints, LayerParams& params) CV_OVERRIDE
     {
+        params.set("input_scales", DictValue::arrayReal(scales[0].data(), scales[0].size()));
+        params.set("input_zeropoints", DictValue::arrayInt(zeropoints[0].data(), zeropoints[0].size()));
         if (op == SUM)
         {
             std::vector<float> newCoeffs;
@@ -897,7 +899,6 @@ public:
             newCoeffs[0] /= scales[1][0];
             params.set("coeff", DictValue::arrayReal(newCoeffs.data(), newCoeffs.size()));
             params.set("offset", zeropoints[1][0]);
-            params.set("input_zeropoints", DictValue::arrayInt(zeropoints[0].data(), zeropoints[0].size()));
             return true;
         }
         return op == MAX;

modules/dnn/src/layers/fully_connected_layer.cpp

@@ -642,6 +642,8 @@ public:
         params.blobs.push_back(weightsQuantized.reshape(1, shape(blobs[0])));
         params.blobs.push_back(biasQuantized);
         params.blobs.push_back(outputMultiplier);
+        params.set("input_scale", inputScale);
+        params.set("input_zeropoint", inputZp);
         return true;
     }
 

modules/dnn/src/layers/permute_layer.cpp

@@ -47,6 +47,7 @@
 #include "../ie_ngraph.hpp"
 #include "../op_vkcom.hpp"
 #include "../op_webnn.hpp"
+#include "../op_timvx.hpp"
 
 #include <float.h>
 #include <algorithm>
@@ -108,6 +109,9 @@ public:
             _order.push_back(currentOrder);
         }
 
+        zeropoint = params.get<int>("zeropoints", 0);
+        scale = params.get<float>("scales", 1.0f);
+
         setParamsFrom(params);
         checkNeedForPermutation();
     }
@@ -122,6 +126,20 @@ public:
             return true;
         }
 #endif
+
+#ifdef HAVE_TIMVX
+        if (backendId == DNN_BACKEND_TIMVX && haveTimVX())
+        {
+            int len = this->type.length();
+            if (len <= 4)
+                return false;
+
+            if (this->type.substr(len - 4) == "Int8")
+                return true;
+            else
+                return false;
+        }
+#endif
         return backendId == DNN_BACKEND_OPENCV ||
                backendId == DNN_BACKEND_CUDA ||
                backendId == DNN_BACKEND_WEBNN ||
@@ -471,12 +489,120 @@ public:
     }
 #endif // HAVE_VULKAN
 
+#ifdef HAVE_TIMVX
+  virtual Ptr<BackendNode> initTimVX(void* timVXInfo_,
+                                       const std::vector<Ptr<BackendWrapper> > &inputsWrapper,
+                                       const std::vector<Ptr<BackendWrapper> > &outputsWrapper,
+                                       bool isLast) CV_OVERRIDE
+    {
+        // tvGraph Initialization.
+        auto timVxInfo = reinterpret_cast<TimVXInfo *>(timVXInfo_);
+        CV_Assert(timVxInfo);
+        Ptr<TimVXGraph> tvGraph = timVxInfo->getGraph();
+        CV_Assert(tvGraph);
+        Ptr<tim::vx::Graph> graph = tvGraph->graph;
+
+        std::vector<int> inputsIndex, outputsIndex;
+        int input_index = -1, output_index = -1;
+
+        if (outputsWrapper.size() != 1) // only work for single outputBlob
+            return Ptr<BackendNode>();
+
+        // Input
+        Ptr<TimVXBackendWrapper> inputWrapper = inputsWrapper[0].dynamicCast<TimVXBackendWrapper>();
+        if (inputWrapper->isTensor())
+        {
+            input_index = tvGraph->getTensorIndex(inputWrapper->getTensor());
+            if (input_index == -1)
+            {
+                // Copy To New inputWrapper
+                Mat tmp = inputWrapper->getMat();
+                inputWrapper = Ptr<TimVXBackendWrapper>(new TimVXBackendWrapper(tmp));
+            }
+        }
+
+        if (!inputWrapper->isTensor())
+        {
+            Ptr<tim::vx::Quantization> tvInputQuant = Ptr<tim::vx::Quantization>(
+                    new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, scale, zeropoint));
+            inputWrapper->createTensor(graph,tim::vx::TensorAttribute::INPUT, tvInputQuant);
+            input_index = tvGraph->addWrapper(inputWrapper);
+        }
+        inputsIndex.push_back(input_index);
+
+        //Output
+        Ptr<TimVXBackendWrapper> outputWrapper = outputsWrapper[0].dynamicCast<TimVXBackendWrapper>();
+        // output has the same quantized attrib.
+        Ptr<tim::vx::Quantization> outputQuant = inputWrapper->getTensorQuantization();
+
+        if (isLast)
+        {
+            auto shapeType = getShapeTypeFromMat(outputWrapper->getMat());
+
+            // For Graph Output tensor, we need to set tensor shape before createTensor().
+            outputWrapper->setTensorShape(shapeType);
+            outputWrapper->createTensor(graph, tim::vx::TensorAttribute::OUTPUT, outputQuant);
+        }
+        else
+        {
+            outputWrapper->createTensor(graph, tim::vx::TensorAttribute::TRANSIENT, outputQuant);
+        }
+        output_index = tvGraph->addWrapper(outputWrapper);
+        outputsIndex.push_back(output_index);
+
+        std::vector<uint32_t> tvOrder;
+        if (getOrderWHCN(tvOrder))
+        {
+            std::shared_ptr<tim::vx::Operation> tvPermute = graph->CreateOperation<tim::vx::ops::Transpose>(tvOrder);
+
+            Ptr<TimVXBackendNode> tvBackendNode = new TimVXBackendNode(tvGraph, tvPermute, inputsIndex, outputsIndex);
+
+            return tvBackendNode;
+        }
+        else
+        {
+            return Ptr<BackendNode>();
+        }
+    }
+#endif // HAVE_TIMVX
+
     virtual bool tryQuantize(const std::vector<std::vector<float> > &scales,
                              const std::vector<std::vector<int> > &zeropoints, LayerParams& params) CV_OVERRIDE
     {
         return true;
     }
 
+    // convert OpenCV NCHW order to WHCN order.
+    bool getOrderWHCN(std::vector<uint32_t>& orderWHCN)
+    {
+        std::map<int, int> lookup;
+        int orderLen = _order.size();
+        if (orderLen <2)
+            return false;
+        orderWHCN.assign(_order.begin(), _order.end());
+
+        if (orderLen == 2)
+        {
+            return true;
+        }
+        else if (orderLen >= 3)
+        {
+            for (int i = 0; i < orderLen; i++)
+            {
+                lookup[i] = orderLen - i - 1;
+            }
+
+            for (int i = 0; i < orderLen; i++)
+            {
+                orderWHCN[i] = lookup[_order[i]];
+            }
+            std::reverse(orderWHCN.begin(), orderWHCN.end());
+            return true;
+        }
+        else
+            return false;
+    }
+
     size_t _count;
     std::vector<size_t> _order;
 
@@ -492,6 +618,8 @@ public:
 #endif
 
     size_t _numAxes;
+    int zeropoint;
+    float scale;
 };
 
 Ptr<PermuteLayer> PermuteLayer::create(const LayerParams &params)

modules/dnn/src/layers/pooling_layer.cpp

@@ -272,6 +272,17 @@ public:
                 return true;
             }
         }
+        else if (backendId == DNN_BACKEND_TIMVX)
+        {
+#ifdef HAVE_TIMVX
+            if (kernel_size.size() == 3)
+            {
+                // fallback to CPU implementation.
+                preferableTarget = DNN_TARGET_CPU;
+            }
+#endif
+            return false;
+        }
         return false;
     }
 

modules/dnn/src/layers/reshape_layer.cpp

@@ -46,6 +46,7 @@
 #include "../op_inf_engine.hpp"
 #include "../ie_ngraph.hpp"
 #include "../op_webnn.hpp"
+#include "../op_timvx.hpp"
 
 #include <opencv2/dnn/shape_utils.hpp>
 
@@ -167,6 +168,9 @@ public:
         hasDynamicShapes = params.get<bool>("has_dynamic_shapes", false);
         shapesInitialized = !hasDynamicShapes;
 
+        zeropoint = params.get<int>("zeropoints", 0);
+        scale = params.get<float>("scales", 1.0f);
+
         CV_Assert(numAxes >= -1);
         newShapeRange = (numAxes == -1) ? Range(axis, INT_MAX) : Range(axis, axis + numAxes);
 
@@ -202,6 +206,18 @@ public:
 
     virtual bool supportBackend(int backendId) CV_OVERRIDE
     {
+        if (backendId == DNN_BACKEND_TIMVX && haveTimVX())
+        {
+            int len = this->type.length();
+            if (len <= 4)
+                return false;
+
+            if (this->type.substr(len - 4) == "Int8")
+                return true;
+            else
+                return false;
+        }
+
 #ifdef HAVE_INF_ENGINE
         if (backendId == DNN_BACKEND_INFERENCE_ENGINE_NGRAPH)
             return true;
@@ -348,6 +364,99 @@ public:
     }
 #endif
 
+    virtual Ptr<BackendNode> initTimVX(void* timVXInfo_,
+                                       const std::vector<Ptr<BackendWrapper> > &inputsWrapper,
+                                       const std::vector<Ptr<BackendWrapper> > &outputsWrapper,
+                                       bool isLast) CV_OVERRIDE
+    {
+#ifdef HAVE_TIMVX
+        // tvGraph Initialization.
+        auto timVxInfo = reinterpret_cast<TimVXInfo *>(timVXInfo_);
+        CV_Assert(timVxInfo);
+        Ptr<TimVXGraph> tvGraph = timVxInfo->getGraph();
+        CV_Assert(tvGraph);
+        Ptr<tim::vx::Graph> graph = tvGraph->graph;
+
+        std::vector<int> inputsIndex, outputsIndex;
+        int input_index = -1, output_index = -1;
+
+        int reshapeNum = 0;
+        Ptr<TimVXBackendWrapper> tmpWrapper, inputWrapper, outputWrapper;
+        for (size_t i = 0; i < outputsWrapper.size(); i++)
+        {
+            tmpWrapper = inputsWrapper[i].dynamicCast<TimVXBackendWrapper>();
+            Mat srcBlob = tmpWrapper->getMat();
+
+            tmpWrapper = outputsWrapper[i].dynamicCast<TimVXBackendWrapper>();
+            Mat dstBlob = tmpWrapper->getMat();
+            if (dstBlob.data != srcBlob.data)
+            {
+                reshapeNum++;
+                inputWrapper = inputsWrapper[i].dynamicCast<TimVXBackendWrapper>();
+                outputWrapper = outputsWrapper[i].dynamicCast<TimVXBackendWrapper>();
+            }
+        }
+
+        // Only work for single reshape Mat
+        if (reshapeNum != 1)
+        {
+          return Ptr<BackendNode>();
+        }
+
+        // Input
+        if (inputWrapper->isTensor())
+        {
+          input_index = tvGraph->getTensorIndex(inputWrapper->getTensor());
+          if (input_index == -1)
+          {
+              // Copy To New inputWrapper
+              Mat tmp = inputWrapper->getMat();
+              inputWrapper = Ptr<TimVXBackendWrapper>(new TimVXBackendWrapper(tmp));
+          }
+        }
+
+        if (!inputWrapper->isTensor() || input_index == -1)
+        {
+            Ptr<tim::vx::Quantization> tvInputQuant = Ptr<tim::vx::Quantization>(
+                    new tim::vx::Quantization(tim::vx::QuantType::ASYMMETRIC, scale, zeropoint));
+          inputWrapper->createTensor(graph,tim::vx::TensorAttribute::INPUT,tvInputQuant);
+          input_index = tvGraph->addWrapper(inputWrapper);
+        }
+        inputsIndex.push_back(input_index);
+
+        //Output
+        // Output Tensor has the same quantized attrib as Input Tesor.
+        Ptr<tim::vx::Quantization> outputQuant = inputWrapper->getTensorQuantization();
+        if (isLast)
+        {
+            auto shapeType = getShapeTypeFromMat(outputWrapper->getMat());
+
+            // For Graph Output tensor, we need to set tensor shape before createTensor().
+            outputWrapper->setTensorShape(shapeType);
+            outputWrapper->createTensor(graph, tim::vx::TensorAttribute::OUTPUT, outputQuant);
+        }
+        else
+        {
+            outputWrapper->createTensor(graph, tim::vx::TensorAttribute::TRANSIENT, outputQuant);
+        }
+        output_index = tvGraph->addWrapper(outputWrapper);
+        outputsIndex.push_back(output_index);
+
+        // generate output shape.
+        MatShape outputShape = shape(outputWrapper->getMat());
+        // reverse shape, from NCHW to WHCN
+        std::reverse(outputShape.begin(), outputShape.end());
+        std::vector<uint32_t> tvShape(outputShape.begin(), outputShape.end());
+
+        std::shared_ptr<tim::vx::Operation> tvReshape = graph->CreateOperation<tim::vx::ops::Reshape>(tvShape);
+
+        Ptr<TimVXBackendNode> tvBackendNode = new TimVXBackendNode(tvGraph, tvReshape, inputsIndex, outputsIndex);
+
+        return tvBackendNode;
+#endif  // HAVE_TIMVX
+        return Ptr<BackendNode>();
+    }
+
     virtual bool tryQuantize(const std::vector<std::vector<float> > &scales,
                              const std::vector<std::vector<int> > &zeropoints, LayerParams& params) CV_OVERRIDE
     {
@@ -360,6 +469,8 @@ private:
     std::vector<int> inputIndices; // Which axes from input are needed to compute correct output shape
     bool hasDynamicShapes;
     bool shapesInitialized;
+    float scale;
+    int zeropoint;
 };
 
 Ptr<ReshapeLayer> ReshapeLayer::create(const LayerParams& params)

modules/dnn/src/layers/softmax_layer.cpp

@@ -390,6 +390,8 @@ public:
         }
         params.blobs.clear();
         params.blobs.push_back(lookUpTable);
+        params.set("input_scale", inpScale);
+        params.set("input_zeropoint", zeropoints[0][0]);
         return true;
     }
 

modules/dnn/src/legacy_backend.cpp

@@ -12,6 +12,7 @@
 #include "op_vkcom.hpp"
 #include "op_cuda.hpp"
 #include "op_webnn.hpp"
+#include "op_timvx.hpp"
 
 namespace cv {
 namespace dnn {
@@ -110,6 +111,13 @@ Ptr<BackendWrapper> wrapMat(int backendId, int targetId, cv::Mat& m)
             CV_Assert(IS_DNN_CUDA_TARGET(targetId));
         }
 #endif
+    }
+    else if (backendId == DNN_BACKEND_TIMVX)
+    {
+        CV_Assert(haveTimVX());
+#ifdef HAVE_TIMVX
+        return Ptr<BackendWrapper>(new TimVXBackendWrapper(m));
+#endif  // HAVE_TIMVX
     }
     else
         CV_Error(Error::StsNotImplemented, "Unknown backend identifier");

modules/dnn/src/net_impl.cpp

@@ -133,6 +133,9 @@ void Net::Impl::setUpNet(const std::vector<LayerPin>& blobsToKeep_)
               preferableTarget == DNN_TARGET_VULKAN);
     CV_Assert(preferableBackend != DNN_BACKEND_CUDA ||
               IS_DNN_CUDA_TARGET(preferableTarget));
+    CV_Assert(preferableBackend != DNN_BACKEND_TIMVX ||
+              preferableTarget == DNN_TARGET_NPU);
+
     if (!netWasAllocated || this->blobsToKeep != blobsToKeep_)
     {
         if (preferableBackend == DNN_BACKEND_OPENCV && IS_DNN_OPENCL_TARGET(preferableTarget))
@@ -179,6 +182,12 @@ void Net::Impl::setUpNet(const std::vector<LayerPin>& blobsToKeep_)
             preferableTarget = DNN_TARGET_CPU;
         }
 
+        if (preferableBackend == DNN_BACKEND_TIMVX && !haveTimVX())
+        {
+            preferableBackend = DNN_BACKEND_OPENCV;
+            preferableTarget = DNN_TARGET_CPU;
+        }
+
         clear();
 
         if (hasDynamicShapes)
@@ -515,7 +524,7 @@ void Net::Impl::allocateLayer(int lid, const LayersShapesMap& layersShapes)
         ld.outputBlobsWrappers[i] = wrap(ld.outputBlobs[i]);
 
     /* CUDA backend has its own system for internal blobs; we don't need these */
-    ld.internalBlobsWrappers.resize((preferableBackend == DNN_BACKEND_CUDA) ? 0 : ld.internals.size());
+    ld.internalBlobsWrappers.resize((preferableBackend == DNN_BACKEND_CUDA || preferableBackend == DNN_BACKEND_TIMVX) ? 0 : ld.internals.size());
     for (int i = 0; i < ld.internalBlobsWrappers.size(); ++i)
         ld.internalBlobsWrappers[i] = wrap(ld.internals[i]);
 
@@ -814,6 +823,10 @@ void Net::Impl::forwardLayer(LayerData& ld)
             {
                 forwardWebnn(ld.outputBlobsWrappers, node, isAsync);
             }
+            else if (preferableBackend == DNN_BACKEND_TIMVX)
+            {
+                forwardTimVX(ld.outputBlobsWrappers, node);
+            }
 #ifdef HAVE_VULKAN
             else if (preferableBackend == DNN_BACKEND_VKCOM)
             {
@@ -1568,7 +1581,7 @@ string Net::Impl::dump(bool forceAllocation) const
             prevNode = itBackend->second;
         }
     }
-    std::vector<string> colors = { "#ffffb3", "#fccde5", "#8dd3c7", "#bebada", "#80b1d3", "#fdb462", "#ff4848", "#b35151", "#b266ff" };
+    std::vector<string> colors = { "#ffffb3", "#fccde5", "#8dd3c7", "#bebada", "#80b1d3", "#fdb462", "#ff4848", "#b35151", "#b266ff", "#b266ff", "#3cb371"};
     string backend;
     switch (prefBackend)
     {
@@ -1580,9 +1593,8 @@ string Net::Impl::dump(bool forceAllocation) const
     case DNN_BACKEND_OPENCV: backend = "OCV/"; break;
     case DNN_BACKEND_VKCOM: backend = "VULKAN/"; break;
     case DNN_BACKEND_CUDA: backend = "CUDA/"; break;
-    case DNN_BACKEND_WEBNN:
+    case DNN_BACKEND_WEBNN: backend = "WEBNN/"; break;
-        backend = "WEBNN/";
+    case DNN_BACKEND_TIMVX: backend = "TIMVX/"; break;
-        break;
         // don't use default:
     }
     out << "digraph G {\n";
@@ -1767,6 +1779,10 @@ string Net::Impl::dump(bool forceAllocation) const
             out << "CUDA_FP16";
             colorId = 6;
             break;
+        case DNN_TARGET_NPU:
+            out << "NPU";
+            colorId = 9;
+            break;
             // don't use default:
         }
         CV_Assert(colorId < colors.size());

modules/dnn/src/net_impl.hpp

@@ -11,6 +11,7 @@
 #include "op_vkcom.hpp"
 #include "op_cuda.hpp"
 #include "op_webnn.hpp"
+#include "op_timvx.hpp"
 
 #include <opencv2/dnn/shape_utils.hpp>
 #include <opencv2/imgproc.hpp>
@@ -152,6 +153,14 @@ struct Net::Impl : public detail::NetImplBase
     void initVkComBackend();
 #endif
 
+#ifdef HAVE_TIMVX
+    // Create timVxInfo for reserve tvGraphList.
+    TimVXInfo timVxInfo = TimVXInfo();
+    void tvUpdateConfictMap(int graphIndex, LayerData& ld, std::vector<std::vector<int> >& graphConflictMap);
+    void tvConvertToOutputNode(const LayerData& ld, Ptr<TimVXBackendWrapper>& targetWrap);
+    void initTimVXBackend();
+#endif
+
 #ifdef HAVE_CUDA
     struct CudaInfo_t
     {

modules/dnn/src/net_impl_backend.cpp

@@ -74,6 +74,12 @@ Ptr<BackendWrapper> Net::Impl::wrap(Mat& host)
             default:
                 CV_Assert(IS_DNN_CUDA_TARGET(preferableTarget));
             }
+#endif
+        }
+        else if (preferableBackend == DNN_BACKEND_TIMVX)
+        {
+#ifdef HAVE_TIMVX
+            return Ptr<BackendWrapper>(new TimVXBackendWrapper(baseBuffer, host));
 #endif
         }
         else
@@ -131,6 +137,14 @@ void Net::Impl::initBackend(const std::vector<LayerPin>& blobsToKeep_)
         initCUDABackend(blobsToKeep_);
 #else
         CV_Error(Error::StsNotImplemented, "This OpenCV version is built without support of CUDA/CUDNN");
+#endif
+    }
+    else if (preferableBackend == DNN_BACKEND_TIMVX)
+    {
+#ifdef HAVE_TIMVX
+        initTimVXBackend();
+#else
+        CV_Error(Error::StsNotImplemented, "This OpenCV version is built without support of TimVX");
 #endif
     }
     else
@@ -145,9 +159,9 @@ void Net::Impl::setPreferableBackend(int backendId)
     if (backendId == DNN_BACKEND_DEFAULT)
         backendId = (Backend)getParam_DNN_BACKEND_DEFAULT();
 
-    if (netWasQuantized && backendId != DNN_BACKEND_OPENCV)
+    if (netWasQuantized && backendId != DNN_BACKEND_OPENCV && backendId != DNN_BACKEND_TIMVX)
     {
-        CV_LOG_WARNING(NULL, "DNN: Only default backend supports quantized networks");
+        CV_LOG_WARNING(NULL, "DNN: Only default and TIMVX backends support quantized networks");
         backendId = DNN_BACKEND_OPENCV;
     }
 
@@ -166,9 +180,9 @@ void Net::Impl::setPreferableBackend(int backendId)
 void Net::Impl::setPreferableTarget(int targetId)
 {
     if (netWasQuantized && targetId != DNN_TARGET_CPU &&
-        targetId != DNN_TARGET_OPENCL && targetId != DNN_TARGET_OPENCL_FP16)
+        targetId != DNN_TARGET_OPENCL && targetId != DNN_TARGET_OPENCL_FP16 && targetId != DNN_TARGET_NPU)
     {
-        CV_LOG_WARNING(NULL, "DNN: Only CPU and OpenCL/OpenCL FP16 target is supported by quantized networks");
+        CV_LOG_WARNING(NULL, "DNN: Only CPU, OpenCL/OpenCL FP16 and NPU targets are supported by quantized networks");
         targetId = DNN_TARGET_CPU;
     }
 

modules/dnn/src/net_impl_fuse.cpp

@@ -38,7 +38,8 @@ void Net::Impl::fuseLayers(const std::vector<LayerPin>& blobsToKeep_)
 
     if(!fusion || (preferableBackend != DNN_BACKEND_OPENCV &&
                     preferableBackend != DNN_BACKEND_CUDA &&
-                    preferableBackend != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH))
+                    preferableBackend != DNN_BACKEND_INFERENCE_ENGINE_NGRAPH &&
+                    preferableBackend != DNN_BACKEND_TIMVX))
        return;
 
 #if 0  // FIXIT mode without fusion is broken due to unsupported layers and handling of "custom" nodes

modules/dnn/src/onnx/onnx_importer.cpp

@@ -3271,6 +3271,7 @@ void ONNXImporter::parseQConv(LayerParams& layerParams, const opencv_onnx::NodeP
     layerParams.type = "ConvolutionInt8";
     layerParams.set("num_output", outCn);
     layerParams.set("input_zeropoint", inp_zp.at<int8_t>(0));
+    layerParams.set("input_scale",inp_sc.at<float>(0));
     layerParams.blobs.push_back(weights);
     layerParams.blobs.push_back(biasFused);
     layerParams.blobs.push_back(outputMultiplier);
@@ -3310,6 +3311,9 @@ void ONNXImporter::parseQMatMul(LayerParams& layerParams, const opencv_onnx::Nod
     layerParams.type = "InnerProductInt8";
     layerParams.set("num_output", outCn);
     layerParams.set("axis", firstInpDims - secondInpDims + 1);
+    layerParams.set("input_scale", inp_sc.at<float>(0));
+    layerParams.set("input_zeropoint", inp_zp.at<int8_t>(0));
+
     layerParams.blobs.push_back(weights);
     layerParams.blobs.push_back(bias);
     layerParams.blobs.push_back(outputMultiplier);
@@ -3380,6 +3384,7 @@ void ONNXImporter::parseQEltwise(LayerParams& layerParams, const opencv_onnx::No
             rescaleParams.set("depth", CV_8S);
             rescaleParams.set("scale", scale);
             rescaleParams.set("shift", shift);
+            rescaleParams.set("isEltwise", true);
             addLayer(rescaleParams, node_proto);
             return;
         }
@@ -3428,7 +3433,6 @@ void ONNXImporter::parseQEltwise(LayerParams& layerParams, const opencv_onnx::No
                 Mat blob_dequantized;
                 blob.convertTo(blob_dequantized, CV_32F, inp_scales[1], -(inp_scales[1] * inp_zps[1]));
                 layerParams.blobs.push_back(blob_dequantized);
-                layerParams.set("input_scales", DictValue::arrayReal(inp_scales.data(), inp_scales.size()));
             }
         }
     }
@@ -3443,9 +3447,9 @@ void ONNXImporter::parseQEltwise(LayerParams& layerParams, const opencv_onnx::No
     {
         layerParams.type = "ScaleInt8";
         layerParams.set("bias_term", op == "sum");
-        layerParams.set("input_scales", DictValue::arrayReal(inp_scales.data(), inp_scales.size()));
     }
 
+    layerParams.set("input_scales", DictValue::arrayReal(inp_scales.data(), inp_scales.size()));
     layerParams.set("input_zeropoints", DictValue::arrayInt(inp_zps.data(), inp_zps.size()));
     addLayer(layerParams, node_proto);
 }
@@ -3471,6 +3475,9 @@ void ONNXImporter::parseQLeakyRelu(LayerParams& layerParams, const opencv_onnx::
     }
 
     layerParams.type = "ReLUInt8";
+    layerParams.set("input_scale", inp_sc);
+    layerParams.set("input_zeropoint", inp_zp);
+    layerParams.set("slope", slope);
     layerParams.blobs.push_back(lookUpTable);
     addLayer(layerParams, node_proto);
 }
@@ -3495,6 +3502,8 @@ void ONNXImporter::parseQSigmoid(LayerParams& layerParams, const opencv_onnx::No
     }
 
     layerParams.type = "SigmoidInt8";
+    layerParams.set("input_scale", inp_sc);
+    layerParams.set("input_zeropoint", inp_zp);
     layerParams.blobs.push_back(lookUpTable);
     addLayer(layerParams, node_proto);
 }
@@ -3548,6 +3557,7 @@ void ONNXImporter::parseQConcat(LayerParams& layerParams, const opencv_onnx::Nod
                 rescaleParams.set("depth", CV_8S);
                 rescaleParams.set("scale", scale);
                 rescaleParams.set("shift", shift);
+                rescaleParams.set("isEltwise", false);
 
                 opencv_onnx::NodeProto proto;
                 proto.add_input(node_proto.input(i));

modules/dnn/src/op_timvx.cpp

@@ -0,0 +1,931 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2019-2021, Shenzhen Institute of Artificial Intelligence and
+// Robotics for Society, all rights reserved.
+// Third party copyrights are property of their respective owners.
+
+#include "precomp.hpp"
+#include <opencv2/dnn/shape_utils.hpp>
+#include "op_timvx.hpp"
+#include "net_impl.hpp"
+
+namespace cv
+{
+namespace dnn
+{
+#ifdef HAVE_TIMVX
+
+CV__DNN_INLINE_NS_BEGIN
+
+// update all comsumer
+void Net::Impl::tvUpdateConfictMap(int graphIndex, LayerData& ld, std::vector<std::vector<int> >& graphConflictMap)
+{
+    if (ld.consumers.empty())
+        return;
+    for (int i = 0; i < ld.consumers.size(); i++)
+    {
+        LayerData &consumerld = layers[ld.consumers[i].lid];
+        std::vector<int>::iterator it = std::find(graphConflictMap[ld.consumers[i].lid].begin(),
+                                                    graphConflictMap[ld.consumers[i].lid].end(), graphIndex);
+
+        if (it == graphConflictMap[ld.consumers[i].lid].end())
+        {
+            graphConflictMap[ld.consumers[i].lid].push_back(graphIndex);
+            tvUpdateConfictMap(graphIndex, consumerld, graphConflictMap);
+        }
+        else
+            continue;
+    }
+}
+
+// Convert TRANSIENT to OUTPUT
+void Net::Impl::tvConvertToOutputNode(const LayerData& ld, Ptr<TimVXBackendWrapper>& targetWrap)
+{
+    // find right layer.
+    for (auto& inputLayerId : ld.inputLayersId)
+    {
+        LayerData &inputld = layers[inputLayerId];
+        auto itWrap = std::find(inputld.outputBlobsWrappers.begin(),
+                                inputld.outputBlobsWrappers.end(), targetWrap);
+        if (itWrap != inputld.outputBlobsWrappers.end())
+        {
+            auto outputWrap = (*itWrap).dynamicCast<TimVXBackendWrapper>();
+            if (!outputWrap->isTensor())
+                continue;
+
+            auto inputNode = inputld.backendNodes[DNN_BACKEND_TIMVX].dynamicCast<TimVXBackendNode>();
+            if (!inputNode->isLast && inputNode->opIndex != -1)
+            {
+                CV_Assert(outputWrap->getTensorAttr() == tim::vx::TRANSIENT);
+                // set last
+                inputNode->isLast = true;
+
+                auto shapeType = getShapeTypeFromMat(outputWrap->getMat());
+                auto outQuant = outputWrap->getTensorQuantization();
+
+                outputWrap->setTensorShape(shapeType);
+                outputWrap->createTensor(inputNode->tvGraph->graph,
+                                            tim::vx::TensorAttribute::OUTPUT, outQuant);
+                int outIndex = inputNode->tvGraph->addWrapper(outputWrap);
+                inputNode->outputIndexList.clear();
+                inputNode->outputIndexList.push_back(outIndex);
+            }
+        }
+    }
+}
+
+void Net::Impl::initTimVXBackend()
+{
+    CV_TRACE_FUNCTION();
+    CV_Assert(preferableBackend == DNN_BACKEND_TIMVX);
+
+    // Build TimVX Graph from sets of layers that support this TimVX backend.
+    // Split a whole model on several TimVX Graph if some of layers are not implemented by TimVX backend.
+    if (!haveTimVX())
+        return;
+
+    // Allocate graphConflictMap
+    if (timVxInfo.graphConflictMap.empty())
+        timVxInfo.graphConflictMap.resize(layers.size());
+
+    auto it = layers.begin();
+    bool isLast = false; // If the node is the last node in current tvGraph.
+
+    for (; it != layers.end(); it++)
+    {
+        isLast = false;
+        LayerData &ld = it->second;
+        if(ld.skip)
+            continue;
+        Ptr<Layer> layer = ld.layerInstance;
+        if (!layer->supportBackend(preferableBackend))
+        {
+            continue;
+        }
+
+        // If layer consumers are more than one, set isLast true.
+        // For now, TimVX backend divides multiple branchs into multiple tvGraph.
+        if (ld.consumers.size() == 0)
+        {
+            isLast = true;
+        }
+        else if(ld.consumers.size() == 1)
+        {
+            LayerData* consumerld = &layers[ld.consumers[0].lid];
+
+            while (consumerld)
+            {
+                if (consumerld->skip)
+                {
+                    if (consumerld->consumers.size() == 1)
+                    {
+                        int nextLayerId = consumerld->consumers[0].lid;
+                        consumerld = &layers[nextLayerId];
+                    }
+                    else
+                    {
+                        isLast = true;
+                        break;
+                    }
+                }
+                else
+                {
+                    break;
+                }
+            }
+            Ptr<Layer>& consumerLayer = consumerld->layerInstance;
+
+            if (!isLast && !consumerLayer->supportBackend(preferableBackend))
+            {
+                isLast = true;
+            }
+        }
+        else
+        {
+            // If there are is multiple input, and only one of them is supported.
+            int tvSupportNum = 0;
+            for (int i = 0; i<ld.consumers.size(); i++)
+            {
+                LayerData* consumerld = &layers[ld.consumers[0].lid];
+
+                while (consumerld)
+                {
+                    if (consumerld->skip)
+                    {
+                        if (consumerld->consumers.size() == 1)
+                        {
+                            int nextLayerId = consumerld->consumers[0].lid;
+                            consumerld = &layers[nextLayerId];
+                        }
+                        else
+                        {
+                            isLast = true;
+                            break;
+                        }
+                    }
+                    else
+                    {
+                        break;
+                    }
+                }
+                Ptr<Layer>& consumerLayer = consumerld->layerInstance;
+
+                if (consumerLayer->supportBackend(preferableBackend))
+                {
+                    tvSupportNum++;
+                }
+            }
+
+            if (tvSupportNum != 1)
+                isLast = true;
+        }
+
+        int graphIndex = -1;
+        bool needRecorrect = !timVxInfo.findGraphIndex(ld.inputBlobsWrappers, graphIndex);
+
+        if (graphIndex != -1 && !needRecorrect)
+        {
+            needRecorrect = timVxInfo.isConflict(ld.id, graphIndex);
+        }
+
+        // Recorrect the input layer.
+        if (needRecorrect)
+        {
+            // set all inputLayers' as last layer, and convert TRANSIENT to output.
+            for (int i = 0; i < ld.inputBlobsWrappers.size(); i++)
+            {
+                auto inputWrap = ld.inputBlobsWrappers[i];
+                auto tvInputWrap = inputWrap.dynamicCast<TimVXBackendWrapper>();
+                if (!tvInputWrap->isTensor())
+                    continue;
+
+                auto attr = tvInputWrap->getTensorAttr();
+                if (attr == tim::vx::TensorAttribute::OUTPUT)
+                {
+                    continue;
+                }
+                else if (attr == tim::vx::TensorAttribute::INPUT)
+                {
+                    Mat matTmp = tvInputWrap->getMat();
+                    tvInputWrap = Ptr<TimVXBackendWrapper>(new TimVXBackendWrapper(matTmp));
+
+                }
+                else if (attr == tim::vx::TensorAttribute::TRANSIENT)
+                {
+                    tvConvertToOutputNode(ld, tvInputWrap);
+                    // updateConflictMap
+                    tvUpdateConfictMap(graphIndex, ld, timVxInfo.graphConflictMap);
+                }
+            }
+            graphIndex = -1;
+        }
+
+        if (graphIndex == -1)
+        {
+            graphIndex = timVxInfo.createGraph();
+        }
+        timVxInfo.setTmpGraphIndex(graphIndex);
+
+        ld.backendNodes[DNN_BACKEND_TIMVX] =
+                layer->initTimVX(&timVxInfo, ld.inputBlobsWrappers, ld.outputBlobsWrappers, isLast);
+
+        // post process, create last node correctly.
+        if (isLast && ld.backendNodes[DNN_BACKEND_TIMVX])
+        {
+            auto tmpNode = ld.backendNodes[DNN_BACKEND_TIMVX].dynamicCast<TimVXBackendNode>();
+            tmpNode->isLast = true;
+            // update graphConflictMap
+            tvUpdateConfictMap(graphIndex, ld, timVxInfo.graphConflictMap);
+        }
+
+        // post process for failing to create timvx Node.
+        if (!ld.backendNodes[DNN_BACKEND_TIMVX])
+        {
+            for (int i = 0; i < ld.inputBlobsWrappers.size(); i++)
+            {
+                auto inputWrap = ld.inputBlobsWrappers[i];
+                auto tvInputWrap = inputWrap.dynamicCast<TimVXBackendWrapper>();
+                if (!tvInputWrap->isTensor())
+                    continue;
+
+                auto attr = tvInputWrap->getTensorAttr();
+                if (attr == tim::vx::TensorAttribute::TRANSIENT)
+                {
+                    tvConvertToOutputNode(ld, tvInputWrap);
+                }
+            }
+        }
+    }
+
+    // Op Binding
+    it = layers.begin();
+    Ptr<TimVXBackendNode> node;
+    std::vector<Ptr<TimVXGraph> > tmpGrapList;
+    for (; it != layers.end(); it++)
+    {
+        LayerData &ld = it->second;
+
+        if (ld.backendNodes[DNN_BACKEND_TIMVX])
+            node = ld.backendNodes[DNN_BACKEND_TIMVX].dynamicCast<TimVXBackendNode>();
+        else
+            continue;
+
+        // Binding tvTensor and tvOp
+        if (node->opIndex >= 0)
+            node->opBinding();
+    }
+}
+
+CV__DNN_INLINE_NS_END
+
+// from CPU to NPU
+bool copyToTensor(std::shared_ptr<tim::vx::Tensor> &dst, const Mat &src)
+{
+    CV_Assert(src.isContinuous() && (src.type() == CV_8S || src.type() == CV_32F));
+    if (dst->CopyDataToTensor(src.data, src.total()))
+    {
+        return true;
+    }
+    else
+        return false;
+}
+
+// from NPU to CPU
+bool copyToMat(const Mat &dst, std::shared_ptr<tim::vx::Tensor> &src)
+{
+    CV_Assert(dst.isContinuous() && (dst.type() == CV_8S || dst.type() == CV_32F));
+    if (src->CopyDataFromTensor(dst.data))
+    {
+        return true;
+    }
+    else
+        return false;
+}
+
+tvActivationType getTimVXActType(String & actString)
+{
+    if (actString == "ReLUInt8") return tvActReLU;
+    if (actString == "ReLU6Int8") return tvActReLU6;
+    if (actString == "TanHInt8") return tvActTanH;
+    if (actString == "SwishInt8") return tvActSwish;
+    if (actString == "MishInt8") return tvActMish;
+    if (actString == "SigmoidInt8") return tvActSigmoid;
+    if (actString == "ELUInt8") return tvActELU;
+
+    return tvActNotSupported;
+}
+
+tim::vx::ShapeType getShapeTypeFromMat(const Mat& mat, bool ifConst)
+{
+    /* Convert Mat shape to TimVX Tensor shape.
+    DataLayout in TimVX is WHCN, while NCHW in OpenCV.
+    So we do vector reverse.
+    */
+    CV_Assert(!mat.empty());
+    tim::vx::ShapeType tvInputShape;
+    auto matShape = shape(mat);
+    tvInputShape.assign(matShape.begin(), matShape.end());
+
+    if ( matShape.size() > 1 )  // TODO: check when we need reverse the shape vector.
+    {
+        if (ifConst && tvInputShape.size() == 2 && tvInputShape[1] == 1)
+        {   // if bias vector, shape [n, 1] to [n].
+            tvInputShape.resize(1);
+        }
+        else
+            std::reverse(tvInputShape.begin(), tvInputShape.end());
+    }
+    return tvInputShape;
+}
+
+bool getQuantType(const std::vector<float>& scales, int numOutput)
+{
+    CV_Assert(!scales.empty());
+    if (numOutput == -1)
+    {
+        numOutput = scales.size();
+    }
+    bool tvSymmetric = false;
+
+    for (int i =1; i < numOutput; i++)
+    {
+        if (std::abs(scales[0] - scales[i]) > std::numeric_limits<float>::epsilon())
+        {
+            tvSymmetric = true;
+            break;
+        }
+    }
+
+    return tvSymmetric;
+}
+
+// convert mat Depth to tensorDataType
+tim::vx::DataType dataTypeConvert(int matDepth)
+{
+    tim::vx::DataType tensorDataType;
+    switch(matDepth)
+    {
+        case CV_8U:
+        {
+            tensorDataType = tim::vx::DataType::UINT8;
+            break;
+        }
+        case CV_8S:
+        {
+            tensorDataType = tim::vx::DataType::INT8;
+            break;
+        }
+        case CV_16U:
+        {
+            tensorDataType = tim::vx::DataType::UINT16;
+            break;
+        }
+        case CV_16S:
+        {
+            tensorDataType = tim::vx::DataType::INT16;
+            break;
+        }
+        case CV_32S:
+        {
+            tensorDataType = tim::vx::DataType::INT32;
+            break;
+        }
+        case CV_32F:
+        {
+            tensorDataType = tim::vx::DataType::FLOAT32;
+            break;
+        }
+        case CV_16F:
+        {
+            tensorDataType = tim::vx::DataType::FLOAT16;
+            break;
+        }
+        default:
+        {
+            tensorDataType = tim::vx::DataType::UNKNOWN;
+            break;
+        }
+    }
+    return tensorDataType;
+}
+
+std::vector<Ptr<TimVXBackendWrapper> > getWrappers(const std::vector<int> wrappersIndex,
+                                                          Ptr<TimVXGraph> tvGraph)
+{
+    std::vector<Ptr<TimVXBackendWrapper> > wrappers;
+    for (int i = 0; i<wrappersIndex.size(); i++)
+    {
+        auto wrapper = tvGraph->getWrapper(wrappersIndex[i]);
+        if (wrapper)
+            wrappers.push_back(wrapper);
+    }
+
+    return wrappers;
+}
+
+// *********************** TimVXGraph ********************
+TimVXGraph::TimVXGraph()
+{
+    // new TimVX Graph
+    context = tim::vx::Context::Create();
+    graph = context->CreateGraph();
+    isCompiled = false;
+}
+
+TimVXGraph::~TimVXGraph()
+{
+
+    // release opList
+    for (auto& tensor: tensorList)
+        tensor.reset();
+
+    // release tensorList
+    for (auto& op: opList)
+        op.reset();
+
+    // release graph
+    graph.reset();
+
+    // release context
+    context.reset();
+}
+
+std::shared_ptr<tim::vx::Operation> TimVXGraph::getOp(const int opIndex)
+{
+    CV_Assert(0 <= opIndex && !opList.empty() && opIndex < opList.size());
+    return opList[opIndex];
+}
+
+int TimVXGraph::addWrapper(Ptr<TimVXBackendWrapper>& tensorWrapper)
+{
+    CV_Assert(tensorWrapper->isTensor());
+    tim::vx::TensorAttribute tensorAttr = tensorWrapper->getTensorAttr();
+
+    wrapperList.push_back(tensorWrapper);
+    tensorList.push_back(tensorWrapper->getTensor());
+    int wrapperIndex = wrapperList.size() -1;
+
+    if (tensorAttr == tim::vx::TensorAttribute::INPUT)
+    {
+        inputWrappersIndex.push_back(wrapperIndex);
+    }
+
+    if (tensorAttr == tim::vx::TensorAttribute::OUTPUT)
+    {
+        outputWrappersIndex.push_back(wrapperIndex);
+    }
+
+    return wrapperIndex;
+}
+
+Ptr<TimVXBackendWrapper> TimVXGraph::getWrapper(int wrapperIndex)
+{
+    CV_Assert(wrapperIndex>=0 && wrapperIndex < wrapperList.size());
+    return wrapperList[wrapperIndex];
+}
+
+int TimVXGraph::addOp(const std::shared_ptr<tim::vx::Operation>& op)
+{
+    CV_Assert(op);
+    opList.emplace_back(op);
+    return opList.size()-1;
+}
+
+int TimVXGraph::getTensorIndex(const std::shared_ptr<tim::vx::Tensor>& tensor)
+{
+    auto it = find(tensorList.begin(), tensorList.end(), tensor);
+    if (it != tensorList.end())
+        return it - tensorList.begin();
+    else
+        return -1;
+}
+
+void TimVXGraph::forward()
+{
+    CV_Assert(!inputWrappersIndex.empty() && !outputWrappersIndex.empty());
+
+    // Every TimVXGraph Instance only compiles once.
+    if (!this->isCompiled)
+    {
+        if (!graph->Compile())
+            CV_Error(cv::Error::StsBadArg, " Fail to compile TimVX graph!");
+        this->isCompiled = true;
+    }
+
+    if (!graph->Run())
+        CV_Error(cv::Error::StsBadArg, " Fail to run TimVX graph!");
+}
+
+// *********************** TimVXBackendNode ********************
+TimVXBackendNode::TimVXBackendNode(const Ptr<TimVXGraph>& tvGraph_): BackendNode(DNN_BACKEND_TIMVX)
+{
+    opIndex = -1;
+    tvGraph = tvGraph_;
+    isLast = false;
+}
+
+TimVXBackendNode::TimVXBackendNode(const Ptr<TimVXGraph>& tvGraph_,
+                                   const std::shared_ptr<tim::vx::Operation>& op_): BackendNode(DNN_BACKEND_TIMVX)
+{
+    tvGraph = tvGraph_;
+    opIndex = tvGraph->addOp(op_);
+    isLast = false;
+}
+
+TimVXBackendNode::TimVXBackendNode(const Ptr<TimVXGraph>& tvGraph_, std::shared_ptr<tim::vx::Operation>& op_,
+                                   std::vector<int>& inputsIndex, std::vector<int>& outpusIndex)
+                                   :BackendNode(DNN_BACKEND_TIMVX)
+{
+    tvGraph = tvGraph_;
+    opIndex = tvGraph->addOp(op_);
+    isLast = false;
+
+    if (!inputsIndex.empty())
+        inputIndexList.assign(inputsIndex.begin(), inputsIndex.end());
+
+    if (!outpusIndex.empty())
+        outputIndexList.assign(outpusIndex.begin(), outpusIndex.end());
+}
+
+bool TimVXBackendNode::opBinding()
+{
+    if (!tvGraph || tvGraph->isCompiled || opIndex == -1)
+        return false;
+
+    std::shared_ptr<tim::vx::Operation> op = tvGraph->getOp(opIndex);
+
+    if (!inputIndexList.empty())
+    {
+        std::vector<Ptr<TimVXBackendWrapper> > inputsWrapper = getWrappers(inputIndexList, tvGraph);
+        // Binding input Tensor.
+        for (auto& warpper: inputsWrapper)
+        {
+            op->BindInput(warpper->getTensor());
+        }
+    }
+
+    if (!outputIndexList.empty())
+    {
+        std::vector<Ptr<TimVXBackendWrapper> > outputsWrapper = getWrappers(outputIndexList, tvGraph);
+        for (auto& warpper: outputsWrapper)
+        {
+            op->BindOutput(warpper->getTensor());
+        }
+    }
+    return true;
+}
+
+void TimVXBackendNode::setInputTensor()
+{
+    if (!tvGraph || opIndex == -1)
+        return;
+
+    if (!inputIndexList.empty())
+    {
+        std::vector<Ptr<TimVXBackendWrapper> > inputsWrapper = getWrappers(inputIndexList, tvGraph);
+
+        // Binding input Tensor.
+        for (auto& warpper: inputsWrapper)
+        {
+            if (warpper->getTensorAttr() == tim::vx::TensorAttribute::INPUT)
+            {
+                warpper->setHostDirty();
+                warpper->copyToDevice();
+            }
+        }
+    }
+}
+
+// *********************** TimVXBackendWrapper ********************
+// Default Constructor
+TimVXBackendWrapper::TimVXBackendWrapper() : BackendWrapper(DNN_BACKEND_TIMVX, DNN_TARGET_NPU)
+{
+    isTensor_ = false;
+    deviceDirty = false;
+    hostDirty = false;
+    tensorType = tim::vx::DataType::UNKNOWN;
+    tensorShape = {};
+    tensorIndex = -1;
+    tensorAttr = tim::vx::TensorAttribute::CONSTANT;
+}
+
+TimVXBackendWrapper::TimVXBackendWrapper(Mat& m) : BackendWrapper(DNN_BACKEND_TIMVX,
+                                                                  DNN_TARGET_NPU)
+{
+    host = m;
+    isTensor_ = false;
+    deviceDirty = false;
+    hostDirty = true;
+    tensorType = dataTypeConvert(m.type());
+    tensorShape = {};
+    tensorIndex = -1;
+    tensorAttr = tim::vx::TensorAttribute::CONSTANT;
+
+    // TODO: unsupported data by TimVX should run convert function first.
+    CV_Assert(tensorType != tim::vx::DataType::UNKNOWN);
+}
+
+TimVXBackendWrapper::TimVXBackendWrapper(const Ptr<BackendWrapper>& baseBuffer, Mat& m)
+    :BackendWrapper(DNN_BACKEND_TIMVX, DNN_TARGET_NPU)
+{
+    Ptr<TimVXBackendWrapper> base = baseBuffer.dynamicCast<TimVXBackendWrapper>();
+    CV_Assert(!base.empty());
+    tensor = base->tensor;
+    isTensor_ = base->isTensor_;
+    tensorIndex = base->tensorIndex;
+    tensorType = base->tensorType;
+    tensorAttr = base->tensorAttr;
+    tensorShape = base->tensorShape;
+    deviceDirty = base->deviceDirty;
+    hostDirty = base->hostDirty;
+    host = m;
+}
+
+TimVXBackendWrapper::TimVXBackendWrapper(std::shared_ptr<tim::vx::Tensor>& tensor_)
+    :BackendWrapper(DNN_BACKEND_TIMVX, DNN_TARGET_NPU)
+{
+    tensor = tensor_;
+    isTensor_ = true;
+    deviceDirty = true;
+    hostDirty = false;
+    tensorType = tensor_->GetDataType(); // getTensor DataType.
+    tensorAttr = tensor_->GetSpec().attr_;  // getTensor Attribution.
+    tensorShape = tensor_->GetShape();
+    tensorIndex = -1;
+}
+
+void TimVXBackendWrapper::setTensorShape(const tim::vx::ShapeType & matShape)
+{
+    CV_Assert(!matShape.empty());
+    tensorShape.assign(matShape.begin(), matShape.end());
+}
+
+int TimVXBackendWrapper::getTensorIndex()
+{
+    CV_Assert(isTensor_);
+    return tensorIndex;
+}
+
+tim::vx::TensorAttribute TimVXBackendWrapper::getTensorAttr()
+{
+    CV_Assert(isTensor_);
+    return tensorAttr;
+}
+
+// Create tensor
+void TimVXBackendWrapper::createTensor(std::shared_ptr<tim::vx::Graph>& graph,
+    tim::vx::TensorAttribute tensorAttribute)
+{
+    Ptr<tim::vx::Quantization> epmtyQuant = nullptr;
+    return this->createTensor(graph, tensorAttribute, epmtyQuant);
+}
+
+// Create tensor
+void TimVXBackendWrapper::createTensor(std::shared_ptr<tim::vx::Graph>& graph,
+    tim::vx::TensorAttribute tensorAttribute, Ptr<tim::vx::Quantization>& tvQuant)
+{
+    CV_Assert(graph);
+    tim::vx::TensorSpec tensorSpec;
+
+    if (tensorAttribute == tim::vx::INPUT)
+    {
+        CV_Assert(!host.empty());
+        tensorShape = getShapeTypeFromMat(host);
+    }
+    else if (tensorAttribute == tim::vx::OUTPUT)
+    {
+        CV_Assert(!tensorShape.empty() && !host.empty());
+        tensorShape = getShapeTypeFromMat(host);
+    }
+    else if (tensorAttribute == tim::vx::CONSTANT)
+    {
+        if (!host.empty())
+            tensorShape = getShapeTypeFromMat(host, true);
+    }
+    else
+    {
+        if (!host.empty())
+            tensorShape = getShapeTypeFromMat(host);
+    }
+
+    // Tensor shape
+    if (tvQuant)
+    {
+        tensorSpec = tim::vx::TensorSpec(tensorType, tensorShape, tensorAttribute, *tvQuant);
+    }
+    else
+    {
+        tensorSpec = tim::vx::TensorSpec(tensorType, tensorShape, tensorAttribute);
+    }
+
+    if (!host.empty() && tensorAttribute != tim::vx::INPUT && tensorAttribute != tim::vx::OUTPUT && tensorAttribute != tim::vx::TRANSIENT)
+    {
+        tensor = graph->CreateTensor(tensorSpec, (void *)(host.data));
+    }
+    else
+    {
+        tensor = graph->CreateTensor(tensorSpec);
+    }
+    isTensor_ = true;
+
+    // set Attribution
+    tensorAttr = tensorAttribute;
+}
+
+Ptr<tim::vx::Quantization> TimVXBackendWrapper::getTensorQuantization()
+{
+    CV_Assert(isTensor_ && tensor);
+    auto quantize = tensor->GetQuantization();
+    return makePtr<tim::vx::Quantization>(quantize);
+}
+
+std::shared_ptr<tim::vx::Tensor> TimVXBackendWrapper::getTensor()
+{
+     CV_Assert(isTensor_);
+     return tensor;
+}
+
+Mat TimVXBackendWrapper::getMat()
+{
+    if (host.empty())
+        return {};
+    return host;
+}
+
+
+bool TimVXBackendWrapper::isTensor()
+{
+    return isTensor_;
+}
+
+void TimVXBackendWrapper::copyToHost()
+{
+    if (deviceDirty && !host.empty())
+    {
+        copyToMat(host, tensor);
+        deviceDirty = false;
+    }
+}
+
+void TimVXBackendWrapper::setHostDirty()
+{
+    hostDirty = true;
+}
+
+void TimVXBackendWrapper::setDeviceDirty()
+{
+    deviceDirty = true;
+}
+
+void TimVXBackendWrapper::copyToDevice()
+{
+    if (isTensor_ && hostDirty && !host.empty())
+    {
+        copyToTensor(tensor, host);
+        hostDirty = false;
+    }
+}
+
+// *********************** TimVXInfo ********************
+TimVXInfo::TimVXInfo()
+{
+    graphIndex = -1;
+}
+
+TimVXInfo::~TimVXInfo()
+{}
+
+int TimVXInfo::createGraph()
+{
+    Ptr<TimVXGraph> tmpGraph =  Ptr<TimVXGraph>(new TimVXGraph());
+    this->tvGraphList.push_back(tmpGraph);
+    return this->tvGraphList.size() - 1;
+}
+
+bool TimVXInfo::findGraphIndex(const std::vector<Ptr<BackendWrapper> >  &inputsWrapper, int& graphIndex)
+{
+    graphIndex = -1;
+    int wrapperSize = inputsWrapper.size();
+    int graphSize = tvGraphList.size();
+
+    if (wrapperSize != 0 && graphSize == 0)
+    {
+        return true;
+    }
+
+    int tensorIndex = -1;
+    Ptr<TimVXBackendWrapper> wrapper;
+    Ptr<TimVXGraph> tvGraph;
+
+    for (int i = 0; i < graphSize; i++)
+    {
+        tvGraph = tvGraphList[i];
+        for (int j = 0; j < wrapperSize; j++ )
+        {
+            wrapper = inputsWrapper[j].dynamicCast<TimVXBackendWrapper>();
+
+            if (!wrapper->isTensor()) // Skip wrapper without Tensor.
+                continue;
+
+            tensorIndex = tvGraph->getTensorIndex(wrapper->getTensor());
+            if (tensorIndex != -1 && wrapper->getTensorAttr() == tim::vx::TensorAttribute::TRANSIENT)
+            {
+                if (graphIndex == -1)
+                    graphIndex = i;
+                else if (graphIndex != i)  // if inputs of the same inputWrapper are from differen tvGraph.
+                {
+                    graphIndex = -1;
+                    return false;
+                }
+            }
+        }
+    }
+    return true;
+}
+
+void TimVXInfo::setTmpGraphIndex(int graphIndex)
+{
+    this->graphIndex = graphIndex;
+}
+
+int TimVXInfo::getTmpGraphIndex()
+{
+    int res = -1;
+    if (graphIndex != -1)
+    {
+        res = graphIndex;
+        graphIndex = -1;
+    }
+    return res;
+}
+
+bool TimVXInfo::isConflict(int layerId, int graphIndex)
+{
+    if (graphConflictMap[layerId].empty())
+        return false;
+
+    std::vector<int>::iterator it = std::find(graphConflictMap[layerId].begin(),
+                                              graphConflictMap[layerId].end(), graphIndex);
+    if (it != graphConflictMap[layerId].end())
+        return true;
+    else
+        return false;
+}
+
+Ptr<TimVXGraph> TimVXInfo::getGraph()
+{
+    int index = getTmpGraphIndex();
+    if (0 <= index && index < tvGraphList.size())
+        return tvGraphList[index];
+    else
+        return {};
+}
+
+#endif
+
+void forwardTimVX(std::vector<Ptr<BackendWrapper> >& outputs, const Ptr<BackendNode>& node_)
+{
+#ifdef HAVE_TIMVX
+    CV_Assert(!node_.empty());
+    Ptr<TimVXBackendNode> node = node_.dynamicCast<TimVXBackendNode>();
+
+    if (node)
+    {
+        // set input
+        node->setInputTensor();
+
+        // graph Forward
+        if (node->isLast)
+        {
+            node->tvGraph->forward();
+        }
+    }
+    else
+        return;
+
+    // set ouput
+    Ptr<TimVXBackendWrapper> outWarpper;
+    for (int i = 0; i < outputs.size(); i++)
+    {
+        outWarpper = outputs[i].dynamicCast<TimVXBackendWrapper>();
+        if (outWarpper->isTensor() && outWarpper->getTensorAttr() == tim::vx::TensorAttribute::OUTPUT)
+        {
+            outWarpper->setDeviceDirty();
+            outWarpper->copyToHost();
+        }
+    }
+#endif
+}
+
+bool haveTimVX()
+{
+#ifdef HAVE_TIMVX
+    return true;
+#else
+    return false;
+#endif
+}
+} // namespace dnn
+} // namespace cv

modules/dnn/src/op_timvx.hpp

@@ -0,0 +1,187 @@
+// This file is part of OpenCV project.
+// It is subject to the license terms in the LICENSE file found in the top-level directory
+// of this distribution and at http://opencv.org/license.html.
+//
+// Copyright (C) 2019-2021, Shenzhen Institute of Artificial Intelligence and
+// Robotics for Society, all rights reserved.
+// Third party copyrights are property of their respective owners.
+
+#ifndef OPENCV_DNN_OP_TIMVX_HPP
+#define OPENCV_DNN_OP_TIMVX_HPP
+
+#include <opencv2/dnn/shape_utils.hpp>
+
+// TimVX head file.
+#ifdef HAVE_TIMVX
+#include "tim/vx/context.h"
+#include "tim/vx/graph.h"
+#include "tim/vx/operation.h"
+#include "tim/vx/ops.h"
+#include "tim/vx/tensor.h"
+#endif  // HAVE_TIMVX
+
+namespace cv
+{
+namespace dnn
+{
+#ifdef HAVE_TIMVX
+
+enum tvActivationType{
+    tvActNotSupported = -1,
+    tvActReLU,
+    tvActReLU6,
+    tvActTanH,
+    tvActSwish,
+    tvActMish,
+    tvActSigmoid,
+    tvActELU
+};
+
+// Data copied from/to Mat to/from Tensor. Change the shape of dst if
+// needed to make it the same shape as src.
+bool copyToTensor(Ptr<tim::vx::Tensor> &dst, const Mat &src);
+bool copyToMat(const Mat &dst, Ptr<tim::vx::Tensor> &src);
+tvActivationType getTimVXActType(String & actString);
+
+// Convert Mat shape to TimVX TensorShape
+tim::vx::ShapeType getShapeTypeFromMat(const Mat& mat, bool ifConst = false);
+
+// if all value in weight
+bool getQuantType(const std::vector<float>& scales, int numOutput = -1);
+
+class TimVXInfo;
+class TimVXGraph;
+class TimVXBackendNode;
+class TimVXBackendWrapper;
+
+// Maintain the tvGraph and tvTensor List. For now, every tvGraph only have one output node, and each node
+// in tvGraph has only one output too. It could be optimized in future.
+// TODO: tvGraph supports multiple output node.
+class TimVXGraph
+{
+public:
+    TimVXGraph();
+    ~TimVXGraph();
+    std::shared_ptr<tim::vx::Operation> getOp(const int opIndex);
+
+    // It will add tensorWrapper to wrapperList, and return index.
+    // And add tensor Ptr to tensorList.
+    int addWrapper(Ptr<TimVXBackendWrapper>& tensorWrapper);
+
+    void forward();
+
+    // Add new op to opList, and return the index.
+    int addOp(const std::shared_ptr<tim::vx::Operation>& op);
+
+    // If tensor existed in tensorList, return the tensorIndex, otherwise return -1.
+    int getTensorIndex(const std::shared_ptr<tim::vx::Tensor>& tensor);
+
+    Ptr<TimVXBackendWrapper> getWrapper(int wrapperIndex);
+
+    std::shared_ptr<tim::vx::Graph> graph;
+    bool isCompiled; // Every tvGraph can only be compiled once.
+
+private:
+    std::shared_ptr<tim::vx::Context> context;
+    std::vector<int> inputWrappersIndex;
+    std::vector<int> outputWrappersIndex;
+    std::vector<Ptr<TimVXBackendWrapper> > wrapperList;
+    std::vector<std::shared_ptr<tim::vx::Tensor> > tensorList;
+    std::vector<std::shared_ptr<tim::vx::Operation> > opList;
+};
+
+class TimVXBackendNode : public BackendNode
+{
+public:
+    TimVXBackendNode(const Ptr<TimVXGraph>& tvGraph);
+    TimVXBackendNode(const Ptr<TimVXGraph>& tvGraph, const std::shared_ptr<tim::vx::Operation>& op);
+    TimVXBackendNode(const Ptr<TimVXGraph>& tvGraph, std::shared_ptr<tim::vx::Operation>& op,
+                    std::vector<int>& inputsIndex, std::vector<int>& outpusIndex);
+
+    void setInputTensor();
+    bool opBinding();
+
+    // flag for marking OutputNode of tvGraph this node is the last node in this TimVX Graph.
+    bool isLast;
+    int opIndex;
+
+    // index of tensor and wrapper.
+    std::vector<int> inputIndexList;
+    std::vector<int> outputIndexList;
+    Ptr<TimVXGraph> tvGraph;
+};
+
+class TimVXBackendWrapper : public BackendWrapper
+{
+public:
+    TimVXBackendWrapper();
+    TimVXBackendWrapper(Mat& m);
+    TimVXBackendWrapper(const Ptr<BackendWrapper>& baseBuffer, Mat& m);
+    TimVXBackendWrapper(std::shared_ptr<tim::vx::Tensor>& tensor);
+
+    // Create Output Tensor
+    void createTensor(std::shared_ptr<tim::vx::Graph>& graph, tim::vx::TensorAttribute tensorAttribute);
+    void createTensor(std::shared_ptr<tim::vx::Graph>& graph, tim::vx::TensorAttribute tensorAttribute,
+                                                  Ptr<tim::vx::Quantization>& tvQuant);
+    std::shared_ptr<tim::vx::Tensor> getTensor();
+    Mat getMat();
+
+    // The Output tensor in TimVX doesn't have HostMat, The shape can only be given.
+    void setTensorShape(const tim::vx::ShapeType & matShape);
+    int getTensorIndex();
+    Ptr<tim::vx::Quantization> getTensorQuantization();
+    tim::vx::TensorAttribute getTensorAttr();
+    bool isTensor();
+
+    // Data Copy, CPU <==> NPU
+    virtual void copyToHost() CV_OVERRIDE;
+    virtual void setHostDirty() CV_OVERRIDE;
+    void setDeviceDirty();
+    void copyToDevice();
+
+private:
+    tim::vx::DataType tensorType;
+    bool deviceDirty;
+    bool hostDirty;
+    int tensorIndex;  // index of tensorList in specific TimVXGraph.
+    bool isTensor_;
+    Mat host;
+
+    tim::vx::ShapeType tensorShape;
+    std::shared_ptr<tim::vx::Tensor> tensor;
+    tim::vx::TensorAttribute tensorAttr;
+};
+
+// Contain all created tvGraphList, used in every
+class TimVXInfo{
+public:
+    TimVXInfo();
+    ~TimVXInfo();
+
+    // Find the right graph Index set as graphIndex, if cannot find, return empty ptr.
+    Ptr<TimVXGraph> getGraph();
+    bool findGraphIndex(const std::vector<Ptr<BackendWrapper> > &inputsWrapper, int& graphIndex);
+    void setTmpGraphIndex(int graphIndex);
+    bool isConflict(int layerId, int graphIndex);
+
+    // create a TimVXGraph, add it to tvGraphList, and return the index in tvGraphList.
+    int createGraph();
+
+    // graphConflictIndex[layerIndex] saves conflict graph index, which should be excluded
+    std::vector<std::vector<int> > graphConflictMap;
+
+private:
+    int getTmpGraphIndex();
+    std::vector<Ptr<TimVXGraph> > tvGraphList;
+    int graphIndex;
+
+};
+
+#endif
+
+void forwardTimVX(std::vector<Ptr<BackendWrapper> > &outputs, const Ptr<BackendNode>& node);
+bool haveTimVX();
+} // namespace dnn
+} // namespace cv
+
+#endif // OPENCV_DNN_OP_TIMVX_HPP

modules/dnn/src/registry.cpp

@@ -10,6 +10,7 @@
 #include "op_vkcom.hpp"
 #include "op_cuda.hpp"
 #include "op_webnn.hpp"
+#include "op_timvx.hpp"
 
 #include "halide_scheduler.hpp"
 
@@ -109,6 +110,13 @@ private:
             backends.push_back(std::make_pair(DNN_BACKEND_CUDA, DNN_TARGET_CUDA_FP16));
         }
 #endif
+
+#ifdef HAVE_TIMVX
+        if (haveTimVX())
+        {
+            backends.push_back(std::make_pair(DNN_BACKEND_TIMVX, DNN_TARGET_NPU));
+        }
+#endif
     }
 
     BackendsList backends;

modules/dnn/test/test_common.hpp

@@ -48,6 +48,7 @@
 #define CV_TEST_TAG_DNN_SKIP_ONNX_CONFORMANCE    "dnn_skip_onnx_conformance"
 #define CV_TEST_TAG_DNN_SKIP_PARSER              "dnn_skip_parser"
 
+#define CV_TEST_TAG_DNN_SKIP_TIMVX               "dnn_skip_timvx"
 
 #ifdef HAVE_INF_ENGINE
 #if INF_ENGINE_VER_MAJOR_EQ(2018050000)

modules/dnn/test/test_common.impl.hpp

@@ -30,6 +30,7 @@ void PrintTo(const cv::dnn::Backend& v, std::ostream* os)
     case DNN_BACKEND_INFERENCE_ENGINE_NN_BUILDER_2019: *os << "DLIE"; return;
     case DNN_BACKEND_INFERENCE_ENGINE_NGRAPH: *os << "NGRAPH"; return;
     case DNN_BACKEND_WEBNN: *os << "WEBNN"; return;
+    case DNN_BACKEND_TIMVX: *os << "TIMVX"; return;
     } // don't use "default:" to emit compiler warnings
     *os << "DNN_BACKEND_UNKNOWN(" << (int)v << ")";
 }
@@ -46,6 +47,7 @@ void PrintTo(const cv::dnn::Target& v, std::ostream* os)
     case DNN_TARGET_FPGA: *os << "FPGA"; return;
     case DNN_TARGET_CUDA: *os << "CUDA"; return;
     case DNN_TARGET_CUDA_FP16: *os << "CUDA_FP16"; return;
+    case DNN_TARGET_NPU: *os << "NPU"; return;
     } // don't use "default:" to emit compiler warnings
     *os << "DNN_TARGET_UNKNOWN(" << (int)v << ")";
 }
@@ -478,6 +480,11 @@ void initDNNTests()
     registerGlobalSkipTag(
         CV_TEST_TAG_DNN_SKIP_CUDA, CV_TEST_TAG_DNN_SKIP_CUDA_FP32, CV_TEST_TAG_DNN_SKIP_CUDA_FP16
     );
+#endif
+#ifdef HAVE_TIMVX
+    registerGlobalSkipTag(
+        CV_TEST_TAG_DNN_SKIP_TIMVX
+    );
 #endif
     registerGlobalSkipTag(
         CV_TEST_TAG_DNN_SKIP_ONNX_CONFORMANCE,

modules/dnn/test/test_int8_layers.cpp

@@ -12,6 +12,9 @@ testing::internal::ParamGenerator< tuple<Backend, Target> > dnnBackendsAndTarget
 {
     std::vector< tuple<Backend, Target> > targets;
     targets.push_back(make_tuple(DNN_BACKEND_OPENCV, DNN_TARGET_CPU));
+#ifdef HAVE_TIMVX
+    targets.push_back(make_tuple(DNN_BACKEND_TIMVX, DNN_TARGET_NPU));
+#endif
     return testing::ValuesIn(targets);
 }
 
@@ -104,14 +107,29 @@ TEST_P(Test_Int8_layers, Convolution1D)
 
 TEST_P(Test_Int8_layers, Convolution2D)
 {
-    testLayer("layer_convolution", "Caffe", 0.0174, 0.0758, 1, 1, true);
+    if(backend == DNN_BACKEND_TIMVX)
-    testLayer("single_conv", "TensorFlow", 0.00413, 0.02201);
+        testLayer("single_conv", "TensorFlow", 0.00424, 0.02201);
-    testLayer("depthwise_conv2d", "TensorFlow", 0.0388, 0.169);
+    else
+        testLayer("single_conv", "TensorFlow", 0.00413, 0.02201);
+
     testLayer("atrous_conv2d_valid", "TensorFlow", 0.0193, 0.0633);
     testLayer("atrous_conv2d_same", "TensorFlow", 0.0185, 0.1322);
     testLayer("keras_atrous_conv2d_same", "TensorFlow", 0.0056, 0.0244);
-    testLayer("convolution", "ONNX", 0.0052, 0.01516);
+
-    testLayer("two_convolution", "ONNX", 0.00295, 0.00840);
+    if(backend == DNN_BACKEND_TIMVX)
+        testLayer("convolution", "ONNX", 0.00534, 0.01516);
+    else
+        testLayer("convolution", "ONNX", 0.0052, 0.01516);
+
+    if(backend == DNN_BACKEND_TIMVX)
+        testLayer("two_convolution", "ONNX", 0.0033, 0.01);
+    else
+        testLayer("two_convolution", "ONNX", 0.00295, 0.00840);
+
+    if(backend == DNN_BACKEND_TIMVX)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_TIMVX);
+    testLayer("layer_convolution", "Caffe", 0.0174, 0.0758, 1, 1, true);
+    testLayer("depthwise_conv2d", "TensorFlow", 0.0388, 0.169);
 }
 
 TEST_P(Test_Int8_layers, Convolution3D)
@@ -130,9 +148,21 @@ TEST_P(Test_Int8_layers, Flatten)
 
 TEST_P(Test_Int8_layers, Padding)
 {
-    testLayer("padding_valid", "TensorFlow", 0.0026, 0.0064);
+    if (backend == DNN_BACKEND_TIMVX)
-    testLayer("padding_same", "TensorFlow", 0.0081, 0.032);
+        testLayer("padding_valid", "TensorFlow", 0.0292, 0.0105);
-    testLayer("spatial_padding", "TensorFlow", 0.0078, 0.028);
+    else
+        testLayer("padding_valid", "TensorFlow", 0.0026, 0.0064);
+
+    if (backend == DNN_BACKEND_TIMVX)
+        testLayer("padding_same", "TensorFlow", 0.0085, 0.032);
+    else
+        testLayer("padding_same", "TensorFlow", 0.0081, 0.032);
+
+    if (backend == DNN_BACKEND_TIMVX)
+        testLayer("spatial_padding", "TensorFlow", 0.0079, 0.028);
+    else
+        testLayer("spatial_padding", "TensorFlow", 0.0078, 0.028);
+
     testLayer("mirror_pad", "TensorFlow", 0.0064, 0.013);
     testLayer("pad_and_concat", "TensorFlow", 0.0021, 0.0098);
     testLayer("padding", "ONNX", 0.0005, 0.0069);
@@ -283,20 +313,35 @@ TEST_P(Test_Int8_layers, InnerProduct)
 {
     testLayer("layer_inner_product", "Caffe", 0.005, 0.02, 1, 1, true);
     testLayer("matmul", "TensorFlow", 0.0061, 0.019);
-    testLayer("nhwc_transpose_reshape_matmul", "TensorFlow", 0.0009, 0.0091);
+
+    if (backend == DNN_BACKEND_TIMVX)
+        testLayer("nhwc_transpose_reshape_matmul", "TensorFlow", 0.0018, 0.0175);
+    else
+        testLayer("nhwc_transpose_reshape_matmul", "TensorFlow", 0.0009, 0.0091);
+
     testLayer("nhwc_reshape_matmul", "TensorFlow", 0.03, 0.071);
     testLayer("matmul_layout", "TensorFlow", 0.035, 0.06);
     testLayer("tf2_dense", "TensorFlow", 0, 0);
     testLayer("matmul_add", "ONNX", 0.041, 0.082);
     testLayer("linear", "ONNX", 0.0018, 0.0029);
-    testLayer("constant", "ONNX", 0.00021, 0.0006);
+
+    if (backend == DNN_BACKEND_TIMVX)
+        testLayer("constant", "ONNX", 0.00048, 0.0013);
+    else
+        testLayer("constant", "ONNX", 0.00021, 0.0006);
+
     testLayer("lin_with_constant", "ONNX", 0.0011, 0.0016);
 }
 
 TEST_P(Test_Int8_layers, Reshape)
 {
     testLayer("reshape_layer", "TensorFlow", 0.0032, 0.0082);
-    testLayer("reshape_nchw", "TensorFlow", 0.0089, 0.029);
+
+    if (backend == DNN_BACKEND_TIMVX)
+        testLayer("reshape_nchw", "TensorFlow", 0.0092, 0.0495);
+    else
+        testLayer("reshape_nchw", "TensorFlow", 0.0089, 0.029);
+
     testLayer("reshape_conv", "TensorFlow", 0.035, 0.054);
     testLayer("reshape_reduce", "TensorFlow", 0.0042, 0.0078);
     testLayer("reshape_as_shape", "TensorFlow", 0.0014, 0.0028);
@@ -307,7 +352,12 @@ TEST_P(Test_Int8_layers, Reshape)
     testLayer("flatten_by_prod", "ONNX", 0.0048, 0.0081);
     testLayer("squeeze", "ONNX", 0.0048, 0.0081);
     testLayer("unsqueeze", "ONNX", 0.0033, 0.0053);
-    testLayer("squeeze_and_conv_dynamic_axes", "ONNX", 0.0054, 0.0154);
+
+    if (backend == DNN_BACKEND_TIMVX)
+        testLayer("squeeze_and_conv_dynamic_axes", "ONNX", 0.006, 0.0212);
+    else
+        testLayer("squeeze_and_conv_dynamic_axes", "ONNX", 0.0054, 0.0154);
+
     testLayer("unsqueeze_and_conv_dynamic_axes", "ONNX", 0.0037, 0.0151);
 }
 
@@ -378,6 +428,10 @@ TEST_P(Test_Int8_layers, Dropout)
 TEST_P(Test_Int8_layers, Eltwise)
 {
     testLayer("layer_eltwise", "Caffe", 0.062, 0.15);
+
+    if (backend == DNN_BACKEND_TIMVX)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_TIMVX);
+
     testLayer("conv_2_inps", "Caffe", 0.0086, 0.0232, 2, 1, true, false);
     testLayer("eltwise_sub", "TensorFlow", 0.015, 0.047);
     testLayer("eltwise_add_vec", "TensorFlow", 0.037, 0.21); // tflite 0.0095, 0.0365
@@ -862,6 +916,8 @@ TEST_P(Test_Int8_nets, EfficientDet)
         applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL_FP16);
     if (target == DNN_TARGET_OPENCL && !ocl::Device::getDefault().isIntel())
         applyTestTag(CV_TEST_TAG_DNN_SKIP_OPENCL);
+    if (backend == DNN_BACKEND_TIMVX)
+        applyTestTag(CV_TEST_TAG_DNN_SKIP_TIMVX);
 
     if (target != DNN_TARGET_CPU)
     {