COMPMID-1552: support kernels sizes 1x7, 7x1, 1x5, 5x1 in NEWinograd

Refactored the validate method to make it easier to maintain in the future when
adding support for new kernels sizes

Change-Id: I12d9fe7af15ceb0e655cef61ca94407558fb29e8
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/146713
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Michalis Spyrou <michalis.spyrou@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

9 files changed, 875 insertions, 125 deletions

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 6ed146bf85..00327f5102 100644
--- a/arm_compute/core/NEON/kernels/convolution/winograd/transforms/output.hpp
+++ b/arm_compute/core/NEON/kernels/convolution/winograd/transforms/output.hpp
@@ -217,7 +217,7 @@ namespace winograd
       _matrix_base + start_channel,
       _matrix_stride,
       _matrix_row_stride,
-      (_biases)?(_biases + start_channel):(nullptr),
+      (_biases != nullptr) ? _biases + start_channel : nullptr,
       _outptr + start_channel
     );
   }
diff --git a/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp b/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp
index 8f990712e8..f5609b6f5c 100644
--- a/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEWinogradConvolutionLayerKernel.cpp
@@ -42,7 +42,7 @@ 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) } };
+    const std::array<Size2D, 8> supported_input_sizes = { { Size2D(1, 3), Size2D(3, 1), Size2D(5, 5), Size2D(3, 3), Size2D(1, 5), Size2D(5, 1), Size2D(7, 1), Size2D(1, 7) } };
     return std::end(supported_input_sizes) != std::find(std::begin(supported_input_sizes), std::end(supported_input_sizes), size);
 }
 
@@ -56,10 +56,10 @@ Status validate_arguments_winograd_weight_trans(const ITensorInfo *input, const
     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_MSG(!is_kernel_size_supported(Size2D(input_width, input_height)), "Only 1x3, 3x1, 1x5, 5x1, 7x1, 1x7, 3x3 and 5x5 kernels are supported");
     ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 4);
     const Size2D &output_tile = winograd_info.output_tile_size;
-    const std::array<Size2D, 4> supported_tile_sizes = { { Size2D(2U, 2U), Size2D(4U, 4U), Size2D(1U, 6U), Size2D(6U, 1U) } };
+    const std::array<Size2D, 8> supported_tile_sizes = { { Size2D(2U, 2U), Size2D(4U, 4U), Size2D(1U, 6U), Size2D(6U, 1U), Size2D(4, 1), Size2D(1, 4), Size2D(2, 1), Size2D(1, 2) } };
     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
@@ -305,6 +305,10 @@ template class NEWinogradLayerTransformWeightsKernel<float, 2, 2, 5, 5>;
 template class NEWinogradLayerTransformWeightsKernel<float, 1, 6, 1, 3>;
 template class NEWinogradLayerTransformWeightsKernel<float, 6, 1, 3, 1>;
 
+template class NEWinogradLayerTransformWeightsKernel<float, 1, 4, 1, 5>;
+template class NEWinogradLayerTransformWeightsKernel<float, 4, 1, 5, 1>;
+template class NEWinogradLayerTransformWeightsKernel<float, 1, 2, 1, 7>;
+template class NEWinogradLayerTransformWeightsKernel<float, 2, 1, 7, 1>;
 // Input transform
 
 template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
@@ -401,6 +405,11 @@ template class NEWinogradLayerTransformInputKernel<float, 2, 2, 5, 5>;
 template class NEWinogradLayerTransformInputKernel<float, 1, 6, 1, 3>;
 template class NEWinogradLayerTransformInputKernel<float, 6, 1, 3, 1>;
 
+template class NEWinogradLayerTransformInputKernel<float, 1, 4, 1, 5>;
+template class NEWinogradLayerTransformInputKernel<float, 4, 1, 5, 1>;
+template class NEWinogradLayerTransformInputKernel<float, 1, 2, 1, 7>;
+template class NEWinogradLayerTransformInputKernel<float, 2, 1, 7, 1>;
+
 // Output transform
 
 template <typename T, int OutputTileRows, int OutputTileCols, int KernelRows, int KernelCols>
@@ -513,4 +522,9 @@ template class NEWinogradLayerTransformOutputKernel<float, 2, 2, 5, 5>;
 template class NEWinogradLayerTransformOutputKernel<float, 1, 6, 1, 3>;
 template class NEWinogradLayerTransformOutputKernel<float, 6, 1, 3, 1>;
 
+template class NEWinogradLayerTransformOutputKernel<float, 1, 4, 1, 5>;
+template class NEWinogradLayerTransformOutputKernel<float, 4, 1, 5, 1>;
+template class NEWinogradLayerTransformOutputKernel<float, 1, 2, 1, 7>;
+template class NEWinogradLayerTransformOutputKernel<float, 2, 1, 7, 1>;
+
 } // namespace arm_compute
diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/output_2_7_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/output_2_7_fp32.cpp
new file mode 100644
index 0000000000..cfd2029f11
--- /dev/null
+++ b/src/core/NEON/kernels/convolution/winograd/transforms/output_2_7_fp32.cpp
@@ -0,0 +1,170 @@
+/*
+ * 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, 2, 1, 7>::OutputTransform<float>;
+using TransformTransposed = WinogradGEMM<2, 1, 7, 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(vmlaq_n_f32(vmulq_n_f32(F[7], 1), F[2], 1), F[6], 3), F[4], 2), F[3], -2), F[5], -3), 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(vmla_n_f32(vmul_n_f32(F[7], 1), F[2], 1), F[6], 3), F[4], 2), F[3], -2), F[5], -3), 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[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>,
+  },
+};
+
+
+template <>
+template <>
+const TransformTransposed::TileFn TransformTransposed::tile_fns[max_pad_bottom][max_pad_right] = {};
+
+template struct WinogradGEMM<1, 2, 1, 7>::OutputTransform<float>;
+template struct WinogradGEMM<2, 1, 7, 1>::OutputTransform<float>;
+}  // namespace winograd
diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/output_4_5_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/output_4_5_fp32.cpp
new file mode 100644
index 0000000000..2417f527bf
--- /dev/null
+++ b/src/core/NEON/kernels/convolution/winograd/transforms/output_4_5_fp32.cpp
@@ -0,0 +1,178 @@
+/*
+ * 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, 4, 1, 5>::OutputTransform<float>;
+using TransformTransposed = WinogradGEMM<4, 1, 5, 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(vmlaq_n_f32(vmulq_n_f32(F[7], 1), F[2], 1), F[6], 27), F[4], 8), F[3], -8), F[5], -27), 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(vmla_n_f32(vmul_n_f32(F[7], 1), F[2], 1), F[6], 27), F[4], 8), F[3], -8), F[5], -27), 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[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>,
+  },
+};
+
+template <>
+template <>
+const TransformTransposed::TileFn TransformTransposed::tile_fns[max_pad_bottom][max_pad_right] = {};
+
+
+template struct WinogradGEMM<1, 4, 1, 5>::OutputTransform<float>;
+template struct WinogradGEMM<4, 1, 5, 1>::OutputTransform<float>;
+}  // namespace winograd
diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/weights_2_7_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/weights_2_7_fp32.cpp
new file mode 100644
index 0000000000..85cf418656
--- /dev/null
+++ b/src/core/NEON/kernels/convolution/winograd/transforms/weights_2_7_fp32.cpp
@@ -0,0 +1,124 @@
+/*
+ * 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, 2, 1, 7>::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[kernel_cols];
+    for (int j = 0; j < kernel_cols; 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[kernel_cols], V[inner_tile_cols];
+
+        // Read weights
+        for (int j = 0; j < kernel_cols; j++)
+        {
+          w[j] = *(inptrs[j]++);
+        }
+
+        // Compute V = w WT
+        V[0] = (w[0]*-1) / 36.0f;
+        V[1] = (w[1]*-1 + w[3]*-1 + w[5]*-1 + w[0]*1 + w[2]*1 + w[4]*1 + w[6]*1) / 48.0f;
+        V[2] = (w[0]*1 + w[1]*1 + w[2]*1 + w[3]*1 + w[4]*1 + w[5]*1 + w[6]*1) / 48.0f;
+        V[3] = (w[0]*-1 + w[6]*-64 + w[4]*-16 + w[2]*-4 + w[1]*2 + w[3]*8 + w[5]*32) / 120.0f;
+        V[4] = (w[0]*-1 + w[6]*-64 + w[5]*-32 + w[4]*-16 + w[3]*-8 + w[2]*-4 + w[1]*-2) / 120.0f;
+        V[5] = (w[5]*-243 + w[3]*-27 + w[1]*-3 + w[2]*9 + w[4]*81 + w[6]*729 + w[0]*1) / 720.0f;
+        V[6] = (w[1]*3 + w[2]*9 + w[3]*27 + w[4]*81 + w[5]*243 + w[6]*729 + w[0]*1) / 720.0f;
+        V[7] = (w[6]*1) / 1.0f;
+
+        // 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, 2, 1, 7>::WeightsTransform<float>::ops_performed(const KernelShape &shape)
+  {
+    (void) shape;
+    return 0;  // TODO
+  }
+
+  template <>
+  template <>
+  void WinogradGEMM<2, 1, 7, 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, 2, 1, 7>::template WeightsTransform<float>::execute(
+      n_output_channels, n_input_channels, input, output, matrix_stride,
+      matrix_row_stride
+    );
+  }
+
+  template <>
+  template <>
+  int WinogradGEMM<2, 1, 7, 1>::WeightsTransform<float>::ops_performed(const KernelShape &shape)
+  {
+    (void) shape;
+    return 0;  // TODO
+  }
+
+  template struct WinogradGEMM<1, 2, 1, 7>::WeightsTransform<float>;
+  template struct WinogradGEMM<2, 1, 7, 1>::WeightsTransform<float>;
+}
diff --git a/src/core/NEON/kernels/convolution/winograd/transforms/weights_4_5_fp32.cpp b/src/core/NEON/kernels/convolution/winograd/transforms/weights_4_5_fp32.cpp
new file mode 100644
index 0000000000..2f14e20142
--- /dev/null
+++ b/src/core/NEON/kernels/convolution/winograd/transforms/weights_4_5_fp32.cpp
@@ -0,0 +1,124 @@
+/*
+ * 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, 4, 1, 5>::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[kernel_cols];
+    for (int j = 0; j < kernel_cols; 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[kernel_cols], V[inner_tile_cols];
+
+        // Read weights
+        for (int j = 0; j < kernel_cols; j++)
+        {
+          w[j] = *(inptrs[j]++);
+        }
+
+        // Compute V = w WT
+        V[0] = (w[0]*-1) / 36;
+        V[1] = (w[1]*-1 + w[3]*-1 + w[0]*1 + w[2]*1 + w[4]*1) / 48;
+        V[2] = (w[0]*1 + w[1]*1 + w[2]*1 + w[3]*1 + w[4]*1) / 48;
+        V[3] = (w[0]*-1 + w[4]*-16 + w[2]*-4 + w[1]*2 + w[3]*8) / 120;
+        V[4] = (w[0]*-1 + w[4]*-16 + w[3]*-8 + w[2]*-4 + w[1]*-2) / 120;
+        V[5] = (w[3]*-27 + w[1]*-3 + w[2]*9 + w[4]*81 + w[0]*1) / 720;
+        V[6] = (w[1]*3 + w[2]*9 + w[3]*27 + w[4]*81 + w[0]*1) / 720;
+        V[7] = (w[4]*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, 4, 1, 5>::WeightsTransform<float>::ops_performed(const KernelShape &shape)
+  {
+    (void) shape;
+    return 0;  // TODO
+  }
+
+  template <>
+  template <>
+  void WinogradGEMM<4, 1, 5, 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, 4, 1, 5>::template WeightsTransform<float>::execute(
+      n_output_channels, n_input_channels, input, output, matrix_stride,
+      matrix_row_stride
+    );
+  }
+
+  template <>
+  template <>
+  int WinogradGEMM<4, 1, 5, 1>::WeightsTransform<float>::ops_performed(const KernelShape &shape)
+  {
+    (void) shape;
+    return 0;  // TODO
+  }
+
+  template struct WinogradGEMM<1, 4, 1, 5>::WeightsTransform<float>;
+  template struct WinogradGEMM<4, 1, 5, 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 d544fd5710..a7de2fd3e5 100644
--- a/src/core/NEON/kernels/convolution/winograd/winograd_gemm.cpp
+++ b/src/core/NEON/kernels/convolution/winograd/winograd_gemm.cpp
@@ -229,3 +229,12 @@ 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>;
+
+template class WinogradGEMM<1, 4, 1, 5>::Convolution<float, float>;
+template class WinogradGEMM<4, 1, 5, 1>::Convolution<float, float>;
+
+template class WinogradGEMM<1, 2, 1, 7>::Convolution<float, float>;
+template class WinogradGEMM<2, 1, 7, 1>::Convolution<float, float>;
+
+
+
diff --git a/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp b/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp
index ff7934e23a..e41b0be860 100644
--- a/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp
+++ b/src/runtime/NEON/functions/NEWinogradConvolutionLayer.cpp
@@ -39,6 +39,121 @@ namespace arm_compute
 {
 namespace
 {
+inline Status validate_kernel_3x3(const Size2D input_dims, const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
+                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_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)));
+        ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 4, 4, 3, 3>::validate(weights, input1, winograd_info)));
+        ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 4, 4, 3, 3>::validate(batched_mm_output, biases, output, winograd_info)));
+    }
+    else
+    {
+        ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 2, 3, 3>::validate(input, input0, winograd_info)));
+        ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 2, 3, 3>::validate(weights, input1, winograd_info)));
+        ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 2, 3, 3>::validate(batched_mm_output, biases, output, winograd_info)));
+    }
+
+    if(act_info.enabled())
+    {
+        NEActivationLayer::validate(output, nullptr, act_info);
+    }
+    return Status{};
+}
+
+inline Status validate_kernel_5x5(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
+                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 2, 5, 5>::validate(input, input0, winograd_info)));
+    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 2, 5, 5>::validate(weights, input1, winograd_info)));
+    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 2, 5, 5>::validate(batched_mm_output, biases, output, winograd_info)));
+    if(act_info.enabled())
+    {
+        NEActivationLayer::validate(output, nullptr, act_info);
+    }
+    return Status{};
+}
+
+inline Status validate_kernel_3x1(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
+                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 1, 6, 1, 3>::validate(input, input0, winograd_info)));
+    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)));
+    if(act_info.enabled())
+    {
+        NEActivationLayer::validate(output, nullptr, act_info);
+    }
+    return Status{};
+}
+
+inline Status validate_kernel_1x3(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
+                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 6, 1, 3, 1>::validate(input, input0, winograd_info)));
+    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)));
+
+    if(act_info.enabled())
+    {
+        NEActivationLayer::validate(output, nullptr, act_info);
+    }
+    return Status{};
+}
+
+inline Status validate_kernel_5x1(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
+                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 1, 4, 1, 5>::validate(input, input0, winograd_info)));
+    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 1, 4, 1, 5>::validate(weights, input1, winograd_info)));
+    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 1, 4, 1, 5>::validate(batched_mm_output, biases, output, winograd_info)));
+    if(act_info.enabled())
+    {
+        NEActivationLayer::validate(output, nullptr, act_info);
+    }
+    return Status{};
+}
+inline Status validate_kernel_1x5(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
+                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 4, 1, 5, 1>::validate(input, input0, winograd_info)));
+    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 4, 1, 5, 1>::validate(weights, input1, winograd_info)));
+    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 4, 1, 5, 1>::validate(batched_mm_output, biases, output, winograd_info)));
+    if(act_info.enabled())
+    {
+        NEActivationLayer::validate(output, nullptr, act_info);
+    }
+    return Status{};
+}
+
+inline Status validate_kernel_7x1(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
+                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 1, 2, 1, 7>::validate(input, input0, winograd_info)));
+    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 1, 2, 1, 7>::validate(weights, input1, winograd_info)));
+    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 1, 2, 1, 7>::validate(batched_mm_output, biases, output, winograd_info)));
+    if(act_info.enabled())
+    {
+        NEActivationLayer::validate(output, nullptr, act_info);
+    }
+    return Status{};
+}
+
+inline Status validate_kernel_1x7(const ITensorInfo *input, const TensorInfo *input0, const TensorInfo *input1, const TensorInfo *batched_mm_output,
+                                  const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformInputKernel<float, 2, 1, 7, 1>::validate(input, input0, winograd_info)));
+    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformWeightsKernel<float, 2, 1, 7, 1>::validate(weights, input1, winograd_info)));
+    ARM_COMPUTE_RETURN_ON_ERROR((NEWinogradLayerTransformOutputKernel<float, 2, 1, 7, 1>::validate(batched_mm_output, biases, output, winograd_info)));
+
+    if(act_info.enabled())
+    {
+        NEActivationLayer::validate(output, nullptr, act_info);
+    }
+    return Status{};
+}
+
 inline Tensor4DShape internal_get_input_shape(const arm_compute::ITensor *input)
 {
     const DataLayout data_layout = input->info()->data_layout();
@@ -65,7 +180,6 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights,
 Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims)
 {
     Size2D output_tile = Size2D{};
-
     if(kernel_dims == Size2D(3U, 3U))
     {
         output_tile = (input_dims.width <= 4 && input_dims.height <= 4) ? Size2D(2U, 2U) : Size2D(4U, 4U);
@@ -82,6 +196,22 @@ Size2D winograd_output_tile(const Size2D &input_dims, const Size2D &kernel_dims)
     {
         output_tile = Size2D(6U, 1U);
     }
+    else if(kernel_dims == Size2D(1U, 5U))
+    {
+        output_tile = Size2D(1U, 4U);
+    }
+    else if(kernel_dims == Size2D(5U, 1U))
+    {
+        output_tile = Size2D(4U, 1U);
+    }
+    else if(kernel_dims == Size2D(7U, 1U))
+    {
+        output_tile = Size2D(2U, 1U);
+    }
+    else if(kernel_dims == Size2D(1U, 7U))
+    {
+        output_tile = Size2D(1U, 2U);
+    }
     return output_tile;
 }
 
@@ -210,6 +340,42 @@ void NEWinogradConvolutionLayer::configure(const ITensor *input, const ITensor *
             n_gemms                  = config::WinogradBase::N_GEMMS;
             N_BLOCK                  = config::WinogradConv::N_BLOCK;
         }
+        else if(kernel_size == Size2D(1, 5))
+        {
+            using config             = NEWinogradLayerConfiguration<float, float, 4, 1, 5, 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(5, 1))
+        {
+            using config             = NEWinogradLayerConfiguration<float, float, 1, 4, 1, 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;
+        }
+        else if(kernel_size == Size2D(1, 7))
+        {
+            using config             = NEWinogradLayerConfiguration<float, float, 2, 1, 7, 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(7, 1))
+        {
+            using config             = NEWinogradLayerConfiguration<float, float, 1, 2, 1, 7>;
+            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
         {
             ARM_COMPUTE_ERROR("Not supported.");
@@ -417,10 +583,9 @@ 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 bool   square_kernel = kernel_size.width == kernel_size.height;
+    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);
 
     // Check if the Winograd configuration requires fast math
     if(!enable_fast_math)
@@ -437,127 +602,49 @@ Status NEWinogradConvolutionLayer::validate(const ITensorInfo *input, const ITen
     // Validate input transform
     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);
-
-    if(square_kernel)
+    // Validate filter transform
+    const TensorShape input1_shape = misc::shape_calculator::compute_winograd_filter_transform_shape(*weights, winograd_info);
+    const TensorInfo  input1       = weights->clone()->set_tensor_shape(input1_shape);
+    // Validate batched matrix multiply
+    TensorShape batched_mm_output_shape = input0.tensor_shape();
+    batched_mm_output_shape[0]          = input1.tensor_shape()[0];
+    const TensorInfo batched_mm_output  = input0.clone()->set_tensor_shape(batched_mm_output_shape);
+
+    if(kernel_size == Size2D(3, 3))
     {
-        switch(weights->dimension(idx_width))
-        {
-            case 3:
-            {
-                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;
-            }
-            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);
-
-        switch(weights->dimension(idx_width))
-        {
-            case 3:
-            {
-                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;
-            }
-            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:
-            {
-                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;
-            }
-            case 5:
-            {
-                // Validate output transform
-                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;
-            }
-        }
+        return validate_kernel_3x3(input_dims, input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
     }
-    else
+    else if(kernel_size == Size2D(5, 5))
     {
-        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))
-        {
-            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)));
-        }
-        else if(kernel_size == Size2D(1, 3))
-        {
-            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");
-        }
+        return validate_kernel_5x5(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
     }
-    // Validate Activation Layer
-    if(act_info.enabled())
+    if(kernel_size == Size2D(3, 1))
     {
-        NEActivationLayer::validate(output, nullptr, act_info);
+        return validate_kernel_3x1(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
+    }
+    else if(kernel_size == Size2D(1, 3))
+    {
+        return validate_kernel_1x3(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
+    }
+    else if(kernel_size == Size2D(5, 1))
+    {
+        return validate_kernel_5x1(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
+    }
+    else if(kernel_size == Size2D(1, 5))
+    {
+        return validate_kernel_1x5(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
+    }
+    else if(kernel_size == Size2D(7, 1))
+    {
+        return validate_kernel_7x1(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
+    }
+    else if(kernel_size == Size2D(1, 7))
+    {
+        return validate_kernel_1x7(input, &input0, &input1, &batched_mm_output, weights, biases, output, winograd_info, act_info);
+    }
+    else
+    {
+        ARM_COMPUTE_RETURN_ERROR_MSG("Kernel shape not supported");
     }
     return Status{};
 }
diff --git a/tests/validation/NEON/ConvolutionLayer.cpp b/tests/validation/NEON/ConvolutionLayer.cpp
index 591af7b731..bb073b08ee 100644
--- a/tests/validation/NEON/ConvolutionLayer.cpp
+++ b/tests/validation/NEON/ConvolutionLayer.cpp
@@ -165,6 +165,50 @@ FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture<float>, frame
 
 TEST_SUITE_END() // Conv3x1
 
+TEST_SUITE(Conv1x5)
+FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT,
+                       combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x5Dataset(),
+                                               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::LargeWinogradConvolutionLayer1x5Dataset(),
+                                               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() // Conv1x5
+
+TEST_SUITE(Conv5x1)
+FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT,
+                       combine(combine(combine(datasets::SmallWinogradConvolutionLayer5x1Dataset(),
+                                               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::LargeWinogradConvolutionLayer5x1Dataset(),
+                                               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() // Conv5x1
+
 TEST_SUITE(Conv3x3)
 FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT,
                        combine(combine(combine(datasets::SmallWinogradConvolutionLayer3x3Dataset(),