COMPMID-3520: Move ndrange.hpp header from arm_gemm to assembly

Change-Id: I6352a520ce38230cdfbad346b176cb659ab242a7
Signed-off-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/3327
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>

1 files changed, 597 insertions, 0 deletions

diff --git a/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h b/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h
new file mode 100644
index 0000000000..bd141ef50b
--- /dev/null
+++ b/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h
@@ -0,0 +1,597 @@
+/*
+ * Copyright (c) 2017-2020 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#ifndef ARM_COMPUTE_NEGEMMWINOGRADCONVOLUTIONLAYERKERNEL_H
+#define ARM_COMPUTE_NEGEMMWINOGRADCONVOLUTIONLAYERKERNEL_H
+
+#include "arm_compute/core/NEON/INEKernel.h"
+#include "arm_compute/core/NEON/kernels/convolution/common/convolution.hpp"
+#include "arm_compute/core/NEON/kernels/convolution/common/tensor.hpp"
+
+#include "src/core/NEON/kernels/convolution/winograd/winograd_layer.hpp"
+
+namespace arm_compute
+{
+// Forward declarations
+class ITensor;
+
+/** Interface for the NEON kernel to perform Winograd input transform. */
+class INEWinogradLayerTransformInputKernel : public INEKernel
+{
+public:
+    /** Get the working space required to perform the transformation.
+     *
+     * Note, the working space is only required when performing the
+     * transformation - hence it can be reused whenever the transformation is
+     * not running.
+     *
+     * @param num_threads The greatest number of threads that will be used to execute the transform.
+     * @return Size of working space required in bytes.
+     */
+    virtual unsigned int get_working_space_size(unsigned int num_threads) const = 0;
+
+    /** Determine how much memory (in units of TIn) to allocate for the
+     * transformed input.
+     *
+     * @param[in] num_batches  Number of batches in the input tensor.
+     * @param[in] num_channels Number of feature maps in the input tensor.
+     * @param[in] num_rows     Number of rows in each feature map.
+     * @param[in] num_cols     Number of columns in each feature map.
+     * @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
+     *
+     * @return Storage size (in units of TIn) required.
+     */
+    virtual unsigned int get_input_storage_size(int num_batches, int num_channels, int num_rows, int num_cols, bool same_padding) const = 0;
+
+    /** Gets the stride between matrices in the input worspace
+     *
+     * @param[in] num_batches  Number of batches in the input tensor.
+     * @param[in] num_channels Number of feature maps in the input tensor.
+     * @param[in] num_rows     Number of rows in each feature map.
+     * @param[in] num_cols     Number of columns in each feature map.
+     * @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
+     *
+     * @return Stride expressed in bytes.
+     */
+    virtual int get_matrix_stride(int num_batches, int num_channels, int num_rows, int num_cols, bool same_padding) const = 0;
+
+    /** Configure the output transform kernel.
+     *
+     * @param[in]  input_nhwc    Input tensor in NHWC data layout format.
+     * @param[in]  num_batches   Number of batches in input tensor.
+     * @param[in]  num_rows      Number of rows in input tensor.
+     * @param[in]  num_cols      Number of columns in input tensor.
+     * @param[in]  num_channels  Number of channels in input tensor.
+     * @param[in]  padding       Padding type.
+     * @param[out] output        Base of output matrices.
+     * @param[in]  matrix_stride Stride between output matrices.
+     * @param[in]  workspace     Tensor to be used as the working space during the computation.
+     */
+    virtual void configure(const ITensor *input_nhwc, const int num_batches, const int num_rows, const int num_cols, const int num_channels,
+                           const PaddingType padding, ITensor *output, const int matrix_stride, ITensor *workspace) = 0;
+
+    /** Destructor */
+    virtual ~INEWinogradLayerTransformInputKernel()
+    {
+    }
+};
+
+/** NEON kernel to perform Winograd input transform. */
+template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
+class NEWinogradLayerTransformInputKernel : public INEWinogradLayerTransformInputKernel
+{
+public:
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    NEWinogradLayerTransformInputKernel(const NEWinogradLayerTransformInputKernel &) = delete;
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    NEWinogradLayerTransformInputKernel &operator=(const NEWinogradLayerTransformInputKernel &) = delete;
+    /** Allow instances of this class to be moved */
+    NEWinogradLayerTransformInputKernel(NEWinogradLayerTransformInputKernel &&) = default;
+    /** Allow instances of this class to be moved */
+    NEWinogradLayerTransformInputKernel &operator=(NEWinogradLayerTransformInputKernel &&) = default;
+    /** Default destructor */
+    ~NEWinogradLayerTransformInputKernel() = default;
+
+    /** Determine how much memory (in units of TIn) to allocate for the
+     * transformed input.
+     *
+     * @param[in] num_batches  Number of batches in the input tensor.
+     * @param[in] num_channels Number of feature maps in the input tensor.
+     * @param[in] num_rows     Number of rows in each feature map.
+     * @param[in] num_cols     Number of columns in each feature map.
+     * @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
+     *
+     * @return Storage size (in units of TIn) required.
+     */
+    unsigned int get_input_storage_size(
+        int  num_batches,
+        int  num_channels,
+        int  num_rows,
+        int  num_cols,
+        bool same_padding) const override;
+
+    /** Get the working space required to perform the transformation.
+     *
+     * Note, the working space is only required when performing the
+     * transformation - hence it can be reused whenever the transformation is
+     * not running.
+     *
+     * @param[in] num_threads The greatest number of threads that will be used to execute the transform.
+     *
+     * @return Size of working space required in bytes.
+     */
+    unsigned int get_working_space_size(unsigned int num_threads) const override;
+
+    /** Gets the stride between matrices in the input worspace
+     *
+     * @param[in] num_batches  Number of batches in the input tensor.
+     * @param[in] num_channels Number of feature maps in the input tensor.
+     * @param[in] num_rows     Number of rows in each feature map.
+     * @param[in] num_cols     Number of columns in each feature map.
+     * @param[in] same_padding Use "SAME" padding, otherwise use "VALID".
+     *
+     * @return Stride expressed in bytes.
+     */
+    int get_matrix_stride(
+        int  num_batches,
+        int  num_channels,
+        int  num_rows,
+        int  num_cols,
+        bool same_padding) const override;
+
+    /** Default constructor */
+    NEWinogradLayerTransformInputKernel();
+
+    const char *name() const override
+    {
+        return "NEWinogradLayerTransformInputKernel";
+    }
+
+    /** Configure the output transform kernel.
+     *
+     * @param[in]  input_nhwc    Input tensor.  Data types supported: F16/F32. Layout supported NHWC.
+     * @param[in]  num_batches   Number of batches in input tensor.
+     * @param[in]  num_rows      Number of rows in input tensor.
+     * @param[in]  num_cols      Number of columns in input tensor.
+     * @param[in]  num_channels  Number of channels in input tensor.
+     * @param[in]  padding       Padding type.
+     * @param[out] output        Base of output matrices.
+     * @param[in]  matrix_stride Stride between output matrices.
+     * @param[in]  workspace     Tensor to be used as the working space during the computation.
+     */
+    void configure(
+        const ITensor    *input_nhwc,
+        const int         num_batches,
+        const int         num_rows,
+        const int         num_cols,
+        const int         num_channels,
+        const PaddingType padding,
+        ITensor          *output,
+        const int         matrix_stride,
+        ITensor          *workspace) override;
+
+    // Inherited methods overridden:
+    void run(const Window &window, const ThreadInfo &info) override;
+
+    /** Winograd base kernel */
+    using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols, winograd::WinogradRoots::Integers>;
+    /** Winograd convolution kernel */
+    using WinogradConv = typename WinogradBase::template Convolution<T, T>;
+
+    /** Static function to check if given info will lead to a valid configuration of @ref NEWinogradLayerTransformInputKernel
+     *
+     * @param[in] input         First tensor input info. Data types supported: F16/F32.
+     * @param[in] output        Output tensor info. Data types supported: same as @p input.
+     * @param[in] winograd_info Contains Winograd's information described in @ref WinogradInfo
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info);
+
+private:
+    using InputTransform = typename WinogradBase::template InputTransform<T, T>;
+
+    std::unique_ptr<InputTransform> _transform{ nullptr };
+    const ITensor                  *_input_nhwc;
+    int                             _num_batches;    /**< Number of batches in input tensor. */
+    int                             _num_rows;       /**< Number of rows in input tensor. */
+    int                             _num_cols;       /**< Number of columns in input tensor. */
+    int                             _num_channels;   /**< Number of channels in input tensor. */
+    PaddingType                     _padding;        /**< Padding type. */
+    ITensor                        *_output;         /**< Base of output matrices. */
+    int                             _matrix_stride;  /**< Stride between output matrices. */
+    int                             _padding_top;    /**< Padding to apply to the top of the image. */
+    int                             _padding_left;   /**< Padding to apply to the left of the image. */
+    int                             _padding_right;  /**< Padding to apply to the right of the image. */
+    int                             _padding_bottom; /**< Padding to apply to the bottom of the image. */
+    ITensor                        *_workspace;
+};
+
+/** Interface for the NEON kernel to perform Winograd output transform. */
+class INEWinogradLayerTransformOutputKernel : public INEKernel
+{
+public:
+    /** Get the working space required to perform the transformation.
+     *
+     * Note, the working space is only required when performing the
+     * transformation - hence it can be reused whenever the transformation is
+     * not running.
+     *
+     * @param[in] num_threads The greatest number of threads that will be used to execute the transform.
+     *
+     * @return Size of working space required in bytes.
+     */
+    virtual unsigned int get_working_space_size(unsigned int num_threads) const = 0;
+
+    /** Determine how much memory (in units of TOut) to allocate for the
+     * (Winograd domain) output.
+     *
+     * @param[in] num_batches         Number of batches in the output tensor.
+     * @param[in] num_rows            Number of rows in each feature map of the input tensor.
+     * @param[in] num_cols            Number of columns in each feature map of the input tensor.
+     * @param[in] num_output_channels Number of feature maps in the output tensor.
+     *
+     * @return Storage size (in units of TOut) required.
+     */
+    virtual unsigned int get_output_storage_size(int num_batches, int num_rows, int num_cols, int num_output_channels) const = 0;
+
+    /** Gets the stride between matrices in the output worspace
+     *
+     * @param[in] num_batches         Number of batches in the output tensor.
+     * @param[in] num_rows            Number of rows in each feature map of the input tensor.
+     * @param[in] num_cols            Number of columns in each feature map of the input tensor.
+     * @param[in] num_output_channels Number of feature maps in the output tensor.
+     *
+     * @return Stride expressed in bytes.
+     */
+    virtual int get_matrix_stride(int num_batches, int num_rows, int num_cols, int num_output_channels) const = 0;
+
+    /** Get the output shape of a convolution.
+     *
+     * @param[in] num_rows     Number of rows in each feature map of the input tensor.
+     * @param[in] num_cols     Number of columns in each feature map of the input tensor.
+     * @param[in] padding_same True if padding is SAME, false otherwise
+     *
+     * @return Shape of the output tensor
+     */
+    virtual std::pair<unsigned int, unsigned int> get_output_shape(
+        int  num_rows,    /* Number of rows in each feature map of the input tensor. */
+        int  num_cols,    /* Number of columns in each feature map of the input tensor. */
+        bool padding_same /* True if padding is SAME, false otherwise */
+    ) const = 0;
+
+    /** Configure the output transform kernel.
+     *
+     * @param[in]  biases             Pointer to the biases tensor.
+     * @param[in]  transformed_output Pointer to working space for the output tensor in the Winograd domain.
+     * @param[in]  matrix_stride      Output matrix stride, can be computed with winograd::WinogradGEMM<2, 2, 3, 3>::Convolution<float, float>::get_output_matrix_stride()
+     * @param[out] output_nhwc        Pointer to a tensor in NHWC data layout ordered output tensor, in the spatial domain.
+     * @param[in]  num_batches        Number of batches in the input tensor.
+     * @param[in]  num_rows           Number of rows in output tensor.
+     * @param[in]  num_cols           Number of columns in output tensor.
+     * @param[in]  num_channels       Number of feature maps in the output tensor.
+     * @param[in]  workspace          Tensor to be used as the working space during the computation.
+     * @param[in]  activation         Activation to be used
+     */
+    virtual void configure(
+        const ITensor              *biases,
+        const ITensor              *transformed_output,
+        const int                   matrix_stride,
+        ITensor                    *output_nhwc,
+        const int                   num_batches,
+        const int                   num_rows,
+        const int                   num_cols,
+        const int                   num_channels,
+        ITensor                    *workspace,
+        const arm_gemm::Activation &activation) = 0;
+
+    virtual ~INEWinogradLayerTransformOutputKernel()
+    {
+    }
+};
+
+/** NEON kernel to perform Winograd output transform. */
+template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
+class NEWinogradLayerTransformOutputKernel : public INEWinogradLayerTransformOutputKernel
+{
+public:
+    const char *name() const override
+    {
+        return "NEWinogradLayerTransformOutputKernel";
+    }
+    /** Constructor */
+    NEWinogradLayerTransformOutputKernel();
+
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    NEWinogradLayerTransformOutputKernel(const NEWinogradLayerTransformOutputKernel &) = delete;
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    NEWinogradLayerTransformOutputKernel &operator=(const NEWinogradLayerTransformOutputKernel &) = delete;
+    /** Allow instances of this class to be moved */
+    NEWinogradLayerTransformOutputKernel(NEWinogradLayerTransformOutputKernel &&) = default;
+    /** Allow instances of this class to be moved */
+    NEWinogradLayerTransformOutputKernel &operator=(NEWinogradLayerTransformOutputKernel &&) = default;
+    /** Default destructor */
+    ~NEWinogradLayerTransformOutputKernel() = default;
+
+    // Inherited methods overridden:
+    /** Determine how much memory (in units of TOut) to allocate for the
+     * (Winograd domain) output.
+     *
+     * @param[in] num_batches         Number of batches in the output tensor.
+     * @param[in] num_rows            Number of rows in each feature map of the input tensor.
+     * @param[in] num_cols            Number of columns in each feature map of the input tensor.
+     * @param[in] num_output_channels Number of feature maps in the output tensor.
+     *
+     * @return Storage size (in units of TOut) required.
+     */
+    unsigned int get_output_storage_size(int num_batches, int num_rows, int num_cols, int num_output_channels) const override;
+
+    /** Gets the stride between matrices in the output worspace
+     *
+     * @param[in] num_batches         Number of batches in the output tensor.
+     * @param[in] num_rows            Number of rows in each feature map of the input tensor.
+     * @param[in] num_cols            Number of columns in each feature map of the input tensor.
+     * @param[in] num_output_channels Number of feature maps in the output tensor.
+     *
+     * @return Stride expressed in bytes.
+     */
+    int get_matrix_stride(int num_batches, int num_rows, int num_cols, int num_output_channels) const override;
+    /** Get the output shape of a convolution.
+     *
+     * @param[in] num_rows     Number of rows in each feature map of the input tensor.
+     * @param[in] num_cols     Number of columns in each feature map of the input tensor.
+     * @param[in] padding_same True if padding is SAME, false otherwise
+     *
+     * @return Shape of the output tensor
+     */
+    std::pair<unsigned int, unsigned int> get_output_shape(
+        int  num_rows, /* Number of rows in each feature map of the input tensor. */
+        int  num_cols, /* Number of columns in each feature map of the input tensor. */
+        bool padding_same) const override;
+
+    /** Get the working space required to perform the transformation.
+     *
+     * Note, the working space is only required when performing the
+     * transformation - hence it can be reused whenever the transformation is
+     * not running.
+     *
+     * @param[in] num_threads The greatest number of threads that will be used to execute the transform.
+     *
+     * @return Size of working space required in bytes.
+     */
+    unsigned int get_working_space_size(unsigned int num_threads) const override;
+
+    /** Configure the output transform kernel.
+     *
+     * @param[in]  biases             Pointer to the biases tensor.
+     * @param[in]  transformed_output Pointer to working space for the output tensor in the Winograd domain.
+     * @param[in]  matrix_stride      Output matrix stride, can be computed with winograd::WinogradGEMM<2, 2, 3, 3>::Convolution<float, float>::get_output_matrix_stride()
+     * @param[out] output_nhwc        Pointer to a tensor with NHWC data layout, in the spatial domain.
+     * @param[in]  num_batches        Number of batches in the input tensor.
+     * @param[in]  num_rows           Number of rows in output tensor.
+     * @param[in]  num_cols           Number of columns in output tensor.
+     * @param[in]  num_channels       Number of feature maps in the output tensor.
+     * @param[in]  workspace          Tensor to be used as the working space during the computation.
+     * @param[in]  activation         Activation to be used
+     */
+    void configure(
+        const ITensor              *biases,
+        const ITensor              *transformed_output,
+        const int                   matrix_stride,
+        ITensor                    *output_nhwc,
+        const int                   num_batches,
+        const int                   num_rows,
+        const int                   num_cols,
+        const int                   num_channels,
+        ITensor                    *workspace,
+        const arm_gemm::Activation &activation) override;
+
+    void run(const Window &window, const ThreadInfo &info) override;
+
+    /** Static function to check if given info will lead to a valid configuration of @ref NEWinogradLayerTransformOutputKernel
+     *
+     * @param[in] input         Source tensor info with shape [C, N, 16, batches] or [C, N, 36, batches]. Data types supported: F16/F32.
+     * @param[in] bias          Biases tensor info. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. It can be a nullptr. Data type supported: as @p input
+     * @param[in] output        Destination tensor info with shape [output_convolved_dims.width, output_convolved_dims.height, C, batches]. Data type supported: same as @p input
+     * @param[in] winograd_info Contains Winograd's information described in @ref WinogradInfo
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info);
+
+private:
+    using WinogradBase    = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols, winograd::WinogradRoots::Integers>;
+    using WinogradConv    = typename WinogradBase::template Convolution<T, T>;
+    using OutputTransform = typename WinogradBase::template OutputTransform<T, T>;
+
+    std::unique_ptr<OutputTransform> _transform{ nullptr };
+    const ITensor                   *_biases;
+    const ITensor                   *_transformed_output;
+    ITensor                         *_workspace;
+    int                              _matrix_stride;
+    int                              _matrix_row_stride;
+    ITensor                         *_output_nhwc;
+    int                              _num_batches;
+    int                              _num_rows;
+    int                              _num_cols;
+    int                              _num_channels;
+};
+
+/** Interface for the NEON kernel to perform Winograd weights transform. */
+class INEWinogradLayerTransformWeightsKernel : public INEKernel
+{
+public:
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    INEWinogradLayerTransformWeightsKernel(const INEWinogradLayerTransformWeightsKernel &) = default;
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    INEWinogradLayerTransformWeightsKernel &operator=(const INEWinogradLayerTransformWeightsKernel &) = default;
+    /** Allow instances of this class to be moved */
+    INEWinogradLayerTransformWeightsKernel(INEWinogradLayerTransformWeightsKernel &&) = default;
+    /** Allow instances of this class to be moved */
+    INEWinogradLayerTransformWeightsKernel &operator=(INEWinogradLayerTransformWeightsKernel &&) = default;
+
+    INEWinogradLayerTransformWeightsKernel()
+    {
+    }
+    virtual ~INEWinogradLayerTransformWeightsKernel()
+    {
+    }
+    /** Determine how much memory (in units of T) to allocate for the
+     * transformed weights.
+     *
+     * @param[in] num_output_channels Number of output feature maps.
+     * @param[in] num_input_channels  Number of input feature maps.
+     *
+     * @return Storage size (in units of T) required.
+     */
+    virtual unsigned int get_weight_storage_size(int num_output_channels, int num_input_channels) const = 0;
+    /** Gets the stride between matrices in the kernel worspace
+     *
+     * @param[in] num_output_channels Number of output feature maps.
+     * @param[in] num_input_channels  Number of input feature maps.
+     *
+     * @return Stride expressed in bytes.
+     */
+    virtual int get_matrix_stride(int num_output_channels, int num_input_channels) const = 0;
+
+    /** Configure the weights transform kernel.
+     *
+     * @param[in]  weights_hwio        Pointer to the weights tensor
+     * @param[out] output              Pointer to working space for the output tensor in the Winograd domain.
+     * @param[in]  matrix_stride       Stride across matrices in the output workspace.
+     * @param[in]  num_output_channels Number of filters.
+     * @param[in]  num_input_channels  Number of channels in each filter.
+     */
+
+    virtual void configure(const ITensor *weights_hwio, ITensor *output, const int matrix_stride, const int num_output_channels, const int num_input_channels) = 0;
+
+    /** Static function to check if given info will lead to a valid configuration of @ref NEWinogradLayerTransformWeightsKernel
+     *
+     * @param[in] input   First tensor input info. Data types supported: F16/F32.
+     * @param[in] weights Weights tensor info. Data types supported: same as @p input.
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input, const ITensorInfo *weights);
+};
+
+/** NEON kernel to perform Winograd weights transform. */
+template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
+class NEWinogradLayerTransformWeightsKernel final : public INEWinogradLayerTransformWeightsKernel
+{
+public:
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    NEWinogradLayerTransformWeightsKernel(const NEWinogradLayerTransformWeightsKernel &) = delete;
+    /** Prevent instances of this class from being copied (As this class contains pointers) */
+    NEWinogradLayerTransformWeightsKernel &operator=(const NEWinogradLayerTransformWeightsKernel &) = delete;
+    /** Allow instances of this class to be moved */
+    NEWinogradLayerTransformWeightsKernel(NEWinogradLayerTransformWeightsKernel &&) = default;
+    /** Allow instances of this class to be moved */
+    NEWinogradLayerTransformWeightsKernel &operator=(NEWinogradLayerTransformWeightsKernel &&) = default;
+    /** Default destructor */
+    ~NEWinogradLayerTransformWeightsKernel() = default;
+
+    /** Default constructor. */
+    NEWinogradLayerTransformWeightsKernel();
+    const char *name() const override
+    {
+        return "NEWinogradLayerTransformWeightsKernel";
+    }
+
+    /** Static function to check if given info will lead to a valid configuration of @ref NEWinogradLayerTransformWeightsKernel
+     *
+     * @param[in] input         Source tensor info. The input is a 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] (NCHW data layout).
+     *                          kernel_x must be 3 and equal to kernel_y. Data types supported: F16/F32.
+     * @param[in] output        Destination tensor info. The output is a 3D tensor with dimensions [OFM, IFM, 16] or [OFM, IFM, 36]. Data type supported: same as @p input
+     * @param[in] winograd_info Contains Winograd's information described in @ref WinogradInfo
+     *
+     * @return a status
+     */
+    static Status validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info);
+
+    // Inherited methods overridden:
+
+#ifndef DOXYGEN_SKIP_THIS
+    /** Configure the weights transform kernel.
+     *
+     * @param[in]  weights_hwio        Pointer to the weights tensor
+     * @param[out] output              Pointer to working space for the output tensor in the Winograd domain.
+     * @param[in]  matrix_stride       Stride across matrices in the output workspace.
+     * @param[in]  num_output_channels Number of filters.
+     * @param[in]  num_input_channels  Number of channels in each filter.
+     */
+    void configure(const ITensor *weights_hwio, ITensor *output, const int matrix_stride, const int num_output_channels, const int num_input_channels) override;
+#endif /* DOXYGEN_SKIP_THIS */
+
+    /** Determine how much memory (in units of T) to allocate for the
+     * transformed weights.
+     *
+     * @param[in] num_output_channels Number of output feature maps.
+     * @param[in] num_input_channels  Number of input feature maps.
+     *
+     * @return Storage size (in units of T) required.
+     */
+    unsigned int get_weight_storage_size(int num_output_channels, int num_input_channels) const override;
+
+    /** Gets the stride between matrices in the input worspace
+     *
+     * @param[in] num_output_channels Number of output feature maps.
+     * @param[in] num_input_channels  Number of input feature maps.
+     *
+     * @return Stride expressed in bytes.
+     */
+    int get_matrix_stride(int num_output_channels, int num_input_channels) const override;
+    void run(const Window &window, const ThreadInfo &info) override;
+    bool is_parallelisable() const override;
+
+private:
+    using WinogradBase     = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols, winograd::WinogradRoots::Integers>;
+    using WinogradConv     = typename WinogradBase::template Convolution<T, T>;
+    using WeightsTransform = typename WinogradBase::template WeightsTransform<T, T>;
+
+    std::unique_ptr<WeightsTransform> _transform{ nullptr };
+    const ITensor                    *_weights_hwio;
+    ITensor                          *_output;
+    int                               _matrix_stride;
+    int                               _num_output_channels;
+    int                               _num_input_channels;
+};
+
+/** NEON kernel to perform Winograd. */
+template <typename TIn, typename TOut, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
+class NEWinogradLayerConfiguration
+{
+public:
+    /** Winograd base kernel */
+    using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols, winograd::WinogradRoots::Integers>;
+    /** Winograd convolution kernel */
+
+    using WinogradConv = typename WinogradBase::template Convolution<TIn, TOut>;
+
+    using TransformInputKernel   = NEWinogradLayerTransformInputKernel<TIn, OutputTileRows, OutputTileCols, KernelRows, KernelCols>;
+    using TransformWeightsKernel = NEWinogradLayerTransformWeightsKernel<TIn, OutputTileRows, OutputTileCols, KernelRows, KernelCols>;
+    using TransformOutputKernel  = NEWinogradLayerTransformOutputKernel<TOut, OutputTileRows, OutputTileCols, KernelRows, KernelCols>;
+};
+
+} // namespace arm_compute
+#endif /*ARM_COMPUTE_NEGEMMWINOGRADCONVOLUTIONLAYERKERNEL_H*/