diff --git a/src/cpu/operators/CpuWinogradConv2d.cpp b/src/cpu/operators/CpuWinogradConv2d.cpp
index dcc18ce..7be2d6d 100644
--- a/src/cpu/operators/CpuWinogradConv2d.cpp
+++ b/src/cpu/operators/CpuWinogradConv2d.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2021 Arm Limited.
+ * Copyright (c) 2021-2022 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -31,19 +31,19 @@
 #include "arm_compute/runtime/NEON/NEScheduler.h"
 #include "src/common/utils/Log.h"
 #include "src/core/CPP/Validate.h"
+#include "src/core/NEON/kernels/assembly/winograd.hpp"
+#include "src/core/NEON/kernels/convolution/common/tensor.hpp"
 #include "src/core/NEON/kernels/convolution/common/utils.hpp"
-#include "src/core/NEON/kernels/convolution/winograd/winograd.hpp"
 #include "src/core/helpers/MemoryHelpers.h"
+#include "src/core/helpers/WindowHelpers.h"
+#include "src/core/utils/AssemblyUtils.h"
 #include "src/cpu/kernels/CpuWinogradConv2dKernel.h"
+#include "src/cpu/kernels/assembly/arm_gemm.hpp"
 #include "src/cpu/operators/CpuActivation.h"
 #include "src/cpu/operators/CpuPermute.h"
-#include "src/cpu/operators/CpuWinogradConv2d.h"
 #include "src/cpu/utils/CpuAuxTensorHandler.h"
-
 #include "support/Cast.h"
 
-#include <set>
-
 namespace arm_compute
 {
 namespace cpu
@@ -53,174 +53,20 @@
 
 namespace
 {
-arm_gemm::Activation arm_gemm_activation_from_acl_activation(const ActivationLayerInfo &act_info)
+inline Tensor4DShape internal_get_shape(const ITensorInfo *in)
 {
-    switch(act_info.activation())
-    {
-        case ActivationLayerInfo::ActivationFunction::RELU:
-        {
-            return arm_gemm::Activation(arm_gemm::Activation::Type::ReLU, act_info.a(), act_info.b());
-        }
-        case ActivationLayerInfo::ActivationFunction::BOUNDED_RELU:
-        {
-            return arm_gemm::Activation(arm_gemm::Activation::Type::BoundedReLU, act_info.a(), act_info.b());
-        }
-        default:
-        {
-            return arm_gemm::Activation(arm_gemm::Activation::Type::None);
-        }
-    }
-}
-
-inline Status validate_kernel_3x3(const Size2D input_dims, const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
-                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::F32);
-
-    if(src->data_type() == DataType::F32)
-    {
-        if(input_dims.width > 4 && input_dims.height > 4)
-        {
-            ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 4, 4, 3, 3>::validate(src, input0, winograd_info)));
-            ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 4, 4, 3, 3>::validate(weights, input1, winograd_info)));
-            ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 4, 4, 3, 3>::validate(batched_mm_output, biases, dst, winograd_info)));
-        }
-        else
-        {
-            ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 2, 2, 3, 3>::validate(src, input0, winograd_info)));
-            ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 2, 2, 3, 3>::validate(weights, input1, winograd_info)));
-            ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 2, 2, 3, 3>::validate(batched_mm_output, biases, dst, winograd_info)));
-        }
-    }
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-    else if(src->data_type() == DataType::F16)
-    {
-        ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<__fp16, 4, 4, 3, 3>::validate(src, input0, winograd_info)));
-        ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<__fp16, 4, 4, 3, 3>::validate(weights, input1, winograd_info)));
-        ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<__fp16, 4, 4, 3, 3>::validate(batched_mm_output, biases, dst, winograd_info)));
-    }
-#endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */
-
-    if(act_info.enabled())
-    {
-        CpuActivation::validate(dst, nullptr, act_info);
-    }
-    return Status{};
-}
-
-inline Status validate_kernel_5x5(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
-                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 2, 2, 5, 5>::validate(src, input0, winograd_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 2, 2, 5, 5>::validate(weights, input1, winograd_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 2, 2, 5, 5>::validate(batched_mm_output, biases, dst, winograd_info)));
-    if(act_info.enabled())
-    {
-        CpuActivation::validate(dst, nullptr, act_info);
-    }
-    return Status{};
-}
-
-inline Status validate_kernel_3x1(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
-                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 1, 6, 1, 3>::validate(src, input0, winograd_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 1, 6, 1, 3>::validate(weights, input1, winograd_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 1, 6, 1, 3>::validate(batched_mm_output, biases, dst, winograd_info)));
-    if(act_info.enabled())
-    {
-        CpuActivation::validate(dst, nullptr, act_info);
-    }
-    return Status{};
-}
-
-inline Status validate_kernel_1x3(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
-                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 6, 1, 3, 1>::validate(src, input0, winograd_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 6, 1, 3, 1>::validate(weights, input1, winograd_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 6, 1, 3, 1>::validate(batched_mm_output, biases, dst, winograd_info)));
-
-    if(act_info.enabled())
-    {
-        CpuActivation::validate(dst, nullptr, act_info);
-    }
-    return Status{};
-}
-
-inline Status validate_kernel_5x1(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
-                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 1, 4, 1, 5>::validate(src, input0, winograd_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 1, 4, 1, 5>::validate(weights, input1, winograd_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 1, 4, 1, 5>::validate(batched_mm_output, biases, dst, winograd_info)));
-    if(act_info.enabled())
-    {
-        CpuActivation::validate(dst, nullptr, act_info);
-    }
-    return Status{};
-}
-inline Status validate_kernel_1x5(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
-                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 4, 1, 5, 1>::validate(src, input0, winograd_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 4, 1, 5, 1>::validate(weights, input1, winograd_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 4, 1, 5, 1>::validate(batched_mm_output, biases, dst, winograd_info)));
-    if(act_info.enabled())
-    {
-        CpuActivation::validate(dst, nullptr, act_info);
-    }
-    return Status{};
-}
-
-inline Status validate_kernel_7x1(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
-                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 1, 2, 1, 7>::validate(src, input0, winograd_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 1, 2, 1, 7>::validate(weights, input1, winograd_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 1, 2, 1, 7>::validate(batched_mm_output, biases, dst, winograd_info)));
-    if(act_info.enabled())
-    {
-        CpuActivation::validate(dst, nullptr, act_info);
-    }
-    return Status{};
-}
-
-inline Status validate_kernel_1x7(const ITensorInfo *src, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
-                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
-{
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F32);
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformInputKernel<float, 2, 1, 7, 1>::validate(src, input0, winograd_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformWeightsKernel<float, 2, 1, 7, 1>::validate(weights, input1, winograd_info)));
-    ARM_COMPUTE_RETURN_ON_ERROR((CpuWinogradConv2dTransformOutputKernel<float, 2, 1, 7, 1>::validate(batched_mm_output, biases, dst, winograd_info)));
-
-    if(act_info.enabled())
-    {
-        CpuActivation::validate(dst, nullptr, act_info);
-    }
-    return Status{};
-}
-
-inline Tensor4DShape internal_get_input_shape(const ITensorInfo *src)
-{
-    const DataLayout data_layout = src->data_layout();
-    const int        in_width    = src->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH));
-    const int        in_height   = src->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT));
-    const int        in_channels = src->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL));
-    const int        in_batches  = src->dimension(3);
+    const DataLayout data_layout = in->data_layout();
+    const int        in_width    = in->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::WIDTH));
+    const int        in_height   = in->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::HEIGHT));
+    const int        in_channels = in->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL));
+    const int        in_batches  = in->dimension(get_data_layout_dimension_index(data_layout, DataLayoutDimension::BATCHES));
 
     return Tensor4DShape{ in_batches, in_height, in_width, in_channels };
 }
 
 Status validate_arguments(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst, const PadStrideInfo &conv_info)
 {
-    ARM_COMPUTE_UNUSED(dst);
+    ARM_COMPUTE_UNUSED(dst, weights);
     ARM_COMPUTE_RETURN_ERROR_ON_CPU_F16_UNSUPPORTED(src);
 
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd layer only supports unit strides.");
@@ -229,108 +75,85 @@
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, biases);
         ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
     }
-    return ICpuWinogradConv2dTransformWeightsKernel::validate(src, weights);
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(src, 1, DataType::F16, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(src, weights);
+    return Status{};
 }
-Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims, DataType data_type)
+
+bool get_winograd_kernel_implementation(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *dst,
+                                        const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, bool enable_fast_math,
+                                        arm_conv::winograd::WinogradImpl *winograd_impl, std::unique_ptr<arm_conv::ConvolutionArgs> &conv_args)
 {
-    Size2D output_tile = Size2D{};
-    if(kernel_dims == Size2D(3U, 3U))
+    arm_conv::winograd::WinogradConfig winograd_cfg;
+    arm_gemm::GemmConfig               cfg;
+
+    const DataType data_type = src->data_type();
+    Tensor4DShape  in_shape{ internal_get_shape(src) };
+    Tensor4DShape  out_shape{ internal_get_shape(dst) };
+    Tensor4DShape  kernel_shape{ internal_get_shape(weights) };
+    uint32_t       nthreads = NEScheduler::get().num_threads();
+    // Get configuration arguments for Winograd
+    winograd_cfg.output_rows = 0;
+    winograd_cfg.output_cols = 0;
+    conv_args                = std::make_unique<arm_conv::ConvolutionArgs>(
+                                   in_shape.n_batches,
+                                   arm_conv::Shape2D{ static_cast<uint32_t>(in_shape.n_rows), static_cast<uint32_t>(in_shape.n_cols) },
+                                   in_shape.n_channels,
+                                   conv_info.pad_top(),
+                                   conv_info.pad_left(),
+                                   arm_conv::Shape2D{ static_cast<uint32_t>(out_shape.n_rows), static_cast<uint32_t>(out_shape.n_cols) },
+                                   out_shape.n_channels,
+                                   arm_conv::Shape2D{ static_cast<uint32_t>(kernel_shape.n_rows), static_cast<uint32_t>(kernel_shape.n_cols) },
+                                   assembly_utils::map_to_arm_gemm_activation(act_info));
+
+    bool success = false;
+    if(data_type == DataType::F32)
     {
-        output_tile = (input_dims.width <= 4 || input_dims.height <= 4) ? Size2D(2U, 2U) : Size2D(4U, 4U);
-        if(data_type == DataType::F16)
-        {
-            output_tile = Size2D(4U, 4U);
-        }
+        success = arm_conv::winograd::get_implementation<float>(
+                      *winograd_impl, &CPUInfo::get(), *conv_args, nthreads, enable_fast_math, &winograd_cfg, nullptr);
     }
-    else if(kernel_dims == Size2D(5U, 5U))
+#if defined(__aarch64__) && defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
+    else if(data_type == DataType::F16)
     {
-        output_tile = Size2D(2U, 2U);
+        success = arm_conv::winograd::get_implementation<__fp16>(
+                      *winograd_impl, &CPUInfo::get(), *conv_args, nthreads, enable_fast_math, &winograd_cfg, nullptr);
     }
-    else if(kernel_dims == Size2D(1U, 3U))
+#endif // defined(__aarch64__) && defined(__ARM_FEATURE_FP16_VECTOR_ARITHMETIC)
+    else
     {
-        output_tile = Size2D(1U, 6U);
+        success = false;
     }
-    else if(kernel_dims == Size2D(3U, 1U))
-    {
-        output_tile = Size2D(6U, 1U);
-    }
-    else if(kernel_dims == Size2D(1U, 5U))
-    {
-        output_tile = Size2D(1U, 4U);
-    }
-    else if(kernel_dims == Size2D(5U, 1U))
-    {
-        output_tile = Size2D(4U, 1U);
-    }
-    else if(kernel_dims == Size2D(7U, 1U))
-    {
-        output_tile = Size2D(2U, 1U);
-    }
-    else if(kernel_dims == Size2D(1U, 7U))
-    {
-        output_tile = Size2D(1U, 2U);
-    }
-    return output_tile;
+    return success;
 }
-
-bool check_support_fast_math(const Size2D &output_tile, const Size2D &kernel_size, DataType data_type)
-{
-    // Check if we want to configure a Winograd configuration which requires fast math
-    using WinogradConfiguration = std::pair<std::pair<int, int>, std::pair<int, int>>;
-
-    const std::vector<WinogradConfiguration> fast_math_winograd_f16 =
-    {
-        WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(3, 3))
-    };
-
-    const std::vector<WinogradConfiguration> fast_math_winograd_f32 =
-    {
-        WinogradConfiguration(std::pair<int, int>(2, 2), std::pair<int, int>(5, 5)),
-        WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5))
-    };
-
-    auto p = std::make_pair(std::pair<int, int>(output_tile.width, output_tile.height),
-                            std::pair<int, int>(kernel_size.width, kernel_size.height));
-
-    switch(data_type)
-    {
-        case DataType::F16:
-            return std::find(fast_math_winograd_f16.begin(), fast_math_winograd_f16.end(), p) != fast_math_winograd_f16.end();
-        case DataType::F32:
-            return std::find(fast_math_winograd_f32.begin(), fast_math_winograd_f32.end(), p) != fast_math_winograd_f32.end();
-        default:
-            return false;
-    }
-}
-
 inline bool fuse_function_supported(const ActivationLayerInfo &act_info)
 {
     return act_info.activation() == ActivationLayerInfo::ActivationFunction::RELU || act_info.activation() == ActivationLayerInfo::ActivationFunction::BOUNDED_RELU;
 }
-
 } // namespace
 
 CpuWinogradConv2d::CpuWinogradConv2d()
+
     : _gemm_function(std::make_unique<CpuGemm>()),
       _activation_func(std::make_unique<CpuActivation>()),
+      _transform_input_kernel(nullptr),
+      _transform_output_kernel(nullptr),
       _permute_input(std::make_unique<CpuPermute>()),
       _permute_output(std::make_unique<CpuPermute>()),
       _permute_weights(std::make_unique<CpuPermute>()),
-      _transform_input_kernel(nullptr),
-      _transform_weights_kernel(nullptr),
-      _transform_output_kernel(nullptr),
-      _data_layout(),
       _aux_mem(AuxTensorIdx::Count),
+      _conv_args{ nullptr },
+      _winograd_impl{},
+      _data_layout(),
+      _winograd_transformed_input{},
+      _winograd_transformed_output{},
+      _winograd_transformed_weights{},
+      _input_workspace(),
+      _output_workspace(),
+      _weights_hwio(),
       _input_nhwc(),
       _output_nhwc(),
-      _input_workspace(),
-      _kernel_storage(),
-      _output_workspace(),
-      _input_transformed(),
-      _output_transformed(),
-      _weights_hwio(),
-      _run_activation(false),
-      _is_prepared(false)
+      _is_prepared{ false },
+      _run_activation{ false }
 {
 }
 
@@ -342,464 +165,199 @@
     ARM_COMPUTE_ERROR_ON_NULLPTR(src, weights, dst);
     ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(src, weights, biases, dst, conv_info));
     ARM_COMPUTE_LOG_PARAMS(src, weights, biases, dst, conv_info, act_info, enable_fast_math);
+    ARM_COMPUTE_UNUSED(biases);
+    const DataType data_type = src->data_type();
+    uint32_t       nthreads  = NEScheduler::get().num_threads();
+    _data_layout             = src->data_layout();
+    const Tensor4DShape kernel_shape{ internal_get_shape(weights) };
 
-    // Get indices for the width and height
-    _data_layout                   = src->data_layout();
-    const unsigned int width_idx   = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::WIDTH);
-    const unsigned int height_idx  = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::HEIGHT);
-    const unsigned int channel_idx = get_data_layout_dimension_index(_data_layout, DataLayoutDimension::CHANNEL);
+    bool success = get_winograd_kernel_implementation(src, weights, dst, conv_info, act_info, enable_fast_math, &_winograd_impl, _conv_args);
 
-    const Size2D   input_dims  = Size2D(src->dimension(width_idx), src->dimension(height_idx));
-    const Size2D   kernel_size = Size2D(weights->dimension(width_idx), weights->dimension(height_idx));
-    const DataType data_type   = src->data_type();
-    const Size2D   output_tile = winograd_output_tile(input_dims, kernel_size, data_type);
+    ARM_COMPUTE_EXIT_ON_MSG_VAR(!success, "Unsupported kernel size: %d x %d.\n", kernel_shape.n_rows, kernel_shape.n_cols);
+    ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using input transform: %s\n", _winograd_impl.input_transform->get_name().c_str());
+    ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using weight transform: %s\n", _winograd_impl.input_transform->get_name().c_str());
+    ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using output transform: %s\n", _winograd_impl.input_transform->get_name().c_str());
 
-    // Check if the Winograd configuration requires fast math
-    if(!enable_fast_math)
+    const bool has_impl = ((_winograd_impl.input_transform != nullptr) && (_winograd_impl.output_transform != nullptr) && (_winograd_impl.gemm_args != nullptr));
+    if(has_impl)
     {
-        ARM_COMPUTE_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size, data_type),
-                                 "This Winograd configuration requires enable_fast_math=true");
-    }
+        // Determine how much working space is required, allocate it.
+        const size_t input_workspace_size  = _winograd_impl.input_transform->get_working_space_size(*_conv_args, nthreads);
+        const size_t output_workspace_size = _winograd_impl.output_transform->get_working_space_size(*_conv_args, nthreads);
 
-    _is_prepared = false;
+        TensorInfo input_workspace_info(TensorShape(input_workspace_size), 1, DataType::U8);
+        TensorInfo output_workspace_info(TensorShape(output_workspace_size), 1, DataType::U8);
+        _input_workspace  = input_workspace_info;
+        _output_workspace = output_workspace_info;
 
-    std::unique_ptr<ICpuWinogradConv2dTransformInputKernel>   transform_input_kernel;
-    std::unique_ptr<ICpuWinogradConv2dTransformWeightsKernel> transform_weights_kernel;
-    std::unique_ptr<ICpuWinogradConv2dTransformOutputKernel>  transform_output_kernel;
+        const auto &wds = _winograd_impl.winograd_spec;
 
-    int n_gemms = 1;
-    int N_BLOCK = 1; // Size of block used by GEMM.
-    if(data_type == DataType::F32)
-    {
-        if(kernel_size == Size2D(3, 3))
+        // Preparing winograd transformed input tensor
+        const size_t     data_type_size    = src->element_size();
+        const uint32_t   m                 = _winograd_impl.gemm_args->_Msize; // Total number of tiles
+        const uint32_t   k                 = _winograd_impl.gemm_args->_Ksize; // Input channels
+        const uint32_t   n                 = _winograd_impl.gemm_args->_Nsize; // Output channels
+        const uint32_t   n_gemms           = _winograd_impl.gemm_args->_nmulti;
+        const uint32_t   n_batches         = _winograd_impl.gemm_args->_nbatches;
+        constexpr size_t storage_alignment = 64;
+
+        const TensorShape a_shape(k, m, n_batches, n_gemms);
+        Strides           a_strides(data_type_size);
+        a_strides.set(1, data_type_size * _winograd_impl.winograd_spec.input_ld_row);
+        a_strides.set(2, data_type_size * _winograd_impl.winograd_spec.input_ld_batch);
+        a_strides.set(3, data_type_size * _winograd_impl.winograd_spec.input_ld_matrix);
+
+        const TensorShape b_shape(n, k, n_gemms);
+        Strides           b_strides(data_type_size);
+        b_strides.set(1, data_type_size * _winograd_impl.winograd_spec.weight_ld_row);
+        b_strides.set(2, data_type_size * _winograd_impl.winograd_spec.weight_ld_matrix);
+
+        const TensorShape d_shape(n, m, n_batches, n_gemms);
+        Strides           d_strides(data_type_size);
+        d_strides.set(1, data_type_size * _winograd_impl.winograd_spec.output_ld_row);
+        d_strides.set(2, data_type_size * _winograd_impl.winograd_spec.output_ld_batch);
+        d_strides.set(3, data_type_size * _winograd_impl.winograd_spec.output_ld_matrix);
+
+        TensorInfo a_info{};
+        TensorInfo b_info{};
+        TensorInfo d_info{};
+        a_info.init(a_shape, 1, data_type, a_strides, 0, wds.input_matrix_size_bytes);
+        b_info.init(b_shape, 1, data_type, b_strides, 0, wds.weight_matrix_size_bytes);
+        d_info.init(d_shape, 1, data_type, d_strides, 0, wds.output_matrix_size_bytes);
+
+        _winograd_transformed_input   = a_info;
+        _winograd_transformed_weights = b_info;
+        _winograd_transformed_output  = d_info;
+
+        PermutationVector weights_permutation_vector(3U, 0U, 1U, 2U);
+
+        // Configure the kernel to transform the input tensor from NCHW -> NHWC
+        if(_data_layout == DataLayout::NCHW)
         {
-            if(src->dimension(width_idx) > 4 && src->dimension(height_idx) > 4)
-            {
-                using config             = CpuWinogradConv2dConfiguration<float, float, 4, 4, 3, 3>;
-                transform_input_kernel   = std::make_unique<config::TransformInputKernel>();
-                transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
-                transform_output_kernel  = std::make_unique<config::TransformOutputKernel>();
-                n_gemms                  = config::WinogradBase::N_GEMMS;
-                N_BLOCK                  = config::WinogradConv::N_BLOCK;
-            }
-            else
-            {
-                using config             = CpuWinogradConv2dConfiguration<float, float, 2, 2, 3, 3>;
-                transform_input_kernel   = std::make_unique<config::TransformInputKernel>();
-                transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
-                transform_output_kernel  = std::make_unique<config::TransformOutputKernel>();
-                n_gemms                  = config::WinogradBase::N_GEMMS;
-                N_BLOCK                  = config::WinogradConv::N_BLOCK;
-            }
+            _permute_input->configure(src, &_input_nhwc, PermutationVector(2U, 0U, 1U));
+            weights_permutation_vector = PermutationVector(3U, 2U, 0U, 1U);
         }
-        else if(kernel_size == Size2D(5, 5))
+
+        // Re-order a weight tensor from [Output feature map x Input feature map x Height x Width] to [Height x Width x Input feature map x Output feature map]
+        _permute_weights->configure(weights, &_weights_hwio, weights_permutation_vector);
+
+        // Reorder the convoluted output to ACL's ordering NCHW
+        if(_data_layout == DataLayout::NCHW)
         {
-            using config             = CpuWinogradConv2dConfiguration<float, float, 2, 2, 5, 5>;
-            transform_input_kernel   = std::make_unique<config::TransformInputKernel>();
-            transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
-            transform_output_kernel  = std::make_unique<config::TransformOutputKernel>();
-            n_gemms                  = config::WinogradBase::N_GEMMS;
-            N_BLOCK                  = config::WinogradConv::N_BLOCK;
+            // configure and allocate dst tensor to be used to convert from winograd domain to spatial domain when calling to reshape_output()
+            TensorInfo info(TensorShape(dst->dimension(2), dst->dimension(0),
+                                        dst->dimension(1), dst->dimension(3)),
+                            1, dst->data_type());
+            _output_nhwc = info;
+            _permute_output->configure(&_output_nhwc, dst, PermutationVector(1U, 2U, 0U));
         }
-        else if(kernel_size == Size2D(1, 3))
+
+        // Configure GEMM function
+        _gemm_function->configure(&_winograd_transformed_input, &_winograd_transformed_weights, nullptr, &_winograd_transformed_output, 1.0f, 0.f);
+
+        //Configure Activation Layer
+        _run_activation = act_info.enabled() && !fuse_function_supported(act_info);
+        if(_run_activation)
         {
-            using config             = CpuWinogradConv2dConfiguration<float, float, 6, 1, 3, 1>;
-            transform_input_kernel   = std::make_unique<config::TransformInputKernel>();
-            transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
-            transform_output_kernel  = std::make_unique<config::TransformOutputKernel>();
-            n_gemms                  = config::WinogradBase::N_GEMMS;
-            N_BLOCK                  = config::WinogradConv::N_BLOCK;
+            _activation_func->configure(dst, nullptr, act_info);
         }
-        else if(kernel_size == Size2D(3, 1))
+
+        auto asm_mem_req         = _gemm_function->workspace();
+        _aux_mem[GemmWorkspace]  = asm_mem_req[GemmWorkspace];
+        _aux_mem[Pretranspose]   = asm_mem_req[Pretranspose];
+        _aux_mem[InterleavedLHS] = asm_mem_req[InterleavedLHS];
+        _aux_mem[TransposedRHS]  = asm_mem_req[TransposedRHS];
+        _aux_mem[TempResult]     = asm_mem_req[TempResult];
+
+        // Request temporary memory. Overlap memory needed for Input/Output transformations as they run on different non-overlapping time-steps.
+        _aux_mem[TransformedInput]   = MemoryInfo(offset_int_vec(TransformedInput), MemoryLifetime::Temporary, wds.input_matrix_size_bytes, storage_alignment);
+        _aux_mem[TransformedOutput]  = MemoryInfo(offset_int_vec(TransformedOutput), MemoryLifetime::Temporary, wds.output_matrix_size_bytes, storage_alignment);
+        _aux_mem[WorkspaceIO]        = MemoryInfo(offset_int_vec(WorkspaceIO), MemoryLifetime::Temporary, std::max(input_workspace_size, output_workspace_size));
+        _aux_mem[PermutedWeights]    = MemoryInfo(offset_int_vec(PermutedWeights), MemoryLifetime::Prepare, _weights_hwio.total_size());
+        _aux_mem[TransformedWeights] = MemoryInfo(offset_int_vec(TransformedWeights), MemoryLifetime::Persistent, wds.weight_matrix_size_bytes, storage_alignment);
+        if(_data_layout == DataLayout::NCHW)
         {
-            using config             = CpuWinogradConv2dConfiguration<float, float, 1, 6, 1, 3>;
-            transform_input_kernel   = std::make_unique<config::TransformInputKernel>();
-            transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
-            transform_output_kernel  = std::make_unique<config::TransformOutputKernel>();
-            n_gemms                  = config::WinogradBase::N_GEMMS;
-            N_BLOCK                  = config::WinogradConv::N_BLOCK;
+            _aux_mem[PermutedInput].merge(offset_int_vec(PermutedInput), src->total_size());
+            _aux_mem[PermutedOutput].merge(offset_int_vec(PermutedOutput), dst->total_size());
         }
-        else if(kernel_size == Size2D(1, 5))
-        {
-            using config             = CpuWinogradConv2dConfiguration<float, float, 4, 1, 5, 1>;
-            transform_input_kernel   = std::make_unique<config::TransformInputKernel>();
-            transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
-            transform_output_kernel  = std::make_unique<config::TransformOutputKernel>();
-            n_gemms                  = config::WinogradBase::N_GEMMS;
-            N_BLOCK                  = config::WinogradConv::N_BLOCK;
-        }
-        else if(kernel_size == Size2D(5, 1))
-        {
-            using config             = CpuWinogradConv2dConfiguration<float, float, 1, 4, 1, 5>;
-            transform_input_kernel   = std::make_unique<config::TransformInputKernel>();
-            transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
-            transform_output_kernel  = std::make_unique<config::TransformOutputKernel>();
-            n_gemms                  = config::WinogradBase::N_GEMMS;
-            N_BLOCK                  = config::WinogradConv::N_BLOCK;
-        }
-        else if(kernel_size == Size2D(1, 7))
-        {
-            using config             = CpuWinogradConv2dConfiguration<float, float, 2, 1, 7, 1>;
-            transform_input_kernel   = std::make_unique<config::TransformInputKernel>();
-            transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
-            transform_output_kernel  = std::make_unique<config::TransformOutputKernel>();
-            n_gemms                  = config::WinogradBase::N_GEMMS;
-            N_BLOCK                  = config::WinogradConv::N_BLOCK;
-        }
-        else if(kernel_size == Size2D(7, 1))
-        {
-            using config             = CpuWinogradConv2dConfiguration<float, float, 1, 2, 1, 7>;
-            transform_input_kernel   = std::make_unique<config::TransformInputKernel>();
-            transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
-            transform_output_kernel  = std::make_unique<config::TransformOutputKernel>();
-            n_gemms                  = config::WinogradBase::N_GEMMS;
-            N_BLOCK                  = config::WinogradConv::N_BLOCK;
-        }
-        else
-        {
-            ARM_COMPUTE_ERROR("Not supported.");
-        }
-    }
-#ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-    else if(data_type == DataType::F16)
-    {
-        if(kernel_size == Size2D(3, 3))
-        {
-            using config             = CpuWinogradConv2dConfiguration<__fp16, __fp16, 4, 4, 3, 3>;
-            transform_input_kernel   = std::make_unique<config::TransformInputKernel>();
-            transform_weights_kernel = std::make_unique<config::TransformWeightsKernel>();
-            transform_output_kernel  = std::make_unique<config::TransformOutputKernel>();
-            n_gemms                  = config::WinogradBase::N_GEMMS;
-            N_BLOCK                  = config::WinogradConv::N_BLOCK;
-        }
-        else
-        {
-            ARM_COMPUTE_ERROR("Not supported.");
-        }
-    }
-#endif // __ARM_FEATURE_FP16_VECTOR_ARITHMETIC
-    else
-    {
-        ARM_COMPUTE_ERROR("Not supported.");
-    }
-
-    const PaddingType use_padding_type = (conv_info.pad_top() != 0u || conv_info.pad_left() != 0) ? PADDING_SAME : PADDING_VALID;
-    const bool        use_same_padding = use_padding_type == PADDING_SAME;
-
-    // Get convolved dimensions
-    const int in_channels  = src->dimension(channel_idx);
-    const int out_channels = dst->dimension(channel_idx);
-
-    const Tensor4DShape in_shape(internal_get_input_shape(src));
-    const size_t        data_type_size = src->element_size();
-    // Get the memory required to instantiate a new Winograd operator.
-    constexpr size_t storage_alignment = 64;
-
-    // Kernel Storage
-    const size_t kernel_storage_size = transform_weights_kernel->get_weight_storage_size(out_channels, in_channels) * data_type_size;
-
-    // Input storage
-    const size_t input_storage_size = transform_input_kernel->get_input_storage_size(in_shape.n_batches, in_shape.n_channels, in_shape.n_rows, in_shape.n_cols, use_same_padding) * data_type_size;
-
-    // Output storage
-    const size_t output_storage_size  = transform_output_kernel->get_output_storage_size(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels) * data_type_size;
-    const int    kernel_matrix_stride = transform_weights_kernel->get_matrix_stride(out_channels, in_channels);
-    const int    output_matrix_stride = transform_output_kernel->get_matrix_stride(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels);
-    const auto   output_shape         = transform_output_kernel->get_output_shape(in_shape.n_rows, in_shape.n_cols, use_padding_type == PADDING_SAME);
-    const int    input_matrix_stride  = transform_input_kernel->get_matrix_stride(in_shape.n_batches, in_channels, in_shape.n_rows, in_shape.n_cols, use_padding_type == PADDING_SAME);
-
-    // Configure GEMM
-    const int tile_rows                = iceildiv(output_shape.first, output_tile.height);
-    const int tile_cols                = iceildiv(output_shape.second, output_tile.width);
-    const int m                        = in_shape.n_batches * tile_rows * tile_cols;
-    const int k                        = in_shape.n_channels;
-    const int n                        = out_channels;
-    const int kernel_matrix_row_stride = roundup(out_channels, N_BLOCK);
-    const int output_matrix_row_stride = kernel_matrix_row_stride;
-
-    TensorShape a_shape(k, m, 1, n_gemms);
-    Strides     a_strides(data_type_size);
-    a_strides.set(1, a_strides[0] * k);
-    //a_strides.set(2, data_type_size * input_matrix_stride / n_gemms); FIXME: This is the real batch size, but RSH's code crashes if it's not 0.
-    a_strides.set(2, 0);
-    a_strides.set(3, data_type_size * input_matrix_stride);
-
-    TensorShape b_shape(n, k, n_gemms);
-    Strides     b_strides(data_type_size);
-    b_strides.set(1, data_type_size * kernel_matrix_row_stride);
-    b_strides.set(2, data_type_size * kernel_matrix_stride);
-
-    TensorShape d_shape(n, m, 1, n_gemms);
-    Strides     d_strides(data_type_size);
-    d_strides.set(1, data_type_size * output_matrix_row_stride);
-    //d_strides.set(2, data_type_size * output_matrix_stride / n_gemms); FIXME: This is the real batch size, but RSH's code crashes if it's not 0.
-    d_strides.set(2, 0);
-    d_strides.set(3, data_type_size * output_matrix_stride);
-
-    TensorInfo a_info{};
-    TensorInfo b_info{};
-    TensorInfo d_info{};
-    a_info.init(a_shape, 1, data_type, a_strides, 0, input_storage_size);
-    b_info.init(b_shape, 1, data_type, b_strides, 0, kernel_storage_size);
-    d_info.init(d_shape, 1, data_type, d_strides, 0, output_storage_size);
-
-    _input_transformed  = a_info;
-    _kernel_storage     = b_info;
-    _output_transformed = d_info;
-
-    const ITensorInfo *input_to_use  = src;
-    ITensorInfo       *output_to_use = dst;
-    PermutationVector  weights_permutation_vector(3U, 0U, 1U, 2U);
-    const unsigned int max_num_threads = NEScheduler::get().num_threads();
-
-    // Configure the kernel to transform the input tensor from NCHW -> NHWC
-    if(_data_layout == DataLayout::NCHW)
-    {
-        _permute_input->configure(src, &_input_nhwc, PermutationVector(2U, 0U, 1U));
-        input_to_use               = &_input_nhwc;
-        weights_permutation_vector = PermutationVector(3U, 2U, 0U, 1U);
-    }
-
-    // Configure input transform kernel
-    transform_input_kernel->configure(input_to_use, in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, in_shape.n_channels, use_padding_type,
-                                      &_input_transformed, input_matrix_stride, &_input_workspace);
-    const size_t input_workspace_size = transform_input_kernel->get_working_space_size(max_num_threads);
-    TensorInfo   input_workspace_info(TensorShape(input_workspace_size), 1, DataType::U8);
-    _input_workspace = input_workspace_info;
-
-    // Re-order a weight tensor from [Output feature map x Input feature map x Height x Width] to [Height x Width x Input feature map x Output feature map]
-    _permute_weights->configure(weights, &_weights_hwio, weights_permutation_vector);
-    transform_weights_kernel->configure(&_weights_hwio, &_kernel_storage, kernel_matrix_stride, out_channels, in_channels);
-
-    // Configure GEMM function
-    _gemm_function->configure(&_input_transformed, &_kernel_storage, nullptr, &_output_transformed, 1.0f, 0.f);
-
-    // Configure output transform function
-    // The biases tensor has not been allocated at this point in time, the output transform will add the biases to the final result in the run() method
-    if(_data_layout == DataLayout::NCHW)
-    {
-        // configure and allocate dst tensor to be used to convert from winograd domain to spatial domain when calling to reshape_output()
-        TensorInfo info(TensorShape(dst->dimension(2), dst->dimension(0),
-                                    dst->dimension(1), dst->dimension(3)),
-                        1, dst->data_type());
-        _output_nhwc  = info;
-        output_to_use = &_output_nhwc;
-    }
-    const arm_gemm::Activation activation = arm_gemm_activation_from_acl_activation(act_info);
-
-    transform_output_kernel->configure(biases,
-                                       &_output_transformed,
-                                       output_matrix_stride,
-                                       output_to_use,
-                                       in_shape.n_batches,
-                                       output_shape.first,
-                                       output_shape.second,
-                                       out_channels,
-                                       &_output_workspace,
-                                       activation);
-
-    const size_t output_workspace_size = transform_output_kernel->get_working_space_size(max_num_threads);
-    TensorInfo   output_workspace_info(TensorShape(output_workspace_size), 1, DataType::U8);
-    _output_workspace = output_workspace_info;
-
-    // Reorder the convoluted output to ACL's ordering NCHW
-    if(_data_layout == DataLayout::NCHW)
-    {
-        _permute_output->configure(&_output_nhwc, dst, PermutationVector(1U, 2U, 0U));
-    }
-
-    _transform_input_kernel   = std::move(transform_input_kernel);
-    _transform_weights_kernel = std::move(transform_weights_kernel);
-    _transform_output_kernel  = std::move(transform_output_kernel);
-
-    //Configure Activation Layer
-    _run_activation = act_info.enabled() && !fuse_function_supported(act_info);
-    if(_run_activation)
-    {
-        _activation_func->configure(dst, nullptr, act_info);
-    }
-
-    auto asm_mem_req         = _gemm_function->workspace();
-    _aux_mem[GemmWorkspace]  = asm_mem_req[GemmWorkspace];
-    _aux_mem[Pretranspose]   = asm_mem_req[Pretranspose];
-    _aux_mem[InterleavedLHS] = asm_mem_req[InterleavedLHS];
-    _aux_mem[TransposedRHS]  = asm_mem_req[TransposedRHS];
-    _aux_mem[TempResult]     = asm_mem_req[TempResult];
-
-    // Request temporary memory. Overlap memory needed for Input/Output transformations as they run on different non-overlapping time-steps.
-    _aux_mem[TransformedInput]   = MemoryInfo(offset_int_vec(TransformedInput), MemoryLifetime::Temporary, input_storage_size, storage_alignment);
-    _aux_mem[TransformedOutput]  = MemoryInfo(offset_int_vec(TransformedOutput), MemoryLifetime::Temporary, output_storage_size, storage_alignment);
-    _aux_mem[WorkspaceIO]        = MemoryInfo(offset_int_vec(WorkspaceIO), MemoryLifetime::Temporary, std::max(input_workspace_size, output_workspace_size));
-    _aux_mem[PermutedWeights]    = MemoryInfo(offset_int_vec(PermutedWeights), MemoryLifetime::Prepare, _weights_hwio.total_size());
-    _aux_mem[TransformedWeights] = MemoryInfo(offset_int_vec(TransformedWeights), MemoryLifetime::Persistent, kernel_storage_size, storage_alignment);
-    if(_data_layout == DataLayout::NCHW)
-    {
-        _aux_mem[PermutedInput].merge(offset_int_vec(PermutedInput), src->total_size());
-        _aux_mem[PermutedOutput].merge(offset_int_vec(PermutedOutput), dst->total_size());
     }
 }
-
 Status CpuWinogradConv2d::validate(const ITensorInfo *src, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *dst,
                                    const PadStrideInfo &conv_info, const ActivationLayerInfo &act_info, bool enable_fast_math)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(src, weights, dst);
     ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(src, weights, biases, dst, conv_info));
 
-    // Get indices for the width and height
-    const size_t idx_width  = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::WIDTH);
-    const size_t idx_height = get_data_layout_dimension_index(src->data_layout(), DataLayoutDimension::HEIGHT);
+    const Tensor4DShape              kernel_shape{ internal_get_shape(weights) };
+    arm_conv::winograd::WinogradImpl winograd_impl{};
 
-    // Input shape, kernel size and output tile
-    const Size2D   input_dims  = Size2D(src->dimension(idx_width), src->dimension(idx_height));
-    const Size2D   kernel_size = Size2D(weights->dimension(idx_width), weights->dimension(idx_height));
-    const DataType data_type   = src->data_type();
-    const Size2D   output_tile = winograd_output_tile(input_dims, kernel_size, data_type);
+    std::unique_ptr<arm_conv::ConvolutionArgs> conv_args;
+    const bool                                 success = get_winograd_kernel_implementation(src, weights, dst, conv_info, act_info, enable_fast_math, &winograd_impl, conv_args);
 
-    // Check if the Winograd configuration requires fast math
-    if(!enable_fast_math)
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(check_support_fast_math(output_tile, kernel_size, data_type),
-                                        "This Winograd configuration requires enable_fast_math=true");
-    }
-
-    const WinogradInfo winograd_info = WinogradInfo(output_tile,
-                                                    kernel_size,
-                                                    input_dims,
-                                                    conv_info,
-                                                    src->data_layout());
-
-    // Validate input transform
-    const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*src, winograd_info);
-    const TensorInfo  input0       = src->clone()->set_tensor_shape(input0_shape);
-    // Validate filter transform
-    const TensorShape input1_shape = misc::shape_calculator::compute_winograd_filter_transform_shape(*weights, winograd_info);
-    const TensorInfo  input1       = weights->clone()->set_tensor_shape(input1_shape);
-    // Validate batched matrix multiply
-    TensorShape batched_mm_output_shape = input0.tensor_shape();
-    batched_mm_output_shape[0]          = input1.tensor_shape()[0];
-    const TensorInfo batched_mm_output  = input0.clone()->set_tensor_shape(batched_mm_output_shape);
-
-    if(kernel_size == Size2D(3, 3))
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 1, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 1, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 1, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 1, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != conv_info.pad_left(), "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_bottom(), "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_left(), "Only SAME or VALID padding supported");
-        return validate_kernel_3x3(input_dims, src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
-    }
-    else if(kernel_size == Size2D(5, 5))
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 2, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 2, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 2, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 2, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != conv_info.pad_left(), "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_bottom(), "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != conv_info.pad_left(), "Only SAME or VALID padding supported");
-        return validate_kernel_5x5(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
-    }
-    if(kernel_size == Size2D(3, 1))
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 1, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 1, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_bottom() != 0, "Only SAME or VALID padding supported");
-        return validate_kernel_3x1(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
-    }
-    else if(kernel_size == Size2D(1, 3))
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 1, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 1, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_right() != 0, "Only SAME or VALID padding supported");
-        return validate_kernel_1x3(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
-    }
-    else if(kernel_size == Size2D(5, 1))
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 2, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 2, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_bottom() != 0, "Only SAME or VALID padding supported");
-        return validate_kernel_5x1(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
-    }
-    else if(kernel_size == Size2D(1, 5))
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 2, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 2, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_right() != 0, "Only SAME or VALID padding supported");
-        return validate_kernel_1x5(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
-    }
-    else if(kernel_size == Size2D(7, 1))
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_left() != 3, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_right() != 0u && conv_info.pad_right() != 3, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_bottom() != 0, "Only SAME or VALID padding supported");
-        return validate_kernel_7x1(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
-    }
-    else if(kernel_size == Size2D(1, 7))
-    {
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_top() != 0u && conv_info.pad_top() != 3, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_bottom() != 0u && conv_info.pad_bottom() != 3, "Only SAME or VALID padding supported");
-        ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.pad_left() != 0u && conv_info.pad_right() != 0, "Only SAME or VALID padding supported");
-        return validate_kernel_1x7(src, &input0, &input1, &batched_mm_output, weights, biases, dst, winograd_info, act_info);
-    }
-    else
-    {
-        ARM_COMPUTE_RETURN_ERROR_MSG("Kernel shape not supported");
-    }
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG_VAR(success == false, "Unsupported kernel size: %d x %d.\n", kernel_shape.n_rows, kernel_shape.n_cols);
+    ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using input transform: %s\n", winograd_impl.input_transform->get_name().c_str());
+    ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using weight transform: %s\n", winograd_impl.input_transform->get_name().c_str());
+    ARM_COMPUTE_LOG_MSG_WITH_FORMAT_ACL(arm_compute::logging::LogLevel::INFO, "Using output transform: %s\n", winograd_impl.input_transform->get_name().c_str());
+    return Status{};
 }
 
 void CpuWinogradConv2d::run(ITensorPack &tensors)
 {
     prepare(tensors);
+    auto   src    = tensors.get_const_tensor(ACL_SRC_0);
+    auto   biases = tensors.get_const_tensor(ACL_SRC_2);
+    auto   output = tensors.get_tensor(ACL_DST);
+    Window win;
 
-    auto a = tensors.get_const_tensor(ACL_SRC_0);
-    auto c = tensors.get_const_tensor(ACL_SRC_2);
-    auto d = tensors.get_tensor(ACL_DST);
+    const uint32_t nthreads = NEScheduler::get().num_threads();
 
+    // The Winograd transform implementation does fine-grain threading inside the transforms. Just pass thread_id and nthreads.
+    win.set(Window::DimX, Window::Dimension(0, nthreads, 1));
+
+    // Wrap the winograd-domain tensorInfos created in configuration in tensors and allocate the required memory.
     CpuAuxTensorHandler input_nhwc(offset_int_vec(PermutedInput), _input_nhwc, tensors, true);
-    CpuAuxTensorHandler input_transformed(offset_int_vec(TransformedInput), _input_transformed, tensors, true);
+    CpuAuxTensorHandler winograd_input_transformed(offset_int_vec(TransformedInput), _winograd_transformed_input, tensors, true);
     CpuAuxTensorHandler input_workspace(offset_int_vec(WorkspaceIO), _input_workspace, tensors, true);
-
-    const bool is_nchw = _data_layout == DataLayout::NCHW;
+    const bool          is_nchw = _data_layout == DataLayout::NCHW;
     if(is_nchw)
     {
         //Bring channels to the front as Winograd code expects the tensor to be in the format NHWC
-        ITensorPack pack{ { ACL_SRC, a }, { ACL_DST, input_nhwc.get() } };
+        ITensorPack pack{ { ACL_SRC, src }, { ACL_DST, input_nhwc.get() } };
         _permute_input->run(pack);
     }
 
-    // Transform input tensor to the winograd domain
-    ITensorPack transform_input_pack{ { ACL_SRC, is_nchw ? input_nhwc.get() : a }, { ACL_DST, input_transformed.get() }, { ACL_INT, input_workspace.get() } };
-    NEScheduler::get().schedule_op(_transform_input_kernel.get(), Window::DimX, _transform_input_kernel->window(), transform_input_pack);
+    CpuAuxTensorHandler winograd_output_transformed(offset_int_vec(TransformedOutput), _winograd_transformed_output, tensors, true);
+    CpuAuxTensorHandler output_workspace(offset_int_vec(WorkspaceIO), _output_workspace, tensors, true);
+    CpuAuxTensorHandler output_nhwc(offset_int_vec(PermutedOutput), _output_nhwc, tensors, true);
 
-    CpuAuxTensorHandler output_transformed(offset_int_vec(TransformedOutput), _output_transformed, tensors, true);
-    CpuAuxTensorHandler weights_transformed(offset_int_vec(TransformedWeights), _kernel_storage, tensors, true);
+    ITensorPack transform_input_pack{ { ACL_SRC, is_nchw ? input_nhwc.get() : src }, { ACL_DST, winograd_input_transformed.get() }, { ACL_INT, input_workspace.get() } };
+    _transform_input_kernel = std::make_unique<CpuWinogradConv2dTransformInputKernel>(_winograd_impl, *_conv_args, nthreads);
+
+    NEScheduler::get().schedule_op(_transform_input_kernel.get(), Window::DimX, win, transform_input_pack);
+
+    CpuAuxTensorHandler winograd_weights_transformed(offset_int_vec(TransformedWeights), _winograd_transformed_weights, tensors, true);
 
     // Run 16 GEMMs in multiple threads, each kernel runs one or more GEMMs
     ITensorPack gemm_pack = tensors;
-    gemm_pack.add_const_tensor(ACL_SRC, input_transformed.get());
-    gemm_pack.add_const_tensor(ACL_SRC_1, weights_transformed.get());
+    gemm_pack.add_const_tensor(ACL_SRC, winograd_input_transformed.get());
+    gemm_pack.add_const_tensor(ACL_SRC_1, winograd_weights_transformed.get());
     gemm_pack.add_const_tensor(ACL_BIAS, nullptr);
-    gemm_pack.add_tensor(ACL_DST, output_transformed.get());
+    gemm_pack.add_tensor(ACL_DST, winograd_output_transformed.get());
     _gemm_function->run(gemm_pack);
 
-    // Transform output tensor to the spatial domain
-    CpuAuxTensorHandler output_workspace(offset_int_vec(WorkspaceIO), _output_workspace, tensors, true);
-    CpuAuxTensorHandler output_nhwc(offset_int_vec(PermutedOutput), _output_nhwc, tensors, true);
-    ITensorPack         transform_output_pack{ { ACL_SRC_0, c }, { ACL_SRC_1, output_transformed.get() }, { ACL_DST, is_nchw ? output_nhwc.get() : d }, { ACL_INT, output_workspace.get() } };
-    NEScheduler::get().schedule_op(_transform_output_kernel.get(), Window::DimX, _transform_output_kernel->window(), transform_output_pack);
-
+    // Output transform
+    _transform_output_kernel = std::make_unique<CpuWinogradConv2dTransformOutputKernel>(_winograd_impl, *_conv_args, nthreads);
+    ITensorPack transform_output_pack{ { ACL_SRC_0, winograd_output_transformed.get() }, { ACL_DST, is_nchw ? output_nhwc.get() : output }, { ACL_SRC_1, biases }, { ACL_INT, output_workspace.get() } };
+    NEScheduler::get().schedule_op(_transform_output_kernel.get(), Window::DimX, win, transform_output_pack);
     if(is_nchw)
     {
         // Reorder the convoluted output to ACL's ordering NCHW
-        ITensorPack pack{ { ACL_SRC, output_nhwc.get() }, { ACL_DST, d } };
+        ITensorPack pack{ { ACL_SRC, output_nhwc.get() }, { ACL_DST, output } };
         _permute_output->run(pack);
     }
-
     if(_run_activation)
     {
-        ITensorPack pack{ { ACL_SRC, d }, { ACL_DST, d } };
+        ITensorPack pack{ { ACL_SRC, output }, { ACL_DST, output } };
         _activation_func->run(pack);
     }
 }
@@ -808,34 +366,54 @@
 {
     if(!_is_prepared)
     {
-        // Permute weights
         const ITensor *weights     = tensors.get_const_tensor(ACL_SRC_1);
         ITensor       *weights_aux = utils::cast::polymorphic_cast<ITensor *>(tensors.get_tensor(offset_int_vec(PermutedWeights)));
-        ARM_COMPUTE_ERROR_ON_NULLPTR(weights, weights_aux);
 
         CpuAuxTensorHandler permuted_weights(_weights_hwio, *weights_aux);
         ITensorPack         permute_tensors{ { ACL_SRC, weights }, { ACL_DST, permuted_weights.get() } };
         _permute_weights->run(permute_tensors);
+        const int element_size_in_bytes = permuted_weights.get()->info()->element_size();
+        // Weights were in OHWI format, before being permuted "permuted_weights" to be in HWIO format.
+        const unsigned int height_idx  = 3; // H in HWIO
+        const unsigned int width_idx   = 2; // W in HWIO
+        const unsigned int channel_idx = 1; // I in HWIO
 
-        // Transform weights
+        const int permuted_weight_row_stride     = permuted_weights.get()->info()->strides_in_bytes()[height_idx] / element_size_in_bytes;
+        const int permuted_weight_col_stride     = permuted_weights.get()->info()->strides_in_bytes()[width_idx] / element_size_in_bytes;
+        const int permuted_weight_channel_stride = permuted_weights.get()->info()->strides_in_bytes()[channel_idx] / element_size_in_bytes;
+
+        // Wrap the winograd-domain transformed weight TensorInfo in Auxiliary tensor and allocate the required memory.
         ITensor *weights_transf = utils::cast::polymorphic_cast<ITensor *>(tensors.get_tensor(offset_int_vec(TransformedWeights)));
         ARM_COMPUTE_ERROR_ON_NULLPTR(weights_transf);
+        CpuAuxTensorHandler winograd_transformed_weights(_winograd_transformed_weights, *weights_transf);
 
-        CpuAuxTensorHandler transformed_weights(_kernel_storage, *weights_transf);
-        ITensorPack         transform_tensors{ { ACL_SRC, permuted_weights.get() }, { ACL_DST, transformed_weights.get() } };
-        NEScheduler::get().schedule_op(_transform_weights_kernel.get(), Window::DimX, _transform_weights_kernel->window(), transform_tensors);
+        const void *permuted_weights_ptr;
+        void       *win_wght_transf_ptr;
 
+        permuted_weights_ptr = reinterpret_cast<const void *>(permuted_weights.get()->buffer() + permuted_weights.get()->info()->offset_first_element_in_bytes());
+        win_wght_transf_ptr  = reinterpret_cast<void *>(winograd_transformed_weights.get()->buffer() + winograd_transformed_weights.get()->info()->offset_first_element_in_bytes());
+
+        // Prepare Weights
+        _winograd_impl.weight_transform->execute(
+            *_conv_args,
+            permuted_weights_ptr,
+            permuted_weight_row_stride,
+            permuted_weight_col_stride,
+            permuted_weight_channel_stride,
+            win_wght_transf_ptr,
+            _winograd_impl.winograd_spec,
+            0, 1 // Thread 1 of 1
+        );
         ITensorPack gemm_pack = tensors;
-        gemm_pack.add_const_tensor(ACL_SRC_1, transformed_weights.get());
+        gemm_pack.add_const_tensor(ACL_SRC_1, winograd_transformed_weights.get());
         _gemm_function->prepare(gemm_pack);
-
-        _is_prepared = true;
+        _is_prepared = 1;
     }
 }
-
 experimental::MemoryRequirements CpuWinogradConv2d::workspace() const
 {
     return _aux_mem;
 }
+
 } // namespace cpu
-} // namespace arm_compute
\ No newline at end of file
+} // namespace arm_compute
diff --git a/src/cpu/operators/CpuWinogradConv2d.h b/src/cpu/operators/CpuWinogradConv2d.h
index 0abd110..e0df34e 100644
--- a/src/cpu/operators/CpuWinogradConv2d.h
+++ b/src/cpu/operators/CpuWinogradConv2d.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2021 Arm Limited.
+ * Copyright (c) 2021-2022 Arm Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -29,6 +29,7 @@
 #include "src/core/common/Macros.h"
 #include "src/cpu/ICpuOperator.h"
 #include "src/cpu/kernels/CpuWinogradConv2dKernel.h"
+#include "src/cpu/kernels/assembly/gemm_common.hpp"
 #include "src/cpu/operators/CpuActivation.h"
 #include "src/cpu/operators/CpuGemm.h"
 #include "src/cpu/operators/CpuPermute.h"
@@ -59,13 +60,13 @@
      * |F16            |F16            |F16    |F16            |
      * |F32            |F32            |F32    |F32            |
      *
-     * @param[in]  src              Source tensor info. 3 lower dimensions represent a single input [width, height, IFM],
+     * @param[in]  src              Source tensor Info. 3 lower dimensions represent a single input [width, height, IFM],
      *                              while every optional dimension from 4 and above represent a batch of inputs.
      *                              Data types supported: F16/F32.
-     * @param[in]  weights          Weights tensor info. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input.
+     * @param[in]  weights          Weights tensor Info. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported: Same as @p input.
      *                              Currently only 3x3 and 5x5 kernels are supported.
-     * @param[in]  biases           Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights.
-     * @param[out] dst              Destination tensor info. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
+     * @param[in]  biases           Biases tensor Info. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported: Same as @p weights.
+     * @param[out] dst              Destination tensor Info. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
      *                              Data types supported: Same as @p input.
      * @param[in]  conv_info        Contains padding and stride information described in @ref PadStrideInfo. Currently only unit strides are supported.
      * @param[in]  act_info         (Optional) Activation layer information in case of a fused activation.
@@ -107,28 +108,27 @@
         PermutedOutput     = TransformedInput,
         Count              = 10
     };
-
-    std::unique_ptr<CpuGemm>       _gemm_function;
-    std::unique_ptr<CpuActivation> _activation_func;
-    std::unique_ptr<CpuPermute>    _permute_input;
-    std::unique_ptr<CpuPermute>    _permute_output;
-    std::unique_ptr<CpuPermute>    _permute_weights;
-    std::unique_ptr<ICPPKernel>    _transform_input_kernel;
-    std::unique_ptr<ICPPKernel>    _transform_weights_kernel;
-    std::unique_ptr<ICPPKernel>    _transform_output_kernel;
-
-    DataLayout                       _data_layout;
-    experimental::MemoryRequirements _aux_mem{ Count };
-    TensorInfo                       _input_nhwc;
-    TensorInfo                       _output_nhwc;
-    TensorInfo                       _input_workspace;
-    TensorInfo                       _kernel_storage;
-    TensorInfo                       _output_workspace;
-    TensorInfo                       _input_transformed;
-    TensorInfo                       _output_transformed;
-    TensorInfo                       _weights_hwio;
-    bool                             _run_activation;
-    bool                             _is_prepared;
+    std::unique_ptr<CpuGemm>                   _gemm_function;
+    std::unique_ptr<CpuActivation>             _activation_func;
+    std::unique_ptr<ICPPKernel>                _transform_input_kernel;
+    std::unique_ptr<ICPPKernel>                _transform_output_kernel;
+    std::unique_ptr<CpuPermute>                _permute_input;
+    std::unique_ptr<CpuPermute>                _permute_output;
+    std::unique_ptr<CpuPermute>                _permute_weights;
+    experimental::MemoryRequirements           _aux_mem{ Count };
+    std::unique_ptr<arm_conv::ConvolutionArgs> _conv_args; // Make it unique ptr because this type does not have a default constructor
+    arm_conv::winograd::WinogradImpl           _winograd_impl;
+    DataLayout                                 _data_layout;
+    TensorInfo                                 _winograd_transformed_input;
+    TensorInfo                                 _winograd_transformed_output;
+    TensorInfo                                 _winograd_transformed_weights;
+    TensorInfo                                 _input_workspace;
+    TensorInfo                                 _output_workspace;
+    TensorInfo                                 _weights_hwio;
+    TensorInfo                                 _input_nhwc;
+    TensorInfo                                 _output_nhwc;
+    bool                                       _is_prepared;
+    bool                                       _run_activation;
 };
 } // namespace cpu
 } // namespace arm_compute