COMPMID-1247:Integrate kernel size 1x3 & 3x1 support in NEWinogradLayer.

Change-Id: I6fe198881230e49864c841a3b2366ccf2a9247f9
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/145210
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>

15 files changed, 920 insertions, 132 deletions

diff --git a/arm_compute/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h b/arm_compute/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h
index 9cdd69a70a..c71c105d92 100644
--- a/arm_compute/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h
+++ b/arm_compute/core/NEON/kernels/NEWinogradConvolutionLayerKernel.h
@@ -159,7 +159,7 @@ public:
     void run(const Window &window, const ThreadInfo &info) override;
 
     /** Winograd base kernel */
-    using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelCols, KernelCols>;
+    using WinogradBase = winograd::WinogradGEMM<OutputTileRows, OutputTileCols, KernelRows, KernelCols>;
     /** Winograd convolution kernel */
     using WinogradConv = typename WinogradBase::template Convolution<T, T>;
 
@@ -360,6 +360,21 @@ template <typename T>
 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.
      *
@@ -388,9 +403,14 @@ public:
 
     virtual void configure(const ITensor *weights_hwio, ITensor *output, const int matrix_stride, const int num_output_channels, const int num_input_channels) = 0;
 
-    virtual ~INEWinogradLayerTransformWeightsKernel()
-    {
-    }
+    /** 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: 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. */
diff --git a/arm_compute/core/NEON/kernels/convolution/common/utils.hpp b/arm_compute/core/NEON/kernels/convolution/common/utils.hpp
index 5f42719119..25bfa332fb 100644
--- a/arm_compute/core/NEON/kernels/convolution/common/utils.hpp
+++ b/arm_compute/core/NEON/kernels/convolution/common/utils.hpp
@@ -26,7 +26,7 @@
 
 void PrintMatrix(const float *const m, const int M, const int N, const int row_stride);
 
-inline int iceildiv(const int a, const int b)
+constexpr inline int iceildiv(const int a, const int b)
 {
     return (a + b - 1) / b;
 }
diff --git a/arm_compute/core/NEON/kernels/convolution/winograd/transforms/input.hpp b/arm_compute/core/NEON/kernels/convolution/winograd/transforms/input.hpp
index 13218030d2..369c2ff48f 100644
--- a/arm_compute/core/NEON/kernels/convolution/winograd/transforms/input.hpp
+++ b/arm_compute/core/NEON/kernels/convolution/winograd/transforms/input.hpp
@@ -50,6 +50,22 @@ namespace winograd
     const int matrix_row_stride  /** Stride within matrices. */
   )
   {
+    // If an Nx1 kernel then transpose and redirect to the 1xN implementation
+    if (kernel_cols == 1)
+    {
+      WinogradGEMM<output_tile_cols, output_tile_rows, kernel_cols, kernel_rows>::
+        template InputTransform<T>::execute(
+          input,
+          n_batches, in_batch_stride,
+          n_cols, in_col_stride,
+          n_rows, in_row_stride,
+          n_channels, padding,
+          tile_N, tile_M,
+          output, matrix_stride, matrix_batch_stride, matrix_row_stride
+        );
+      return;
+    }
+
     // Compute the padding required on each edge of the image
     const int pad_top = (padding == PADDING_SAME) ? (kernel_rows - 1) / 2 : 0;
     const int pad_left = (padding == PADDING_SAME) ? (kernel_cols - 1) / 2 : 0;
@@ -111,6 +127,12 @@ namespace winograd
     const int n_cols
   )
   {
+    if (kernel_cols == 1)
+    {
+      // If an Nx1 implementation then this should never be reached.
+      return;
+    }
+
     constexpr int tile_overlap = kernel_cols - 1;
 
     // Loop over columns of tiles
diff --git a/arm_compute/core/NEON/kernels/convolution/winograd/transforms/output.hpp b/arm_compute/core/NEON/kernels/convolution/winograd/transforms/output.hpp
index 700ca76c68..6ed146bf85 100644
--- a/arm_compute/core/NEON/kernels/convolution/winograd/transforms/output.hpp
+++ b/arm_compute/core/NEON/kernels/convolution/winograd/transforms/output.hpp
@@ -45,6 +45,22 @@ namespace winograd
     T* const output
   )
   {
+    // If an Nx1 kernel then transpose and redirect to the 1xN implementation.
+    if (kernel_cols == 1)
+    {
+      WinogradGEMM<output_tile_cols, output_tile_rows, kernel_cols, kernel_rows>::
+        template OutputTransform<T>::execute(
+          n_batches,
+          output_batch_stride,
+          n_cols, output_col_stride,
+          n_rows, output_row_stride,
+          n_channels,
+          matrix_base, matrix_stride, matrix_row_stride,
+          biases, output
+        );
+      return;
+    }
+
     // Compute the number of tiles and hence the padding required on the bottom
     // and right of the image.
     const int tile_M = iceildiv(n_rows, output_tile_rows);
@@ -98,6 +114,12 @@ namespace winograd
     const int row_pad_right
   )
   {
+    if (kernel_cols == 1)
+    {
+      // If an Nx1 implementation then this should never be reached.
+      return;
+    }
+
     // Loop over columns of tiles
     for (int tile_j = 0; tile_j < tile_N; tile_j++)
     {
diff --git a/arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp b/arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp
index bc067fd07a..7098fc48a1 100644
--- a/arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp
+++ b/arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp
@@ -49,8 +49,8 @@ class WinogradGEMM
     static constexpr int output_tile_cols = OutputTileCols;
     static constexpr int kernel_rows = KernelRows;
     static constexpr int kernel_cols = KernelCols;
-    static constexpr int inner_tile_rows = output_tile_rows + kernel_rows - 1;  // TODO Check
-    static constexpr int inner_tile_cols = output_tile_cols + kernel_cols - 1;  // TODO Check
+    static constexpr int inner_tile_rows = output_tile_rows + kernel_rows - 1;
+    static constexpr int inner_tile_cols = output_tile_cols + kernel_cols - 1;
     static constexpr int N_GEMMS = inner_tile_rows * inner_tile_cols;
 
     /** Transform weights from the spatial to the Winograd domain. */
@@ -196,8 +196,21 @@ class WinogradGEMM
           const int n_cols
         );
 
-        static constexpr int max_pad_bottom = inner_tile_rows - 1;
-        static constexpr int max_pad_right = inner_tile_cols - 1;
+        // Tile overlaps
+        static constexpr int overlap_rows = kernel_rows - 1;
+        static constexpr int overlap_cols = kernel_cols - 1;
+
+        // Maximum padding and number of distinct paddings
+        static constexpr int max_pad_top = kernel_rows / 2;
+        static constexpr int n_pad_top = 1 + iceildiv(max_pad_top, inner_tile_rows - overlap_rows);
+
+        static constexpr int max_pad_left = kernel_cols / 2;
+        static constexpr int n_pad_left = 1 + iceildiv(max_pad_left, inner_tile_cols - overlap_cols);
+
+        static constexpr int n_pad_bottom = inner_tile_rows;
+        static constexpr int n_pad_right = inner_tile_cols;
+
+
 
         /** Process a single tile of the input tensor. */
         template <int pad_top, int pad_left, int pad_bottom, int pad_right>
@@ -205,7 +218,8 @@ class WinogradGEMM
 
         // Array of methods to transform tiles of the input tensor.
         typedef void (*TileFn)(int, const T*, int, int, T*, int);
-        static const TileFn tile_fns[2][2][max_pad_bottom][max_pad_right];
+        static const TileFn
+           tile_fns[n_pad_top][n_pad_left][n_pad_bottom][n_pad_right];
 
         /* Member values for instance-based API. */
         const T* const _inptr;
diff --git a/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp b/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp
index 3d7a16dd45..8f990712e8 100644
--- a/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp
@@ -40,19 +40,27 @@ namespace arm_compute
 
 namespace
 {
+inline bool is_kernel_size_supported(Size2D size)
+{
+    const std::array<Size2D, 4> supported_input_sizes = { { Size2D(1, 3), Size2D(3, 1), Size2D(5, 5), Size2D(3, 3) } };
+    return std::end(supported_input_sizes) != std::find(std::begin(supported_input_sizes), std::end(supported_input_sizes), size);
+}
+
 Status validate_arguments_winograd_weight_trans(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info)
 {
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(input);
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
 
-    const size_t idx_width  = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
-    const size_t idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
-    ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(idx_width) != 3 && input->dimension(idx_width) != 5);
-    ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(idx_width) != input->dimension(idx_height));
+    const size_t idx_width    = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
+    const size_t idx_height   = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
+    const auto   input_width  = input->dimension(idx_width);
+    const auto   input_height = input->dimension(idx_height);
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(Size2D(input_width, input_height)), "Only 1x3, 3x1, 3x3 and 5x5 kernels are supported");
     ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 4);
     const Size2D &output_tile = winograd_info.output_tile_size;
-    ARM_COMPUTE_RETURN_ERROR_ON(output_tile != Size2D(2U, 2U) && output_tile != Size2D(4U, 4U));
+    const std::array<Size2D, 4> supported_tile_sizes = { { Size2D(2U, 2U), Size2D(4U, 4U), Size2D(1U, 6U), Size2D(6U, 1U) } };
+    ARM_COMPUTE_RETURN_ERROR_ON(std::end(supported_tile_sizes) == std::find(std::begin(supported_tile_sizes), std::end(supported_tile_sizes), output_tile));
 
     // Checks performed when output is configured
     if(output->total_size() != 0)
@@ -98,8 +106,8 @@ Status validate_arguments_winograd_input_trans(const ITensorInfo *input, const I
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd input transform only supports unit strides");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG((kernel_dims.width != 3U && kernel_dims.width != 5U), "Winograd input transform only supports 3x3 and 5x5 kernels");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG((kernel_dims.width != kernel_dims.height), "Winograd input transform only supports 3x3 and 5x5 kernels");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(Size2D(kernel_dims.width, kernel_dims.height)),
+                                    "Only 1x3, 3x1, 3x3 and 5x5 kernels are supported");
 
     // Validate configured output
     if(output->total_size() != 0)
@@ -151,9 +159,11 @@ Status validate_arguments_winograd_output_trans(const ITensorInfo *input, const
     ARM_COMPUTE_RETURN_ERROR_ON_NULLPTR(output);
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
     ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(1) != num_tiles.area());
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG((kernel_dims.width != 3U && kernel_dims.width != 5U), "Winograd output transform only supports 3x3 and 5x5 kernels");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG((kernel_dims.width != kernel_dims.height), "Winograd output transform only supports 3x3 and 5x5 kernels");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(((input->dimension(2) != size_t(16U)) && (input->dimension(2) != size_t(36U))), "Only 2x2 and 4x4 output tile is supported");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(Size2D(kernel_dims.width, kernel_dims.height)),
+                                    "Only 1x3, 3x1, 3x3 and 5x5 kernels are supported");
+
+    const std::array<unsigned int, 3> supported_gemm_sizes = { { 8U, 16U, 36U } };
+    ARM_COMPUTE_RETURN_ERROR_ON(std::end(supported_gemm_sizes) == std::find(std::begin(supported_gemm_sizes), std::end(supported_gemm_sizes), input->dimension(2)));
     ARM_COMPUTE_UNUSED(kernel_dims);
     if(bias != nullptr)
     {
@@ -201,7 +211,21 @@ std::pair<Status, Window> validate_and_configure_window_winograd_output_trans(IT
 }
 } // namespace
 
-// Weights transform
+template <typename T>
+Status INEWinogradLayerTransformWeightsKernel<T>::validate(const ITensorInfo *input, const ITensorInfo *weights)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+    const DataLayout   data_layout = input->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);
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(!is_kernel_size_supported(Size2D(weights->dimension(width_idx), weights->dimension(height_idx))),
+                                    "Only 1x3, 3x1, 3x3 and 5x5 kernels are supported");
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
+    return Status{};
+}
+
+template class INEWinogradLayerTransformWeightsKernel<float>;
 
 template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
 unsigned int NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols, KernelRows, KernelCols>::get_weight_storage_size(int num_output_channels, int num_input_channels) const
@@ -278,6 +302,8 @@ Status NEWinogradLayerTransformWeightsKernel<T, OutputTileRows, OutputTileCols,
 template class NEWinogradLayerTransformWeightsKernel<float, 2, 2, 3, 3>;
 template class NEWinogradLayerTransformWeightsKernel<float, 4, 4, 3, 3>;
 template class NEWinogradLayerTransformWeightsKernel<float, 2, 2, 5, 5>;
+template class NEWinogradLayerTransformWeightsKernel<float, 1, 6, 1, 3>;
+template class NEWinogradLayerTransformWeightsKernel<float, 6, 1, 3, 1>;
 
 // Input transform
 
@@ -343,14 +369,15 @@ void NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, Kern
     ARM_COMPUTE_UNUSED(info);
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
 
-    const int element_size_in_bytes = _input_nhwc->info()->element_size();
-    const int input_col_stride      = _input_nhwc->info()->strides_in_bytes().y() / element_size_in_bytes;
-    const int input_row_stride      = _input_nhwc->info()->strides_in_bytes().z() / element_size_in_bytes;
-    const int input_batch_stride    = _input_nhwc->info()->strides_in_bytes()[3] / element_size_in_bytes;
-
-    InputTransform input_transform(reinterpret_cast<const T *>(_input_nhwc->buffer() + _input_nhwc->info()->offset_first_element_in_bytes()),
+    const int      element_size_in_bytes = _input_nhwc->info()->element_size();
+    const int      input_col_stride      = _input_nhwc->info()->strides_in_bytes().y() / element_size_in_bytes;
+    const int      input_row_stride      = _input_nhwc->info()->strides_in_bytes().z() / element_size_in_bytes;
+    const int      input_batch_stride    = _input_nhwc->info()->strides_in_bytes()[3] / element_size_in_bytes;
+    const auto     input_nhwc_ptr        = reinterpret_cast<const T *>(_input_nhwc->buffer() + _input_nhwc->info()->offset_first_element_in_bytes());
+    auto           output_ptr            = reinterpret_cast<T *>(_output->buffer() + _output->info()->offset_first_element_in_bytes());
+    InputTransform input_transform(input_nhwc_ptr,
                                    _num_batches, _num_rows, _num_cols, _num_channels, _padding,
-                                   reinterpret_cast<T *>(_output->buffer() + _output->info()->offset_first_element_in_bytes()),
+                                   output_ptr,
                                    _matrix_stride, _num_channels, input_batch_stride, input_row_stride, input_col_stride);
 
     // The code below cannot be moved to configure because biases hasn't been allocated at that point
@@ -371,6 +398,8 @@ Status NEWinogradLayerTransformInputKernel<T, OutputTileRows, OutputTileCols, Ke
 template class NEWinogradLayerTransformInputKernel<float, 2, 2, 3, 3>;
 template class NEWinogradLayerTransformInputKernel<float, 4, 4, 3, 3>;
 template class NEWinogradLayerTransformInputKernel<float, 2, 2, 5, 5>;
+template class NEWinogradLayerTransformInputKernel<float, 1, 6, 1, 3>;
+template class NEWinogradLayerTransformInputKernel<float, 6, 1, 3, 1>;
 
 // Output transform
 
@@ -438,7 +467,6 @@ void NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, Ker
     Window win;
     auto   win_last = output_transform.get_window();
     win.set(Window::DimX, Window::Dimension(0, win_last, 1));
-
     _output_nhwc->info()->set_valid_region(ValidRegion(Coordinates(), _output_nhwc->info()->tensor_shape()));
 
     INEKernel::configure(win);
@@ -452,10 +480,14 @@ void NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, Ker
     ARM_COMPUTE_ERROR_ON_NULLPTR(_output_workspace);
     ARM_COMPUTE_ERROR_ON_NULLPTR(_output_nhwc);
 
+    const int out_batch_stride = 0;
+    const int out_row_stride   = _output_nhwc->info()->strides_in_bytes()[2] / sizeof(T);
+    const int out_col_stride   = _output_nhwc->info()->strides_in_bytes()[1] / sizeof(T);
+
     OutputTransform output_transform(reinterpret_cast<T *>(_output_workspace->buffer()), _matrix_stride, _matrix_row_stride,
                                      (_biases ? reinterpret_cast<T *>(_biases->buffer() + _biases->info()->offset_first_element_in_bytes()) : nullptr),
                                      reinterpret_cast<T *>(_output_nhwc->buffer() + _output_nhwc->info()->offset_first_element_in_bytes()),
-                                     _num_batches, _num_rows, _num_cols, _num_channels, 0, _output_nhwc->info()->strides_in_bytes()[2] / sizeof(T), _output_nhwc->info()->strides_in_bytes()[1] / sizeof(T));
+                                     _num_batches, _num_rows, _num_cols, _num_channels, out_batch_stride, out_row_stride, out_col_stride);
 
     // The code below cannot be moved to configure because biases hasn't been allocated at that point
     const size_t fst = window.x().start();
@@ -478,5 +510,7 @@ Status NEWinogradLayerTransformOutputKernel<T, OutputTileRows, OutputTileCols, K
 template class NEWinogradLayerTransformOutputKernel<float, 2, 2, 3, 3>;
 template class NEWinogradLayerTransformOutputKernel<float, 4, 4, 3, 3>;
 template class NEWinogradLayerTransformOutputKernel<float, 2, 2, 5, 5>;
+template class NEWinogradLayerTransformOutputKernel<float, 1, 6, 1, 3>;
+template class NEWinogradLayerTransformOutputKernel<float, 6, 1, 3, 1>;
 
 } // namespace arm_compute
diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/input_2x2_3x3_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/input_2x2_3x3_fp32.cpp
index 6d8afc0def..97b2695d69 100644
--- a/src/core/NEON/kernels/convolution/winograd/transforms/input_2x2_3x3_fp32.cpp
+++ b/src/core/NEON/kernels/convolution/winograd/transforms/input_2x2_3x3_fp32.cpp
@@ -329,7 +329,7 @@ void Transform::process_tile(
 
 template <>
 template <>
-const Transform::TileFn Transform::tile_fns[2][2][max_pad_bottom][max_pad_right] =
+const Transform::TileFn Transform::tile_fns[n_pad_top][n_pad_left][n_pad_bottom][n_pad_right] =
 {
   {
     {
diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/input_2x2_5x5_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/input_2x2_5x5_fp32.cpp
index fd30b6118e..30c9463bb8 100644
--- a/src/core/NEON/kernels/convolution/winograd/transforms/input_2x2_5x5_fp32.cpp
+++ b/src/core/NEON/kernels/convolution/winograd/transforms/input_2x2_5x5_fp32.cpp
@@ -298,7 +298,7 @@ void Transform::process_tile(
 
 template <>
 template <>
-const Transform::TileFn Transform::tile_fns[2][2][max_pad_bottom][max_pad_right] =
+const Transform::TileFn Transform::tile_fns[n_pad_top][n_pad_left][n_pad_bottom][n_pad_right] =
 {
   {
     {
diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/input_4x4_3x3_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/input_4x4_3x3_fp32.cpp
index 04d1573e4c..7f93187132 100644
--- a/src/core/NEON/kernels/convolution/winograd/transforms/input_4x4_3x3_fp32.cpp
+++ b/src/core/NEON/kernels/convolution/winograd/transforms/input_4x4_3x3_fp32.cpp
@@ -326,7 +326,7 @@ void Transform::process_tile(
  */
 template <>
 template <>
-const Transform::TileFn Transform::tile_fns[2][2][max_pad_bottom][max_pad_right] =
+const Transform::TileFn Transform::tile_fns[n_pad_top][n_pad_left][n_pad_bottom][n_pad_right] =
 {
   {
     {
diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/input_6_3_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/input_6_3_fp32.cpp
new file mode 100644
index 0000000000..67e46499cd
--- /dev/null
+++ b/src/core/NEON/kernels/convolution/winograd/transforms/input_6_3_fp32.cpp
@@ -0,0 +1,226 @@
+/*
+ * Copyright (c) 2017 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.
+ */
+
+#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/input.hpp"
+#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp"
+#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp"
+
+
+namespace winograd
+{
+
+using Transform = WinogradGEMM<1, 6, 1, 3>::InputTransform<float>;
+
+template <>
+template <>
+int Transform::ops_performed(const Tensor4DShape &input_shape)
+{
+  (void) input_shape;
+  return 0;  // TODO
+}
+
+template <>
+template <>
+template <int pad_top, int pad_left, int pad_bottom, int pad_right>
+void Transform::process_tile(
+  int n_channels,
+  const float* const input_base,
+  const int input_row_stride,
+  const int input_col_stride,
+  float* const matrix_base,
+  const int matrix_stride
+)
+{
+  (void) input_row_stride;  // No rows over which to stride
+  constexpr int inner_tile_j = 8;
+  constexpr int cells_j = inner_tile_j - pad_right;
+
+  float *outptr = matrix_base;
+
+  // Get pointers into the input tile
+  const float *x_ptrs[inner_tile_j];
+  for (int j = pad_left, xj = 0; j < cells_j; j++, xj++)
+  {
+    x_ptrs[j] = input_base + xj*input_col_stride;
+  }
+
+  // Vectors used/computed in this kernel.
+  float x[inner_tile_j];
+  float U[inner_tile_j];
+
+  for (int j = 0; j < inner_tile_j; j++)
+  {
+    x[j] = 0.0f;
+  }
+
+  // Perform the Winograd input transformation for each channel in the input
+  // tensor.
+  int channels_remaining = n_channels;
+#ifdef __arm_any__
+  for (; channels_remaining >= 4; channels_remaining -= 4)
+  {
+    float32x4_t x[inner_tile_j], U[inner_tile_j];
+    for (int j = 0; j < inner_tile_cols; j++)
+    {
+      x[j] = vdupq_n_f32(0.0f);
+    }
+
+    // Load x
+    for (int j = pad_left; j < cells_j; j++)
+    {
+      x[j] = vld1q_f32(x_ptrs[j]);
+      x_ptrs[j] += 4;
+    }
+
+    // Compute U = x . X
+    U[0] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[6], 1), x[2], 49), x[4], -14), x[0], -36);
+    U[1] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[6], 1), x[2], 36), x[3], 13), x[4], -13), x[1], -36), x[5], -1);
+    U[2] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[6], 1), x[5], 1), x[2], 36), x[1], 36), x[4], -13), x[3], -13);
+    U[3] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[6], 1), x[3], 20), x[2], 9), x[5], -2), x[4], -10), x[1], -18);
+    U[4] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[6], 1), x[1], 18), x[2], 9), x[5], 2), x[4], -10), x[3], -20);
+    U[5] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[6], 1), x[3], 15), x[2], 4), x[5], -3), x[4], -5), x[1], -12);
+    U[6] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[6], 1), x[1], 12), x[2], 4), x[5], 3), x[4], -5), x[3], -15);
+    U[7] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(x[7], 1), x[3], 49), x[5], -14), x[1], -36);
+
+    // Store the transformed vector
+    for (int j = 0; j < inner_tile_j; j++)
+    {
+      vst1q_f32(outptr + j*matrix_stride, U[j]);
+    }
+    outptr += 4;
+  }
+
+  for (; channels_remaining >= 2; channels_remaining -= 2)
+  {
+    float32x2_t x[inner_tile_j], U[inner_tile_j];
+    for (int j = 0; j < inner_tile_cols; j++)
+    {
+      x[j] = vdup_n_f32(0.0f);
+    }
+
+    // Load x
+    for (int j = pad_left; j < cells_j; j++)
+    {
+      x[j] = vld1_f32(x_ptrs[j]);
+      x_ptrs[j] += 2;
+    }
+
+    // Compute U = x . X
+    U[0] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[6], 1), x[2], 49), x[4], -14), x[0], -36);
+    U[1] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[6], 1), x[2], 36), x[3], 13), x[4], -13), x[1], -36), x[5], -1);
+    U[2] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[6], 1), x[5], 1), x[2], 36), x[1], 36), x[4], -13), x[3], -13);
+    U[3] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[6], 1), x[3], 20), x[2], 9), x[5], -2), x[4], -10), x[1], -18);
+    U[4] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[6], 1), x[1], 18), x[2], 9), x[5], 2), x[4], -10), x[3], -20);
+    U[5] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[6], 1), x[3], 15), x[2], 4), x[5], -3), x[4], -5), x[1], -12);
+    U[6] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[6], 1), x[1], 12), x[2], 4), x[5], 3), x[4], -5), x[3], -15);
+    U[7] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(x[7], 1), x[3], 49), x[5], -14), x[1], -36);
+
+    // Store the transformed vector
+    for (int j = 0; j < inner_tile_j; j++)
+    {
+      vst1_f32(outptr + j*matrix_stride, U[j]);
+    }
+    outptr += 2;
+  }
+#endif  // __arm_any__
+  for (; channels_remaining; channels_remaining--)
+  {
+    // Load x
+    for (int j = pad_left; j < cells_j; j++)
+    {
+      x[j] = *(x_ptrs[j]++);
+    }
+
+    // Compute U = x . X
+    U[0] = x[0]*-36 + x[4]*-14 + x[2]*49 + x[6]*1;
+    U[1] = x[5]*-1 + x[1]*-36 + x[4]*-13 + x[3]*13 + x[2]*36 + x[6]*1;
+    U[2] = x[3]*-13 + x[4]*-13 + x[1]*36 + x[2]*36 + x[5]*1 + x[6]*1;
+    U[3] = x[1]*-18 + x[4]*-10 + x[5]*-2 + x[2]*9 + x[3]*20 + x[6]*1;
+    U[4] = x[3]*-20 + x[4]*-10 + x[5]*2 + x[2]*9 + x[1]*18 + x[6]*1;
+    U[5] = x[1]*-12 + x[4]*-5 + x[5]*-3 + x[2]*4 + x[3]*15 + x[6]*1;
+    U[6] = x[3]*-15 + x[4]*-5 + x[5]*3 + x[2]*4 + x[1]*12 + x[6]*1;
+    U[7] = x[1]*-36 + x[5]*-14 + x[3]*49 + x[7]*1;
+
+    // Store the transformed vector
+    for (int j = 0; j < inner_tile_j; j++)
+    {
+      *(outptr + j*matrix_stride) = U[j];
+    }
+    outptr++;
+  }
+}
+
+template <>
+template <>
+const Transform::TileFn Transform::tile_fns[n_pad_top][n_pad_left][n_pad_bottom][n_pad_right] =
+{
+  {
+    {
+      {
+        Transform::template process_tile<0, 0, 0, 0>,
+        Transform::template process_tile<0, 0, 0, 1>,
+        Transform::template process_tile<0, 0, 0, 2>,
+        Transform::template process_tile<0, 0, 0, 3>,
+        Transform::template process_tile<0, 0, 0, 4>,
+        Transform::template process_tile<0, 0, 0, 5>,
+        Transform::template process_tile<0, 0, 0, 6>,
+      }
+    },
+    {
+      {
+        Transform::template process_tile<0, 1, 0, 0>,
+        Transform::template process_tile<0, 1, 0, 1>,
+        Transform::template process_tile<0, 1, 0, 2>,
+        Transform::template process_tile<0, 1, 0, 3>,
+        Transform::template process_tile<0, 1, 0, 4>,
+        Transform::template process_tile<0, 1, 0, 5>,
+        Transform::template process_tile<0, 1, 0, 6>,
+      }
+    }
+  }
+};
+
+template <int x, int y>
+using TransformTransposed = typename WinogradGEMM<x, 1, y, 1>::template InputTransform<float>;
+
+template <>
+template <>
+const TransformTransposed<6, 3>::TileFn
+  TransformTransposed<6, 3>::tile_fns[n_pad_top][n_pad_left][n_pad_bottom][n_pad_right] = {};
+
+template <>
+template <>
+const TransformTransposed<4, 5>::TileFn
+  TransformTransposed<4, 5>::tile_fns[n_pad_top][n_pad_left][n_pad_bottom][n_pad_right] = {};
+
+template <>
+template <>
+const TransformTransposed<2, 7>::TileFn
+  TransformTransposed<2, 7>::tile_fns[n_pad_top][n_pad_left][n_pad_bottom][n_pad_right] = {};
+
+
+
+template struct WinogradGEMM<1, 6, 1, 3>::InputTransform<float>;
+template struct WinogradGEMM<6, 1, 3, 1>::InputTransform<float>;
+}  // namespace winograd
diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/output_6_3_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/output_6_3_fp32.cpp
new file mode 100644
index 0000000000..16667ccdb6
--- /dev/null
+++ b/src/core/NEON/kernels/convolution/winograd/transforms/output_6_3_fp32.cpp
@@ -0,0 +1,186 @@
+/*
+ * Copyright (c) 2017 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.
+ */
+
+#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/output.hpp"
+#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp"
+#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp"
+
+namespace winograd
+{
+
+using Transform = WinogradGEMM<1, 6, 1, 3>::OutputTransform<float>;
+using TransformTransposed = WinogradGEMM<6, 1, 3, 1>::OutputTransform<float>;
+
+template <>
+template <>
+int Transform::ops_performed(const Tensor4DShape &shape)
+{
+  (void) shape;
+  return 0;  // TODO
+}
+
+template <>
+template <>
+template <int pad_bottom, int pad_right>
+void Transform::process_tile(
+  const int n_channels,
+  const float* const matrix_base,
+  const int matrix_stride,
+  const float* const biases,
+  float* const output,
+  const int output_row_stride,
+  const int output_col_stride
+)
+{
+  (void) output_row_stride;
+  constexpr int cells_j = output_tile_cols - pad_right;
+
+  // Construct a map to the output cells
+  float *outptrs[cells_j];
+  for (int j = 0; j < cells_j; j++)
+  {
+    outptrs[j] = output + j*output_col_stride;
+  }
+  const float *inptr = matrix_base;
+  const float *bptr = biases;
+
+  // For each channel of the output
+  int channels_remaining = n_channels;
+#ifdef __arm_any__
+  for (; channels_remaining >= 4; channels_remaining -= 4)
+  {
+    // Matrices used and computed during this transform
+    float32x4_t F[inner_tile_cols], f[output_tile_cols], b = vdupq_n_f32(0.0f);
+
+    // Read a 1x8 tile in the Winograd domain
+    for (int j = 0; j < inner_tile_cols; j++)
+    {
+      F[j] = vld1q_f32(inptr + j*matrix_stride);
+    }
+    inptr += 4;
+
+    f[0] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(F[6], 1), F[5], 1), F[4], 1), F[3], 1), F[2], 1), F[1], 1), F[0], 1);
+    f[1] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(F[2], 1), F[6], 3), F[4], 2), F[3], -2), F[5], -3), F[1], -1);
+    f[2] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(F[2], 1), F[1], 1), F[6], 9), F[5], 9), F[4], 4), F[3], 4);
+    f[3] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(F[2], 1), F[6], 27), F[4], 8), F[3], -8), F[5], -27), F[1], -1);
+    f[4] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(F[2], 1), F[1], 1), F[6], 81), F[5], 81), F[4], 16), F[3], 16);
+    f[5] = vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmlaq_n_f32(vmulq_n_f32(F[7], 1), F[2], 1), F[6], 243), F[4], 32), F[3], -32), F[5], -243), F[1], -1);
+
+    // Write out the output tile
+    if (bptr != 0)
+    {
+      b = vld1q_f32(bptr);
+      bptr += 4;
+    }
+    for (int j = 0; j < cells_j; j++)
+    {
+      vst1q_f32(outptrs[j], f[j] + b);
+      outptrs[j] += 4;
+    }
+  }
+  for (; channels_remaining >= 2; channels_remaining -= 2)
+  {
+    // Matrices used and computed during this transform
+    float32x2_t F[inner_tile_cols], f[output_tile_cols], b = vdup_n_f32(0.0f);
+
+    // Read a 1x8 tile in the Winograd domain
+    for (int j = 0; j < inner_tile_cols; j++)
+    {
+      F[j] = vld1_f32(inptr + j*matrix_stride);
+    }
+    inptr += 2;
+
+    f[0] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(F[6], 1), F[5], 1), F[4], 1), F[3], 1), F[2], 1), F[1], 1), F[0], 1);
+    f[1] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(F[2], 1), F[6], 3), F[4], 2), F[3], -2), F[5], -3), F[1], -1);
+    f[2] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(F[2], 1), F[1], 1), F[6], 9), F[5], 9), F[4], 4), F[3], 4);
+    f[3] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(F[2], 1), F[6], 27), F[4], 8), F[3], -8), F[5], -27), F[1], -1);
+    f[4] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(F[2], 1), F[1], 1), F[6], 81), F[5], 81), F[4], 16), F[3], 16);
+    f[5] = vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmla_n_f32(vmul_n_f32(F[7], 1), F[2], 1), F[6], 243), F[4], 32), F[3], -32), F[5], -243), F[1], -1);
+
+    // Write out the output tile
+    if (bptr != 0)
+    {
+      b = vld1_f32(bptr);
+      bptr += 2;
+    }
+    for (int j = 0; j < cells_j; j++)
+    {
+      vst1_f32(outptrs[j], f[j] + b);
+      outptrs[j] += 2;
+    }
+  }
+#endif  // __arm_any__
+  for (; channels_remaining; channels_remaining--)
+  {
+    // Matrices used and computed during this transform
+    float F[inner_tile_cols], f[output_tile_cols], b = 0.0f;
+
+    // Read a 1x8 tile in the Winograd domain
+    for (int j = 0; j < inner_tile_cols; j++)
+    {
+      F[j] = *(inptr + j*matrix_stride);
+    }
+    inptr++;
+
+    f[0] = F[0]*1 + F[1]*1 + F[2]*1 + F[3]*1 + F[4]*1 + F[5]*1 + F[6]*1;
+    f[1] = F[1]*-1 + F[5]*-3 + F[3]*-2 + F[4]*2 + F[6]*3 + F[2]*1;
+    f[2] = F[3]*4 + F[4]*4 + F[5]*9 + F[6]*9 + F[1]*1 + F[2]*1;
+    f[3] = F[1]*-1 + F[5]*-27 + F[3]*-8 + F[4]*8 + F[6]*27 + F[2]*1;
+    f[4] = F[3]*16 + F[4]*16 + F[5]*81 + F[6]*81 + F[1]*1 + F[2]*1;
+    f[5] = F[1]*-1 + F[5]*-243 + F[3]*-32 + F[4]*32 + F[6]*243 + F[2]*1 + F[7]*1;
+
+    // Write out the output tile
+    if (bptr != 0)
+    {
+      b = *(bptr++);
+    }
+    for (int j = 0; j < cells_j; j++)
+    {
+      *(outptrs[j]++) = f[j] + b;
+    }
+  }
+}
+
+template <>
+template <>
+const Transform::TileFn Transform::tile_fns[max_pad_bottom][max_pad_right] =
+{
+  {
+    Transform::template process_tile<0, 0>,
+    Transform::template process_tile<0, 1>,
+    Transform::template process_tile<0, 2>,
+    Transform::template process_tile<0, 3>,
+    Transform::template process_tile<0, 4>,
+    Transform::template process_tile<0, 5>,
+  },
+};
+
+template <>
+template <>
+const TransformTransposed::TileFn TransformTransposed::tile_fns[max_pad_bottom][max_pad_right] = {};
+
+
+template struct WinogradGEMM<1, 6, 1, 3>::OutputTransform<float>;
+template struct WinogradGEMM<6, 1, 3, 1>::OutputTransform<float>;
+}  // namespace winograd
diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/weights_6_3_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/weights_6_3_fp32.cpp
new file mode 100644
index 0000000000..c560aa8c8f
--- /dev/null
+++ b/src/core/NEON/kernels/convolution/winograd/transforms/weights_6_3_fp32.cpp
@@ -0,0 +1,125 @@
+/*
+ * Copyright (c) 2017 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.
+ */
+
+#include "arm_compute/core/NEON/kernels/convolution/common/arm.hpp"
+#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp"
+#include "arm_compute/core/NEON/kernels/convolution/winograd/transforms/kernel.hpp"
+
+
+namespace winograd
+{
+  template <>
+  template <>
+  void WinogradGEMM<1, 6, 1, 3>::WeightsTransform<float>::execute(
+    const int n_output_channels,
+    const int n_input_channels,
+    const float* const input,  // NOTE: Data in HWIO order
+    float* const output,
+    const int matrix_stride,
+    const int matrix_row_stride
+  )
+  {
+    // Get pointers to each cell of the weight tensor
+    const auto weight_col_stride = n_input_channels * n_output_channels;
+    const float *inptrs[3];
+    for (int j = 0; j < 3; j++)
+    {
+      inptrs[j] = input + j*weight_col_stride;
+    }
+
+    // For each input channel
+    for (int ic = 0; ic < n_input_channels; ic++)
+    {
+      float *outptr = output + ic * matrix_row_stride;
+
+      // For each output channel
+      int channels_remaining = n_output_channels;
+      for (; channels_remaining; channels_remaining--)
+      {
+        // Matrices used and computed in this kernel
+        float w[3], V[inner_tile_cols];
+
+        // Read weights
+        for (int j = 0; j < 3; j++)
+        {
+          w[j] = *(inptrs[j]++);
+        }
+
+        // Compute V = w WT
+        V[0] = (w[0]*-1) / 36.0f;
+        V[1] = (w[1]*-1 + w[0]*1 + w[2]*1) / 48.0f;
+        V[2] = (w[0]*1 + w[1]*1 + w[2]*1) / 48.0f;
+        V[3] = (w[0]*-1 + w[2]*-4 + w[1]*2) / 120.0f;
+        V[4] = (w[0]*-1 + w[2]*-4 + w[1]*-2) / 120.0f;
+        V[5] = (w[1]*-3 + w[2]*9 + w[0]*1) / 720.0f;
+        V[6] = (w[1]*3 + w[2]*9 + w[0]*1) / 720.0f;
+        V[7] = (w[2]*1) / 1;
+
+        // Store the transformed weights
+        for (int j = 0; j < inner_tile_cols; j++)
+        {
+          *(outptr + j*matrix_stride) = V[j];
+        }
+        outptr++;
+      }
+    }
+  }
+
+  template <>
+  template <>
+  int WinogradGEMM<1, 6, 1, 3>::WeightsTransform<float>::ops_performed(const KernelShape &shape)
+  {
+    (void) shape;
+    return 0;  // TODO
+  }
+
+  template <>
+  template <>
+  void WinogradGEMM<6, 1, 3, 1>::WeightsTransform<float>::execute(
+    const int n_output_channels,
+    const int n_input_channels,
+    const float* const input,  // NOTE: Data in HWIO order
+    float* const output,
+    const int matrix_stride,
+    const int matrix_row_stride
+  )
+  {
+    // Redirect to the 1xN implementation
+    WinogradGEMM<1, 6, 1, 3>::template WeightsTransform<float>::execute(
+      n_output_channels, n_input_channels, input, output, matrix_stride,
+      matrix_row_stride
+    );
+  }
+
+  template <>
+  template <>
+  int WinogradGEMM<6, 1, 3, 1>::WeightsTransform<float>::ops_performed(const KernelShape &shape)
+  {
+    (void) shape;
+    return 0;  // TODO
+  }
+
+  template struct WinogradGEMM<1, 6, 1, 3>::WeightsTransform<float>;
+  template struct WinogradGEMM<6, 1, 3, 1>::WeightsTransform<float>;
+}
diff --git a/src/core/NEON/kernels/convolution/winograd/winograd_gemm.cpp b/src/core/NEON/kernels/convolution/winograd/winograd_gemm.cpp
index a5d43024a4..d544fd5710 100644
--- a/src/core/NEON/kernels/convolution/winograd/winograd_gemm.cpp
+++ b/src/core/NEON/kernels/convolution/winograd/winograd_gemm.cpp
@@ -225,4 +225,7 @@ int WinogradGEMM<kernel_rows, kernel_cols, output_tile_rows, output_tile_cols>::
 template class WinogradGEMM<2, 2, 3, 3>::Convolution<float, float>;
 template class WinogradGEMM<4, 4, 3, 3>::Convolution<float, float>;
 
+template class WinogradGEMM<1, 6, 1, 3>::Convolution<float, float>;
+template class WinogradGEMM<6, 1, 3, 1>::Convolution<float, float>;
+
 template class WinogradGEMM<2, 2, 5, 5>::Convolution<float, float>;
diff --git a/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp b/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp
index 11bb2d881b..ff7934e23a 100644
--- a/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp
@@ -52,25 +52,14 @@ inline Tensor4DShape internal_get_input_shape(const arm_compute::ITensor *input)
 
 Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info)
 {
-    const DataLayout   data_layout = input->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);
-
     ARM_COMPUTE_UNUSED(output);
-    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
-    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(width_idx) != 3 && weights->dimension(height_idx) != 5, "Only 3 and 5 kernels are supported");
-    ARM_COMPUTE_RETURN_ERROR_ON(weights->num_dimensions() > 4);
-
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd layer only supports unit strides.");
-
     if(biases != nullptr)
     {
         ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, biases);
         ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
     }
-
-    return Status{};
+    return INEWinogradLayerTransformWeightsKernel<float>::validate(input, weights);
 }
 
 Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims)
@@ -85,7 +74,14 @@ Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims)
     {
         output_tile = Size2D(2U, 2U);
     }
-
+    else if(kernel_dims == Size2D(1U, 3U))
+    {
+        output_tile = Size2D(1U, 6U);
+    }
+    else if(kernel_dims == Size2D(3U, 1U))
+    {
+        output_tile = Size2D(6U, 1U);
+    }
     return output_tile;
 }
 
@@ -94,7 +90,7 @@ bool check_support_fast_math(const Size2D &output_tile, const Size2D &kernel_siz
     // 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>>;
 
-    std::vector<WinogradConfiguration> fast_math_winograd =
+    const std::vector<WinogradConfiguration> fast_math_winograd =
     {
         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))
@@ -149,48 +145,78 @@ void NEWinogradConvolutionLayer::configure(const ITensor *input, const ITensor *
     int n_gemms = 0;
     int N_BLOCK = 0; // Size of block used by GEMM.
 
-    switch(kernel_size.width)
+    const bool square_kernel = kernel_size.width == kernel_size.height;
+
+    if(square_kernel)
     {
-        case 3:
+        switch(kernel_size.width)
         {
-            if(input->info()->dimension(width_idx) > 4 && input->info()->dimension(height_idx) > 4)
+            case 3:
             {
-                using config             = NEWinogradLayerConfiguration<float, float, 4, 4, 3, 3>;
-                transform_input_kernel   = support::cpp14::make_unique<config::TransformInputKernel>();
-                transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
-                transform_output_kernel  = support::cpp14::make_unique<config::TransformOutputKernel>();
-                n_gemms                  = config::WinogradBase::N_GEMMS;
-                N_BLOCK                  = config::WinogradConv::N_BLOCK;
+                if(input->info()->dimension(width_idx) > 4 && input->info()->dimension(height_idx) > 4)
+                {
+                    using config             = NEWinogradLayerConfiguration<float, float, 4, 4, 3, 3>;
+                    transform_input_kernel   = support::cpp14::make_unique<config::TransformInputKernel>();
+                    transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
+                    transform_output_kernel  = support::cpp14::make_unique<config::TransformOutputKernel>();
+                    n_gemms                  = config::WinogradBase::N_GEMMS;
+                    N_BLOCK                  = config::WinogradConv::N_BLOCK;
+                }
+                else
+                {
+                    using config             = NEWinogradLayerConfiguration<float, float, 2, 2, 3, 3>;
+                    transform_input_kernel   = support::cpp14::make_unique<config::TransformInputKernel>();
+                    transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
+                    transform_output_kernel  = support::cpp14::make_unique<config::TransformOutputKernel>();
+                    n_gemms                  = config::WinogradBase::N_GEMMS;
+                    N_BLOCK                  = config::WinogradConv::N_BLOCK;
+                }
+                break;
             }
-            else
+            case 5:
             {
-                using config             = NEWinogradLayerConfiguration<float, float, 2, 2, 3, 3>;
+                using config             = NEWinogradLayerConfiguration<float, float, 2, 2, 5, 5>;
                 transform_input_kernel   = support::cpp14::make_unique<config::TransformInputKernel>();
                 transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
                 transform_output_kernel  = support::cpp14::make_unique<config::TransformOutputKernel>();
                 n_gemms                  = config::WinogradBase::N_GEMMS;
                 N_BLOCK                  = config::WinogradConv::N_BLOCK;
+                break;
+            }
+            default:
+            {
+                ARM_COMPUTE_ERROR("Not supported.");
+                break;
             }
-            break;
         }
-        case 5:
+    }
+    else
+    {
+        if(kernel_size == Size2D(1, 3))
+        {
+            using config             = NEWinogradLayerConfiguration<float, float, 6, 1, 3, 1>;
+            transform_input_kernel   = support::cpp14::make_unique<config::TransformInputKernel>();
+            transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
+            transform_output_kernel  = support::cpp14::make_unique<config::TransformOutputKernel>();
+            n_gemms                  = config::WinogradBase::N_GEMMS;
+            N_BLOCK                  = config::WinogradConv::N_BLOCK;
+        }
+        else if(kernel_size == Size2D(3, 1))
         {
-            using config             = NEWinogradLayerConfiguration<float, float, 2, 2, 5, 5>;
+            using config             = NEWinogradLayerConfiguration<float, float, 1, 6, 1, 3>;
             transform_input_kernel   = support::cpp14::make_unique<config::TransformInputKernel>();
             transform_weights_kernel = support::cpp14::make_unique<config::TransformWeightsKernel>();
             transform_output_kernel  = support::cpp14::make_unique<config::TransformOutputKernel>();
             n_gemms                  = config::WinogradBase::N_GEMMS;
             N_BLOCK                  = config::WinogradConv::N_BLOCK;
-            break;
         }
-        default:
+        else
         {
             ARM_COMPUTE_ERROR("Not supported.");
-            break;
         }
     }
 
-    const PaddingType use_padding_type = (conv_info.pad_left() != 0u) ? PADDING_SAME : PADDING_VALID;
+    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
@@ -357,12 +383,12 @@ void NEWinogradConvolutionLayer::run()
         //Bring channels to the front as Winograd code expects the tensor to be in the format NHWC
         _permute_input.run();
     }
+
     // Transform input tensor to the winograd domain
     NEScheduler::get().schedule(_transform_input_kernel.get(), Window::DimX);
 
     //Run 16 GEMMs in multiple threads, each kernel runs one or more GEMMs
     _asm_glue.run();
-
     // Transform output tensor to the spatial domain
     NEScheduler::get().schedule(_transform_output_kernel.get(), Window::DimX);
 
@@ -391,9 +417,10 @@ Status NEWinogradConvolutionLayer::validate(const ITensorInfo *input, const ITen
     const size_t idx_height = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
 
     // Input shape, kernel size and output tile
-    const Size2D input_dims  = Size2D(input->dimension(idx_width), input->dimension(idx_height));
-    const Size2D kernel_size = Size2D(weights->dimension(idx_width), weights->dimension(idx_height));
-    const Size2D output_tile = winograd_output_tile(input_dims, kernel_size);
+    const Size2D input_dims    = Size2D(input->dimension(idx_width), input->dimension(idx_height));
+    const Size2D kernel_size   = Size2D(weights->dimension(idx_width), weights->dimension(idx_height));
+    const Size2D output_tile   = winograd_output_tile(input_dims, kernel_size);
+    const bool   square_kernel = kernel_size.width == kernel_size.height;
 
     // Check if the Winograd configuration requires fast math
     if(!enable_fast_math)
@@ -411,90 +438,120 @@ Status NEWinogradConvolutionLayer::validate(const ITensorInfo *input, const ITen
     const TensorShape input0_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input, winograd_info);
     const TensorInfo  input0       = input->clone()->set_tensor_shape(input0_shape);
 
-    switch(weights->dimension(idx_width))
+    if(square_kernel)
     {
-        case 3:
+        switch(weights->dimension(idx_width))
         {
-            if(input_dims.width > 4 && input_dims.height > 4)
+            case 3:
             {
-                ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 4, 4, 3, 3>::validate(input, &input0, winograd_info)));
+                if(input_dims.width > 4 && input_dims.height > 4)
+                {
+                    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 4, 4, 3, 3>::validate(input, &input0, winograd_info)));
+                }
+                else
+                {
+                    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 2, 3, 3>::validate(input, &input0, winograd_info)));
+                }
+                break;
             }
-            else
+            case 5:
             {
-                ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 2, 3, 3>::validate(input, &input0, winograd_info)));
+                ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 2, 5, 5>::validate(input, &input0, winograd_info)));
+                break;
+            }
+            default:
+            {
+                ARM_COMPUTE_RETURN_ERROR_MSG("Only 3x3 and 5x5 kernels supported.");
+                break;
             }
-            break;
-        }
-        case 5:
-        {
-            ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 2, 5, 5>::validate(input, &input0, winograd_info)));
-            break;
-        }
-        default:
-        {
-            ARM_COMPUTE_RETURN_ERROR_MSG("Only 3x3 and 5x5 kernels supported.");
-            break;
         }
-    }
-    // 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 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);
 
-    switch(weights->dimension(idx_width))
-    {
-        case 3:
+        switch(weights->dimension(idx_width))
         {
-            if(input_dims.width > 4 && input_dims.height > 4)
+            case 3:
             {
-                ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 4, 4, 3, 3>::validate(weights, &input1, winograd_info)));
+                if(input_dims.width > 4 && input_dims.height > 4)
+                {
+                    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 4, 4, 3, 3>::validate(weights, &input1, winograd_info)));
+                }
+                else
+                {
+                    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 2, 3, 3>::validate(weights, &input1, winograd_info)));
+                }
+                break;
             }
-            else
+            case 5:
             {
-                ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 2, 3, 3>::validate(weights, &input1, winograd_info)));
+                ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 2, 5, 5>::validate(weights, &input1, winograd_info)));
+                break;
+            }
+            default:
+            {
+                ARM_COMPUTE_RETURN_ERROR_MSG("Only 3x3 and 5x5 kernels supported.");
+                break;
             }
-            break;
-        }
-        case 5:
-        {
-            ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 2, 5, 5>::validate(weights, &input1, winograd_info)));
-            break;
-        }
-        default:
-        {
-            ARM_COMPUTE_RETURN_ERROR_MSG("Only 3x3 and 5x5 kernels supported.");
-            break;
         }
-    }
-    // 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);
-    switch(weights->dimension(idx_width))
-    {
-        case 3:
+        // 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);
+        switch(weights->dimension(idx_width))
         {
-            if(input_dims.width > 4 && input_dims.height > 4)
+            case 3:
             {
-                // Validate output transform
-                ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 4, 4, 3, 3>::validate(&batched_mm_output, biases, output, winograd_info)));
+                if(input_dims.width > 4 && input_dims.height > 4)
+                {
+                    // Validate output transform
+                    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 4, 4, 3, 3>::validate(&batched_mm_output, biases, output, winograd_info)));
+                }
+                else
+                {
+                    // Validate output transform
+                    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 2, 3, 3>::validate(&batched_mm_output, biases, output, winograd_info)));
+                }
+                break;
             }
-            else
+            case 5:
             {
                 // Validate output transform
-                ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 2, 3, 3>::validate(&batched_mm_output, biases, output, winograd_info)));
+                ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 2, 5, 5>::validate(&batched_mm_output, biases, output, winograd_info)));
+                break;
+            }
+            default:
+            {
+                ARM_COMPUTE_RETURN_ERROR_MSG("Only 3x3 and 5x5 kernels supported.");
+                break;
             }
-            break;
         }
-        case 5:
+    }
+    else
+    {
+        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);
+        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, 1))
         {
-            // Validate output transform
-            ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 2, 5, 5>::validate(&batched_mm_output, biases, output, winograd_info)));
-            break;
+            ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 1, 6, 1, 3>::validate(input, &input0, winograd_info)));
+            // Validate filter transform
+            ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 1, 6, 1, 3>::validate(weights, &input1, winograd_info)));
+            ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 1, 6, 1, 3>::validate(&batched_mm_output, biases, output, winograd_info)));
         }
-        default:
+        else if(kernel_size == Size2D(1, 3))
         {
-            ARM_COMPUTE_RETURN_ERROR_MSG("Only 3x3 and 5x5 kernels supported.");
-            break;
+            ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 6, 1, 3, 1>::validate(input, &input0, winograd_info)));
+            // Validate filter transform
+            ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 6, 1, 3, 1>::validate(weights, &input1, winograd_info)));
+            ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 6, 1, 3, 1>::validate(&batched_mm_output, biases, output, winograd_info)));
+        }
+        else
+        {
+            ARM_COMPUTE_RETURN_ERROR_MSG("Kernel shape not supported");
         }
     }
     // Validate Activation Layer
@@ -515,8 +572,8 @@ void NEWinogradConvolutionLayer::prepare()
 
         // Transform weights
         NEScheduler::get().schedule(_transform_weights_kernel.get(), Window::DimX);
-        _weights_hwio.allocator()->free();
 
+        _weights_hwio.allocator()->free();
         _is_prepared = true;
     }
 }
diff --git a/tests/validation/NEON/ConvolutionLayer.cpp b/tests/validation/NEON/ConvolutionLayer.cpp
index 18072e0532..591af7b731 100644
--- a/tests/validation/NEON/ConvolutionLayer.cpp
+++ b/tests/validation/NEON/ConvolutionLayer.cpp
@@ -121,18 +121,97 @@ using NEWinogradConvolutionLayerNoBiasFixture = WinogradConvolutionLayerFastMath
 
 TEST_SUITE(FP32)
 
+TEST_SUITE(Conv1x3)
 FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT,
-                       combine(combine(combine(framework::dataset::concat(datasets::SmallWinogradConvolutionLayer3x3Dataset(),
-                                                                          datasets::SmallWinogradConvolutionLayer5x5Dataset()),
+                       combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x3Dataset(),
+                                               framework::dataset::make("DataType", { DataType::F32 })),
+                                       ActivationFunctionsDataset),
+                               framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+{
+    // Validate output
+    validate(Accessor(_target), _reference, abs_tolerance_f32);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY,
+                       combine(combine(combine(datasets::LargeWinogradConvolutionLayer1x3Dataset(),
                                                framework::dataset::make("DataType", { DataType::F32 })),
                                        ActivationFunctionsDataset),
                                framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+{
+    // Validate output
+    validate(Accessor(_target), _reference, abs_tolerance_f32);
+}
+
+TEST_SUITE_END() // Conv1x3
 
+TEST_SUITE(Conv3x1)
+FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT,
+                       combine(combine(combine(datasets::SmallWinogradConvolutionLayer3x1Dataset(),
+                                               framework::dataset::make("DataType", { DataType::F32 })),
+                                       ActivationFunctionsDataset),
+                               framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+{
+    // Validate output
+    validate(Accessor(_target), _reference, abs_tolerance_f32);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY,
+                       combine(combine(combine(datasets::LargeWinogradConvolutionLayer3x1Dataset(),
+                                               framework::dataset::make("DataType", { DataType::F32 })),
+                                       ActivationFunctionsDataset),
+                               framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 {
     // Validate output
     validate(Accessor(_target), _reference, abs_tolerance_f32);
 }
 
+TEST_SUITE_END() // Conv3x1
+
+TEST_SUITE(Conv3x3)
+FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT,
+                       combine(combine(combine(datasets::SmallWinogradConvolutionLayer3x3Dataset(),
+                                               framework::dataset::make("DataType", { DataType::F32 })),
+                                       ActivationFunctionsDataset),
+                               framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+
+{
+    // Validate output
+    validate(Accessor(_target), _reference, abs_tolerance_f32);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY,
+                       combine(combine(combine(datasets::LargeWinogradConvolutionLayer3x3Dataset(),
+                                               framework::dataset::make("DataType", { DataType::F32 })),
+                                       ActivationFunctionsDataset),
+                               framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+
+{
+    // Validate output
+    validate(Accessor(_target), _reference, abs_tolerance_f32);
+}
+TEST_SUITE_END() // Conv3x3
+
+TEST_SUITE(Conv5x5)
+FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT,
+                       combine(combine(combine(datasets::SmallWinogradConvolutionLayer5x5Dataset(),
+                                               framework::dataset::make("DataType", { DataType::F32 })),
+                                       ActivationFunctionsDataset),
+                               framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+
+{
+    // Validate output
+    validate(Accessor(_target), _reference, abs_tolerance_f32);
+}
+FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY,
+                       combine(combine(combine(datasets::LargeWinogradConvolutionLayer5x5Dataset(),
+                                               framework::dataset::make("DataType", { DataType::F32 })),
+                                       ActivationFunctionsDataset),
+                               framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
+
+{
+    // Validate output
+    validate(Accessor(_target), _reference, abs_tolerance_f32);
+}
+
+TEST_SUITE_END() // Conv5x5
+
 FIXTURE_DATA_TEST_CASE(RunSmallNoBias, NEWinogradConvolutionLayerNoBiasFixture<float>, framework::DatasetMode::PRECOMMIT,
                        combine(combine(combine(framework::dataset::concat(datasets::SmallWinogradConvolutionLayer3x3Dataset(),
                                                                           datasets::SmallWinogradConvolutionLayer5x5Dataset()),