COMPMID-784: Added support for biases in WinogradLayer.

1) Updated to the latest code from the RSH repo.
2) Moved winograd transforms into kernels.
3) Added support for biases

Change-Id: I7f39f34a599b49d7d9b549cc10a4f4d4a8007ab8
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/117474
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>

9 files changed, 1309 insertions, 45 deletions

diff --git a/src/core/NEON/kernels/NEWinogradLayerKernel.cpp b/src/core/NEON/kernels/NEWinogradLayerKernel.cpp
index ea48e1f32b..e2e4e40fe4 100644
--- a/src/core/NEON/kernels/NEWinogradLayerKernel.cpp
+++ b/src/core/NEON/kernels/NEWinogradLayerKernel.cpp
@@ -55,7 +55,7 @@ public:
         float *const       output,            /** Pointer to NHWC ordered output tensor, in the spatial domain. */
         float *const       winograd_output    /** Pointer to working space for the output tensor in the Winograd domain. Must be at least the size returned by `get_output_storage_size`. */
     )
-        : convolver(n_batches, n_input_channels, n_input_rows, n_input_cols, n_output_channels, same_padding, weights, weights_storage, input, winograd_input, output, winograd_output)
+        : convolver(n_batches, n_input_channels, n_input_rows, n_input_cols, n_output_channels, same_padding, weights, weights_storage, input, winograd_input, nullptr, output, winograd_output)
     {
     }
     T convolver;
@@ -65,24 +65,6 @@ Winograd3x3F32::~Winograd3x3F32()
 {
 }
 
-void Winograd3x3F32::transform_output()
-{
-    auto win = _pimpl->convolver.output_transform.get_window();
-    _pimpl->convolver.output_transform.run(0, win);
-}
-
-void Winograd3x3F32::transform_input()
-{
-    auto win = _pimpl->convolver.input_transform.get_window();
-    _pimpl->convolver.input_transform.run(0, win);
-}
-
-void Winograd3x3F32::transform_weights()
-{
-    auto win = _pimpl->convolver.weights_transform.get_window();
-    _pimpl->convolver.weights_transform.run(0, win);
-}
-
 Winograd3x3F32::Winograd3x3F32(
     const int          n_batches,         /** Number of batches in the input and output tensors. */
     const int          n_input_channels,  /** Number of feature maps in a batch of the input tensor. */
@@ -146,4 +128,128 @@ void NEWinogradLayerKernel::run(const Window &window, const ThreadInfo &info)
     const size_t last_gemm  = window.x().end();
     _convolver->_pimpl->convolver.gemms.run(first_gemm, last_gemm);
 }
+
+INEWinogradLayerTransformKernel::INEWinogradLayerTransformKernel()
+    : _convolver(nullptr)
+{
+}
+
+void INEWinogradLayerTransformKernel::configure(Winograd3x3F32 *convolver)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(convolver);
+    _convolver = convolver;
+}
+
+// Weights transform
+
+void NEWinogradLayerTransformWeightsKernel::configure(Winograd3x3F32 *convolver)
+{
+    INEWinogradLayerTransformKernel::configure(convolver);
+    Window win;
+    auto   win_last = _convolver->_pimpl->convolver.weights_transform.get_window();
+    win.set(Window::DimX, Window::Dimension(0, win_last, 1));
+    INEKernel::configure(win);
+}
+
+void NEWinogradLayerTransformWeightsKernel::run(const Window &window, const ThreadInfo &info)
+{
+    ARM_COMPUTE_UNUSED(info);
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    const size_t fst = window.x().start();
+    const size_t lst = window.x().end();
+    _convolver->_pimpl->convolver.weights_transform.run(fst, lst);
+}
+
+bool NEWinogradLayerTransformWeightsKernel::is_parallelisable() const
+{
+    return false;
+}
+
+// Input transform
+
+void NEWinogradLayerTransformInputKernel::configure(Winograd3x3F32 *convolver)
+{
+    INEWinogradLayerTransformKernel::configure(convolver);
+    Window win;
+    auto   win_last = _convolver->_pimpl->convolver.input_transform.get_window();
+    win.set(Window::DimX, Window::Dimension(0, win_last, 1));
+    INEKernel::configure(win);
+}
+
+void NEWinogradLayerTransformInputKernel::run(const Window &window, const ThreadInfo &info)
+{
+    ARM_COMPUTE_UNUSED(info);
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    const size_t fst = window.x().start();
+    const size_t lst = window.x().end();
+    _convolver->_pimpl->convolver.input_transform.run(fst, lst);
+}
+bool NEWinogradLayerTransformInputKernel::is_parallelisable() const
+{
+    return false;
+}
+
+// Output transform
+NEWinogradLayerTransformOutputKernel::NEWinogradLayerTransformOutputKernel()
+    : _biases(nullptr), _output_workspace(nullptr), _matrix_stride(0), _matrix_row_stride(0), _output(nullptr), _n_batches(0), _n_rows(0), _n_cols(0), _n_channels(0)
+{
+}
+
+void NEWinogradLayerTransformOutputKernel::configure(
+    const ITensor     *biases,
+    const float *const output_workingspace,
+    const int          matrix_stride,
+    float *const       output,
+    const int          n_batches,
+    const int          n_rows,
+    const int          n_cols,
+    const int          n_channels)
+{
+    using WinogradBase    = winograd::WinogradGEMM<2, 2, 3, 3>;
+    using OutputTransform = typename WinogradBase::template OutputTransform<float>;
+
+    _biases            = biases;
+    _output_workspace  = output_workingspace;
+    _matrix_stride     = matrix_stride;
+    _matrix_row_stride = roundup(n_channels, WinogradBase::Convolution<float, float>::N_BLOCK);
+    _output            = output;
+    _n_batches         = n_batches;
+    _n_rows            = n_rows;
+    _n_cols            = n_cols;
+    _n_channels        = n_channels;
+
+    // We don't have the biases buffer at this stage as it hasn't been allocated, we pass in nullptr OutputTransform is only used here to compute the window
+    OutputTransform output_transform(_output_workspace, _matrix_stride, _matrix_row_stride, nullptr, _output, _n_batches, _n_rows, _n_cols, _n_channels);
+    Window          win;
+    auto            win_last = output_transform.get_window();
+    win.set(Window::DimX, Window::Dimension(0, win_last, 1));
+    INEKernel::configure(win);
+}
+
+void NEWinogradLayerTransformOutputKernel::run(const Window &window, const ThreadInfo &info)
+{
+    ARM_COMPUTE_UNUSED(info);
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_NULLPTR(_biases->buffer());
+    ARM_COMPUTE_ERROR_ON_NULLPTR(_output_workspace);
+    ARM_COMPUTE_ERROR_ON_NULLPTR(_output);
+
+    using WinogradBase    = winograd::WinogradGEMM<2, 2, 3, 3>;
+    using OutputTransform = typename WinogradBase::template OutputTransform<float>;
+
+    OutputTransform output_transform(_output_workspace, _matrix_stride, _matrix_row_stride,
+                                     reinterpret_cast<float *>(_biases->buffer()), _output,
+                                     _n_batches, _n_rows, _n_cols, _n_channels);
+
+    // The code below cannot be moved to configure because biases hasn't been allocated at that point
+    const size_t fst = window.x().start();
+    const size_t lst = window.x().end();
+    output_transform.run(fst, lst);
+}
+
+bool NEWinogradLayerTransformOutputKernel::is_parallelisable() const
+{
+    return false;
+}
+
 } // namespace arm_compute
diff --git a/src/core/NEON/kernels/winograd/transforms/input_2x2_5x5_fp32.cpp b/src/core/NEON/kernels/winograd/transforms/input_2x2_5x5_fp32.cpp
new file mode 100644
index 0000000000..a6ebca1bce
--- /dev/null
+++ b/src/core/NEON/kernels/winograd/transforms/input_2x2_5x5_fp32.cpp
@@ -0,0 +1,458 @@
+/*
+ * 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 "transforms/input.hpp"
+#include "winograd_gemm.hpp"
+#include "arm.hpp"
+
+namespace winograd
+{
+
+using Transform = WinogradGEMM<2, 2, 5, 5>::InputTransform<float>;
+
+template <>
+template <>
+int Transform::ops_performed(const Tensor4DShape &input_shape)
+{
+  return 0;  // TODO
+}
+
+/*****************************************************************************
+* F(2x2, 5x5) implies the use of a 6x6 input tile.
+*
+* Build an array of the specialised methods that deal with each of the
+* different padding combinations which may be required. These padding
+* constraints are the space:
+*
+*     Padding top in {0, 1}
+*     Padding left in {0, 1}
+*     Padding bottom in {0, 1, 2, 3, 4}
+*     Padding right in {0, 1, 2, 3, 4}
+*/
+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
+)
+{
+  constexpr int cells_i = 6 - pad_bottom;
+  constexpr int cells_j = 6 - pad_right;
+
+  float *outptr = matrix_base;
+
+  // Get pointers into the input tile
+  const float *x_ptrs[6][6];
+  for (int i = pad_top, xi = 0; i < cells_i; i++, xi++)
+  {
+    // Get a pointer into the row
+    const float* const row_ptr = input_base + xi*input_row_stride;
+
+    for (int j = pad_left, xj = 0; j < cells_j; j++, xj++)
+    {
+      x_ptrs[i][j] = row_ptr + xj*input_col_stride;
+    }
+  }
+
+  // Matrices used/computed in this kernel.
+  float x[6][6], XTx[6][6], U[6][6];
+  for (int i = 0; i < 6; i++)
+  {
+    for (int j = 0; j < 6; j++)
+    {
+      x[i][j] = XTx[i][j] = 0.0f;
+    }
+  }
+
+  // Perform the Winograd input transformation for each channel in the input
+  // tensor.
+  int channels_remaining = n_channels;
+#ifdef __aarch64__
+  for (; channels_remaining >= 4; channels_remaining -= 4)
+  {
+    // Matrices used/computed in this kernel
+    float32x4_t x[6][6], XTx[6][6], U[6][6];
+    for (int i = 0; i < 6; i++)
+    {
+      for (int j = 0; j < 6; j++)
+      {
+        x[i][j] = vdupq_n_f32(0.0f);
+        XTx[i][j] = vdupq_n_f32(0.0f);
+      }
+    }
+
+    // Read a 6x6 tile in the Winograd domain
+    for (int i = pad_top; i < cells_i; i++)
+    {
+      for (int j = pad_left; j < cells_j; j++)
+      {
+        x[i][j] = vld1q_f32(x_ptrs[i][j]);
+        x_ptrs[i][j] += 4;
+      }
+    }
+
+    // Compute XT . x
+    for (int j = pad_left; j < cells_j; j++)
+    {
+      // XTx[0][j] =  4*x[0][j] + -5*x[2][j] +  1*x[4][j];
+      XTx[0][j] = vmlsq_n_f32(vmlaq_n_f32(x[4][j], x[0][j], 4.0f), x[2][j], 5.0f);
+
+      // XTx[1][j] = -4*x[1][j] + -4*x[2][j] +  1*x[3][j] +  1*x[4][j];
+      XTx[1][j] = vmlsq_n_f32(vaddq_f32(x[3][j], x[4][j]), vaddq_f32(x[1][j], x[2][j]), 4.0f);
+
+      // XTx[2][j] =  4*x[1][j] + -4*x[2][j] + -1*x[3][j] +  1*x[4][j];
+      XTx[2][j] = vmlaq_n_f32(vsubq_f32(x[4][j], x[3][j]), vsubq_f32(x[1][j], x[2][j]), 4.0f);
+
+      // XTx[3][j] = -2*x[1][j] + -1*x[2][j] +  2*x[3][j] +  1*x[4][j];
+      XTx[3][j] = vmlaq_n_f32(vsubq_f32(x[4][j], x[2][j]), vsubq_f32(x[3][j], x[1][j]), 2.0f);
+
+      // XTx[4][j] =  2*x[1][j] + -1*x[2][j] + -2*x[3][j] +  1*x[4][j];
+      XTx[4][j] = vmlaq_n_f32(vsubq_f32(x[4][j], x[2][j]), vsubq_f32(x[1][j], x[3][j]), 2.0f);
+
+      // XTx[5][j] =  4*x[1][j] + -5*x[3][j] +  1*x[5][j];
+      XTx[5][j] = vmlsq_n_f32(vmlaq_n_f32(x[5][j], x[1][j], 4.0f), x[3][j], 5.0f);
+    }
+
+    // Compute U = XT . x . X
+    for (int i = 0; i < 6; i++)
+    {
+      // U[i][0] =  4*XTx[i][0] + -5*XTx[i][2] +  1*XTx[i][4];
+      U[i][0] = vmlsq_n_f32(vmlaq_n_f32(XTx[i][4], XTx[i][0], 4.0f), XTx[i][2], 5.0f);
+
+      // U[i][1] = -4*XTx[i][1] + -4*XTx[i][2] +  1*XTx[i][3] +  1*XTx[i][4];
+      U[i][1] = vmlsq_n_f32(vaddq_f32(XTx[i][3], XTx[i][4]), vaddq_f32(XTx[i][1], XTx[i][2]), 4.0f);
+
+      // U[i][2] =  4*XTx[i][1] + -4*XTx[i][2] + -1*XTx[i][3] +  1*XTx[i][4];
+      U[i][2] = vmlaq_n_f32(vsubq_f32(XTx[i][4], XTx[i][3]), vsubq_f32(XTx[i][1], XTx[i][2]), 4.0f);
+
+      // U[i][3] = -2*XTx[i][1] + -1*XTx[i][2] +  2*XTx[i][3] +  1*XTx[i][4];
+      U[i][3] = vmlaq_n_f32(vsubq_f32(XTx[i][4], XTx[i][2]), vsubq_f32(XTx[i][3], XTx[i][1]), 2.0f);
+
+      // U[i][4] =  2*XTx[i][1] + -1*XTx[i][2] + -2*XTx[i][3] +  1*XTx[i][4];
+      U[i][4] = vmlaq_n_f32(vsubq_f32(XTx[i][4], XTx[i][2]), vsubq_f32(XTx[i][1], XTx[i][3]), 2.0f);
+
+      // U[i][5] =  4*XTx[i][1] + -5*XTx[i][3] +  1*XTx[i][5];
+      U[i][5] = vmlsq_n_f32(vmlaq_n_f32(XTx[i][5], XTx[i][1], 4.0f), XTx[i][3], 5.0f);
+    }
+
+    // Store the transformed matrix
+    for (int i = 0, m = 0; i < 6; i++)
+    {
+      for (int j = 0; j < 6; j++, m++)
+      {
+        vst1q_f32(outptr + m*matrix_stride, U[i][j]);
+      }
+    }
+    outptr += 4;
+  }
+#endif  // __aarch64__
+#ifdef __arm_any__
+  for (; channels_remaining >= 2; channels_remaining -= 2)
+  {
+    // Matrices used/computed in this kernel
+    float32x2_t x[6][6], XTx[6][6], U[6][6];
+    for (int i = 0; i < 6; i++)
+    {
+      for (int j = 0; j < 6; j++)
+      {
+        x[i][j] = vdup_n_f32(0.0f);
+        XTx[i][j] = vdup_n_f32(0.0f);
+      }
+    }
+
+    // Read a 6x6 tile in the Winograd domain
+    for (int i = pad_top; i < cells_i; i++)
+    {
+      for (int j = pad_left; j < cells_j; j++)
+      {
+        x[i][j] = vld1_f32(x_ptrs[i][j]);
+        x_ptrs[i][j] += 2;
+      }
+    }
+
+    // Compute XT . x
+    for (int j = pad_left; j < cells_j; j++)
+    {
+      // XTx[0][j] =  4*x[0][j] + -5*x[2][j] +  1*x[4][j];
+      XTx[0][j] = vmls_n_f32(vmla_n_f32(x[4][j], x[0][j], 4.0f), x[2][j], 5.0f);
+
+      // XTx[1][j] = -4*x[1][j] + -4*x[2][j] +  1*x[3][j] +  1*x[4][j];
+      XTx[1][j] = vmls_n_f32(vadd_f32(x[3][j], x[4][j]), vadd_f32(x[1][j], x[2][j]), 4.0f);
+
+      // XTx[2][j] =  4*x[1][j] + -4*x[2][j] + -1*x[3][j] +  1*x[4][j];
+      XTx[2][j] = vmla_n_f32(vsub_f32(x[4][j], x[3][j]), vsub_f32(x[1][j], x[2][j]), 4.0f);
+
+      // XTx[3][j] = -2*x[1][j] + -1*x[2][j] +  2*x[3][j] +  1*x[4][j];
+      XTx[3][j] = vmla_n_f32(vsub_f32(x[4][j], x[2][j]), vsub_f32(x[3][j], x[1][j]), 2.0f);
+
+      // XTx[4][j] =  2*x[1][j] + -1*x[2][j] + -2*x[3][j] +  1*x[4][j];
+      XTx[4][j] = vmla_n_f32(vsub_f32(x[4][j], x[2][j]), vsub_f32(x[1][j], x[3][j]), 2.0f);
+
+      // XTx[5][j] =  4*x[1][j] + -5*x[3][j] +  1*x[5][j];
+      XTx[5][j] = vmls_n_f32(vmla_n_f32(x[5][j], x[1][j], 4.0f), x[3][j], 5.0f);
+    }
+
+    // Compute U = XT . x . X
+    for (int i = 0; i < 6; i++)
+    {
+      // U[i][0] =  4*XTx[i][0] + -5*XTx[i][2] +  1*XTx[i][4];
+      U[i][0] = vmls_n_f32(vmla_n_f32(XTx[i][4], XTx[i][0], 4.0f), XTx[i][2], 5.0f);
+
+      // U[i][1] = -4*XTx[i][1] + -4*XTx[i][2] +  1*XTx[i][3] +  1*XTx[i][4];
+      U[i][1] = vmls_n_f32(vadd_f32(XTx[i][3], XTx[i][4]), vadd_f32(XTx[i][1], XTx[i][2]), 4.0f);
+
+      // U[i][2] =  4*XTx[i][1] + -4*XTx[i][2] + -1*XTx[i][3] +  1*XTx[i][4];
+      U[i][2] = vmla_n_f32(vsub_f32(XTx[i][4], XTx[i][3]), vsub_f32(XTx[i][1], XTx[i][2]), 4.0f);
+
+      // U[i][3] = -2*XTx[i][1] + -1*XTx[i][2] +  2*XTx[i][3] +  1*XTx[i][4];
+      U[i][3] = vmla_n_f32(vsub_f32(XTx[i][4], XTx[i][2]), vsub_f32(XTx[i][3], XTx[i][1]), 2.0f);
+
+      // U[i][4] =  2*XTx[i][1] + -1*XTx[i][2] + -2*XTx[i][3] +  1*XTx[i][4];
+      U[i][4] = vmla_n_f32(vsub_f32(XTx[i][4], XTx[i][2]), vsub_f32(XTx[i][1], XTx[i][3]), 2.0f);
+
+      // U[i][5] =  4*XTx[i][1] + -5*XTx[i][3] +  1*XTx[i][5];
+      U[i][5] = vmls_n_f32(vmla_n_f32(XTx[i][5], XTx[i][1], 4.0f), XTx[i][3], 5.0f);
+    }
+
+    // Store the transformed matrix
+    for (int i = 0, m = 0; i < 6; i++)
+    {
+      for (int j = 0; j < 6; j++, m++)
+      {
+        vst1_f32(outptr + m*matrix_stride, U[i][j]);
+      }
+    }
+    outptr += 2;
+  }
+#endif  // __arm_any__
+  for (; channels_remaining; channels_remaining--)
+  {
+    // Load x
+    for (int i = pad_top; i < cells_i; i++)
+    {
+      for (int j = pad_left; j < cells_j; j++)
+      {
+        x[i][j] = *(x_ptrs[i][j]++);
+      }
+    }
+
+    // Compute XT . x
+    for (int j = pad_left; j < cells_j; j++)
+    {
+      XTx[0][j] =  4*x[0][j] + -5*x[2][j] +  1*x[4][j];
+      XTx[1][j] = -4*x[1][j] + -4*x[2][j] +  1*x[3][j] +  1*x[4][j];
+      XTx[2][j] =  4*x[1][j] + -4*x[2][j] + -1*x[3][j] +  1*x[4][j];
+      XTx[3][j] = -2*x[1][j] + -1*x[2][j] +  2*x[3][j] +  1*x[4][j];
+      XTx[4][j] =  2*x[1][j] + -1*x[2][j] + -2*x[3][j] +  1*x[4][j];
+      XTx[5][j] =  4*x[1][j] + -5*x[3][j] +  1*x[5][j];
+    }
+
+    // Compute U = XT . x . X
+    for (int i = 0; i < 6; i++)
+    {
+      U[i][0] =  4*XTx[i][0] + -5*XTx[i][2] +  1*XTx[i][4];
+      U[i][1] = -4*XTx[i][1] + -4*XTx[i][2] +  1*XTx[i][3] +  1*XTx[i][4];
+      U[i][2] =  4*XTx[i][1] + -4*XTx[i][2] + -1*XTx[i][3] +  1*XTx[i][4];
+      U[i][3] = -2*XTx[i][1] + -1*XTx[i][2] +  2*XTx[i][3] +  1*XTx[i][4];
+      U[i][4] =  2*XTx[i][1] + -1*XTx[i][2] + -2*XTx[i][3] +  1*XTx[i][4];
+      U[i][5] =  4*XTx[i][1] + -5*XTx[i][3] +  1*XTx[i][5];
+    }
+
+    // Store the transformed matrix
+    for (int i = 0, m = 0; i < 6; i++)
+    {
+      for (int j = 0; j < 6; j++, m++)
+      {
+        *(outptr + m*matrix_stride) = U[i][j];
+      }
+    }
+    outptr++;
+  }
+}
+
+template <>
+template <>
+const Transform::TileFn Transform::tile_fns[2][2][max_pad_bottom][max_pad_right] =
+{
+  {
+    {
+      {
+        Transform::template process_tile<0, 0, 0, 0>,  // No padding
+        Transform::template process_tile<0, 0, 0, 1>,  // Right
+        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, 1, 0>,  // Bottom
+        Transform::template process_tile<0, 0, 1, 1>,  // Bottom right
+        Transform::template process_tile<0, 0, 1, 2>,  // "          "
+        Transform::template process_tile<0, 0, 1, 3>,  // "          "
+        Transform::template process_tile<0, 0, 1, 4>,  // "          "
+      },
+      {
+        Transform::template process_tile<0, 0, 2, 0>,  // Bottom
+        Transform::template process_tile<0, 0, 2, 1>,  // Bottom right
+        Transform::template process_tile<0, 0, 2, 2>,  // "          "
+        Transform::template process_tile<0, 0, 2, 3>,  // "          "
+        Transform::template process_tile<0, 0, 2, 4>,  // "          "
+      },
+      {
+        Transform::template process_tile<0, 0, 3, 0>,  // Bottom
+        Transform::template process_tile<0, 0, 3, 1>,  // Bottom right
+        Transform::template process_tile<0, 0, 3, 2>,  // "          "
+        Transform::template process_tile<0, 0, 3, 3>,  // "          "
+        Transform::template process_tile<0, 0, 3, 4>,  // "          "
+      },
+      {
+        Transform::template process_tile<0, 0, 4, 0>,  // Bottom
+        Transform::template process_tile<0, 0, 4, 1>,  // Bottom right
+        Transform::template process_tile<0, 0, 4, 2>,  // "          "
+        Transform::template process_tile<0, 0, 4, 3>,  // "          "
+        Transform::template process_tile<0, 0, 4, 4>,  // "          "
+      }
+    },
+    {
+      {
+        Transform::template process_tile<0, 1, 0, 0>,  // Left
+        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, 1, 0>,  // Bottom left
+        Transform::template process_tile<0, 1, 1, 1>,
+        Transform::template process_tile<0, 1, 1, 2>,
+        Transform::template process_tile<0, 1, 1, 3>,
+        Transform::template process_tile<0, 1, 1, 4>,
+      },
+      {
+        Transform::template process_tile<0, 1, 2, 0>,  // "          "
+        Transform::template process_tile<0, 1, 2, 1>,
+        Transform::template process_tile<0, 1, 2, 2>,
+        Transform::template process_tile<0, 1, 2, 3>,
+        Transform::template process_tile<0, 1, 2, 4>,
+      },
+      {
+        Transform::template process_tile<0, 1, 3, 0>,  // "          "
+        Transform::template process_tile<0, 1, 3, 1>,
+        Transform::template process_tile<0, 1, 3, 2>,
+        Transform::template process_tile<0, 1, 3, 3>,
+        Transform::template process_tile<0, 1, 3, 4>,
+      },
+      {
+        Transform::template process_tile<0, 1, 4, 0>,  // "          "
+        Transform::template process_tile<0, 1, 4, 1>,
+        Transform::template process_tile<0, 1, 4, 2>,
+        Transform::template process_tile<0, 1, 4, 3>,
+        Transform::template process_tile<0, 1, 4, 4>,
+      }
+    }
+  },
+  {
+    {
+      {
+        Transform::template process_tile<1, 0, 0, 0>,  // Top
+        Transform::template process_tile<1, 0, 0, 1>,  // Top right
+        Transform::template process_tile<1, 0, 0, 2>,  // "       "
+        Transform::template process_tile<1, 0, 0, 3>,  // "       "
+        Transform::template process_tile<1, 0, 0, 4>,  // "       "
+      },
+      {
+        Transform::template process_tile<1, 0, 1, 0>,
+        Transform::template process_tile<1, 0, 1, 1>,
+        Transform::template process_tile<1, 0, 1, 2>,
+        Transform::template process_tile<1, 0, 1, 3>,
+        Transform::template process_tile<1, 0, 1, 4>,
+      },
+      {
+        Transform::template process_tile<1, 0, 2, 0>,
+        Transform::template process_tile<1, 0, 2, 1>,
+        Transform::template process_tile<1, 0, 2, 2>,
+        Transform::template process_tile<1, 0, 2, 3>,
+        Transform::template process_tile<1, 0, 2, 4>,
+      },
+      {
+        Transform::template process_tile<1, 0, 3, 0>,
+        Transform::template process_tile<1, 0, 3, 1>,
+        Transform::template process_tile<1, 0, 3, 2>,
+        Transform::template process_tile<1, 0, 3, 3>,
+        Transform::template process_tile<1, 0, 3, 4>,
+      },
+      {
+        Transform::template process_tile<1, 0, 4, 0>,
+        Transform::template process_tile<1, 0, 4, 1>,
+        Transform::template process_tile<1, 0, 4, 2>,
+        Transform::template process_tile<1, 0, 4, 3>,
+        Transform::template process_tile<1, 0, 4, 4>,
+      },
+    },
+    {
+      {
+        Transform::template process_tile<1, 1, 0, 0>,  // Top left
+        Transform::template process_tile<1, 1, 0, 1>,
+        Transform::template process_tile<1, 1, 0, 2>,
+        Transform::template process_tile<1, 1, 0, 3>,
+        Transform::template process_tile<1, 1, 0, 4>,
+      },
+      {
+        Transform::template process_tile<1, 1, 1, 0>,
+        Transform::template process_tile<1, 1, 1, 1>,
+        Transform::template process_tile<1, 1, 1, 2>,
+        Transform::template process_tile<1, 1, 1, 3>,
+        Transform::template process_tile<1, 1, 1, 4>,
+      },
+      {
+        Transform::template process_tile<1, 1, 2, 0>,
+        Transform::template process_tile<1, 1, 2, 1>,
+        Transform::template process_tile<1, 1, 2, 2>,
+        Transform::template process_tile<1, 1, 2, 3>,
+        Transform::template process_tile<1, 1, 2, 4>,
+      },
+      {
+        Transform::template process_tile<1, 1, 3, 0>,
+        Transform::template process_tile<1, 1, 3, 1>,
+        Transform::template process_tile<1, 1, 3, 2>,
+        Transform::template process_tile<1, 1, 3, 3>,
+        Transform::template process_tile<1, 1, 3, 4>,
+      },
+      {
+        Transform::template process_tile<1, 1, 4, 0>,
+        Transform::template process_tile<1, 1, 4, 1>,
+        Transform::template process_tile<1, 1, 4, 2>,
+        Transform::template process_tile<1, 1, 4, 3>,
+        Transform::template process_tile<1, 1, 4, 4>,
+      }
+    }
+  }
+};
+
+template struct WinogradGEMM<2, 2, 5, 5>::InputTransform<float>;
+}  // namespace winograd
diff --git a/src/core/NEON/kernels/winograd/transforms/output_2x2_3x3_fp32.cpp b/src/core/NEON/kernels/winograd/transforms/output_2x2_3x3_fp32.cpp
index e7907d18c0..58db7d2ecd 100644
--- a/src/core/NEON/kernels/winograd/transforms/output_2x2_3x3_fp32.cpp
+++ b/src/core/NEON/kernels/winograd/transforms/output_2x2_3x3_fp32.cpp
@@ -65,6 +65,7 @@ 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
@@ -83,6 +84,7 @@ void Transform::process_tile(
     }
   }
   const float *inptr = matrix_base;
+  const float *bptr = biases;
 
   // For each channel of the output
   int channels_remaining = n_channels;
@@ -90,7 +92,7 @@ void Transform::process_tile(
   for (; channels_remaining >= 4; channels_remaining -= 4)
   {
     // Matrices used and computed during this transform
-    float32x4_t F[4][4], FZ[4][2], f[2][2];
+    float32x4_t F[4][4], FZ[4][2], f[2][2], b;
 
     // Read a 4x4 tile in the Winograd domain
     for (int i = 0, m = 0; i < 4; i++)
@@ -122,12 +124,16 @@ void Transform::process_tile(
       f[1][j] = vsubq_f32(vsubq_f32(FZ[1][j], FZ[2][j]), FZ[3][j]);
     }
 
+    // Load the bias vector
+    b = vld1q_f32(bptr);
+    bptr += 4;
+
     // Write out the output tile
     for (int i = 0; i < cells_i; i++)
     {
       for (int j = 0; j < cells_j; j++)
       {
-        vst1q_f32(outptrs[i][j], f[i][j]);
+        vst1q_f32(outptrs[i][j], vaddq_f32(f[i][j], b));
         outptrs[i][j] += 4;
       }
     }
@@ -137,7 +143,7 @@ void Transform::process_tile(
   for (; channels_remaining >= 2; channels_remaining -= 2)
   {
     // Matrices used and computed during this transform
-    float32x2_t F[4][4], FZ[4][2], f[2][2];
+    float32x2_t F[4][4], FZ[4][2], f[2][2], b;
 
     // Read a 4x4 tile in the Winograd domain
     for (int i = 0, m = 0; i < 4; i++)
@@ -169,12 +175,16 @@ void Transform::process_tile(
       f[1][j] = vsub_f32(vsub_f32(FZ[1][j], FZ[2][j]), FZ[3][j]);
     }
 
+    // Load the bias vector
+    b = vld1_f32(bptr);
+    bptr += 2;
+
     // Write out the output tile
     for (int i = 0; i < cells_i; i++)
     {
       for (int j = 0; j < cells_j; j++)
       {
-        vst1_f32(outptrs[i][j], f[i][j]);
+        vst1_f32(outptrs[i][j], vadd_f32(f[i][j], b));
         outptrs[i][j] += 2;
       }
     }
@@ -183,7 +193,7 @@ void Transform::process_tile(
   for (; channels_remaining; channels_remaining--)
   {
     // Matrices used and computed during this transform
-    float F[4][4], FZ[4][2], f[2][2];
+    float F[4][4], FZ[4][2], f[2][2], b;
 
     // Read a 4x4 tile in the Winograd domain
     for (int i = 0, m = 0; i < 4; i++)
@@ -209,12 +219,15 @@ void Transform::process_tile(
       f[1][j] =  FZ[1][j] - FZ[2][j] - FZ[3][j];
     }
 
+    // Load the bias
+    b = *(bptr++);
+
     // Write out the output tile
     for (int i = 0; i < cells_i; i++)
     {
       for (int j = 0; j < cells_j; j++)
       {
-        *(outptrs[i][j]++) = f[i][j];
+        *(outptrs[i][j]++) = f[i][j] + b;
       }
     }
   }
diff --git a/src/core/NEON/kernels/winograd/transforms/output_2x2_5x5_fp32.cpp b/src/core/NEON/kernels/winograd/transforms/output_2x2_5x5_fp32.cpp
new file mode 100644
index 0000000000..bfd670090a
--- /dev/null
+++ b/src/core/NEON/kernels/winograd/transforms/output_2x2_5x5_fp32.cpp
@@ -0,0 +1,242 @@
+/*
+ * 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 "transforms/output.hpp"
+#include "winograd_gemm.hpp"
+#include "arm.hpp"
+
+namespace winograd
+{
+
+using Transform = WinogradGEMM<2, 2, 5, 5>::OutputTransform<float>;
+
+template <>
+template <>
+int Transform::ops_performed(const Tensor4DShape &shape)
+{
+  return 0;  // TODO
+}
+
+/* F(2x2, 5x5) constructs 2x2 output tiles from a 5x5 convolution. Since we use
+ * enough tiles to cover the output space each output tile may contain 0 or 1
+ * padded values to the right and bottom columns or rows of the tile, e.g.:
+ *
+ *      ___     ___
+ *     |   |   |  X|
+ *     |___|   |__X|
+ *
+ *      ___     ___
+ *     |   |   |  X|
+ *     |X_X|   |X_X|
+ *
+ *
+ * We provide a specialised output transform for each of these instances.
+ * Consequently we below construct an array of the various padding options, the
+ * array contains pointers to the specific implementations.
+ */
+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
+)
+{
+  constexpr int cells_i = 2 - pad_bottom;
+  constexpr int cells_j = 2 - pad_right;
+
+  // Construct a map to the output cells
+  float *outptrs[cells_i][cells_j];
+  for (int i = 0; i < cells_i; i++)
+  {
+    for (int j = 0; j < cells_j; j++)
+    {
+      outptrs[i][j] = output + i*output_row_stride + 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 __aarch64__
+  for (; channels_remaining >= 4; channels_remaining -= 4)
+  {
+    // Matrices used and computed during this transform
+    float32x4_t F[6][6], FZ[6][2], f[2][2], b;
+
+    // Read a 6x6 tile in the Winograd domain
+    for (int i = 0, m = 0; i < 6; i++)
+    {
+      for (int j = 0; j < 6; j++, m++)
+      {
+        F[i][j] = vld1q_f32(inptr + m*matrix_stride);
+      }
+    }
+    inptr += 4;
+
+    // Compute the matrix F Z
+    for (int i = 0; i < 6; i++)
+    {
+      // FZ[i][0] =  1*F[i][0] +  1*F[i][1] +  1*F[i][2] +  1*F[i][3] +  1*F[i][4];
+      FZ[i][0] = vaddq_f32(vaddq_f32(vaddq_f32(F[i][0], F[i][1]), vaddq_f32(F[i][2], F[i][3])), F[i][4]);
+
+      // FZ[i][1] =               1*F[i][1] + -1*F[i][2] +  2*F[i][3] + -2*F[i][4] +  1*F[i][5];
+      FZ[i][1] = vaddq_f32(vmlaq_n_f32(vsubq_f32(F[i][1], F[i][2]), vsubq_f32(F[i][3], F[i][4]), 2.0f), F[i][5]);
+    }
+
+    // Compute the output tile f = ZT F Z
+    for (int j = 0; j < 2; j++)
+    {
+      // f[0][j] =  1*FZ[0][j] +  1*FZ[1][j] +  1*FZ[2][j] +  1*FZ[3][j] +  1*FZ[4][j];
+      f[0][j] = vaddq_f32(vaddq_f32(vaddq_f32(FZ[0][j], FZ[1][j]), vaddq_f32(FZ[2][j], FZ[3][j])), FZ[4][j]);
+
+      // f[1][j] =               1*FZ[1][j] + -1*FZ[2][j] +  2*FZ[3][j] + -2*FZ[4][j] +  1*FZ[5][j];
+      f[1][j] = vaddq_f32(vmlaq_n_f32(vsubq_f32(FZ[1][j], FZ[2][j]), vsubq_f32(FZ[3][j], FZ[4][j]), 2.0f), FZ[5][j]);
+    }
+
+    // Write out the output tile
+    b = vld1q_f32(bptr);
+    bptr += 4;
+    for (int i = 0; i < cells_i; i++)
+    {
+      for (int j = 0; j < cells_j; j++)
+      {
+        vst1q_f32(outptrs[i][j], vaddq_f32(f[i][j], b));
+        outptrs[i][j] += 4;
+      }
+    }
+  }
+#endif  // __aarch64__
+#ifdef __arm_any__
+  for (; channels_remaining >= 2; channels_remaining -= 2)
+  {
+    // Matrices used and computed during this transform
+    float32x2_t F[6][6], FZ[6][2], f[2][2], b;
+
+    // Read a 6x6 tile in the Winograd domain
+    for (int i = 0, m = 0; i < 6; i++)
+    {
+      for (int j = 0; j < 6; j++, m++)
+      {
+        F[i][j] = vld1_f32(inptr + m*matrix_stride);
+      }
+    }
+    inptr += 2;
+
+    // Compute the matrix F Z
+    for (int i = 0; i < 6; i++)
+    {
+      // FZ[i][0] =  1*F[i][0] +  1*F[i][1] +  1*F[i][2] +  1*F[i][3] +  1*F[i][4];
+      FZ[i][0] = vadd_f32(vadd_f32(vadd_f32(F[i][0], F[i][1]), vadd_f32(F[i][2], F[i][3])), F[i][4]);
+
+      // FZ[i][1] =               1*F[i][1] + -1*F[i][2] +  2*F[i][3] + -2*F[i][4] +  1*F[i][5];
+      FZ[i][1] = vadd_f32(vmla_n_f32(vsub_f32(F[i][1], F[i][2]), vsub_f32(F[i][3], F[i][4]), 2.0f), F[i][5]);
+    }
+
+    // Compute the output tile f = ZT F Z
+    for (int j = 0; j < 2; j++)
+    {
+      // f[0][j] =  1*FZ[0][j] +  1*FZ[1][j] +  1*FZ[2][j] +  1*FZ[3][j] +  1*FZ[4][j];
+      f[0][j] = vadd_f32(vadd_f32(vadd_f32(FZ[0][j], FZ[1][j]), vadd_f32(FZ[2][j], FZ[3][j])), FZ[4][j]);
+
+      // f[1][j] =               1*FZ[1][j] + -1*FZ[2][j] +  2*FZ[3][j] + -2*FZ[4][j] +  1*FZ[5][j];
+      f[1][j] = vadd_f32(vmla_n_f32(vsub_f32(FZ[1][j], FZ[2][j]), vsub_f32(FZ[3][j], FZ[4][j]), 2.0f), FZ[5][j]);
+    }
+
+    // Write out the output tile
+    b = vld1_f32(bptr);
+    bptr += 2;
+    for (int i = 0; i < cells_i; i++)
+    {
+      for (int j = 0; j < cells_j; j++)
+      {
+        vst1_f32(outptrs[i][j], vadd_f32(f[i][j], b));
+        outptrs[i][j] += 2;
+      }
+    }
+  }
+#endif  // __arm_any__
+  for (; channels_remaining; channels_remaining--)
+  {
+    // Matrices used and computed during this transform
+    float F[6][6], FZ[6][2], f[2][2], b;
+
+    // Read a 6x6 tile in the Winograd domain
+    for (int i = 0, m = 0; i < 6; i++)
+    {
+      for (int j = 0; j < 6; j++, m++)
+      {
+        F[i][j] = *(inptr + m*matrix_stride);
+      }
+    }
+    inptr++;
+
+    // Compute the matrix F Z
+    for (int i = 0; i < 6; i++)
+    {
+      FZ[i][0] =  1*F[i][0] +  1*F[i][1] +  1*F[i][2] +  1*F[i][3] +  1*F[i][4];
+      FZ[i][1] =               1*F[i][1] + -1*F[i][2] +  2*F[i][3] + -2*F[i][4] +  1*F[i][5];
+    }
+
+    // Compute the output tile f = ZT F Z
+    for (int j = 0; j < 2; j++)
+    {
+      f[0][j] =  1*FZ[0][j] +  1*FZ[1][j] +  1*FZ[2][j] +  1*FZ[3][j] +  1*FZ[4][j];
+      f[1][j] =                1*FZ[1][j] + -1*FZ[2][j] +  2*FZ[3][j] + -2*FZ[4][j] +  1*FZ[5][j];
+    }
+
+    // Write out the output tile
+    b = *(bptr++);
+    for (int i = 0; i < cells_i; i++)
+    {
+      for (int j = 0; j < cells_j; j++)
+      {
+        *(outptrs[i][j]++) = f[i][j] + b;
+      }
+    }
+  }
+}
+
+template <>
+template <>
+const Transform::TileFn Transform::tile_fns[max_pad_bottom][max_pad_right] =
+{
+  {
+    Transform::template process_tile<0, 0>,  // No padding
+    Transform::template process_tile<0, 1>,  // Right padding
+  },
+  {
+    Transform::template process_tile<1, 0>,  // Bottom padding
+    Transform::template process_tile<1, 1>,  // Bottom and right padding
+  }
+};
+
+template struct WinogradGEMM<2, 2, 5, 5>::OutputTransform<float>;
+}  // namespace winograd
diff --git a/src/core/NEON/kernels/winograd/transforms/output_4x4_3x3_fp32.cpp b/src/core/NEON/kernels/winograd/transforms/output_4x4_3x3_fp32.cpp
index 483e5c110b..45210d7976 100644
--- a/src/core/NEON/kernels/winograd/transforms/output_4x4_3x3_fp32.cpp
+++ b/src/core/NEON/kernels/winograd/transforms/output_4x4_3x3_fp32.cpp
@@ -82,6 +82,7 @@ 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
@@ -100,6 +101,7 @@ void Transform::process_tile(
     }
   }
   const float *inptr = matrix_base;
+  const float *bptr = biases;
 
   // For each channel of the output
   int channels_remaining = n_channels;
@@ -107,7 +109,7 @@ void Transform::process_tile(
   for (; channels_remaining >= 4; channels_remaining -= 4)
   {
     // Matrices used and computed during this transform
-    float32x4_t F[6][6], FZ[6][4], f[4][4];
+    float32x4_t F[6][6], FZ[6][4], f[4][4], b;
 
     // Read a 6x6 tile in the Winograd domain
     for (int i = 0, m = 0; i < 6; i++)
@@ -152,11 +154,13 @@ void Transform::process_tile(
     }
 
     // Write out the output tile
+    b = vld1q_f32(bptr);
+    bptr += 4;
     for (int i = 0; i < cells_i; i++)
     {
       for (int j = 0; j < cells_j; j++)
       {
-        vst1q_f32(outptrs[i][j], f[i][j]);
+        vst1q_f32(outptrs[i][j], vaddq_f32(f[i][j], b));
         outptrs[i][j] += 4;
       }
     }
@@ -166,7 +170,7 @@ void Transform::process_tile(
   for (; channels_remaining >= 2; channels_remaining -= 2)
   {
     // Matrices used and computed during this transform
-    float32x2_t F[6][6], FZ[6][4], f[4][4];
+    float32x2_t F[6][6], FZ[6][4], f[4][4], b;
 
     // Read a 6x6 tile in the Winograd domain
     for (int i = 0, m = 0; i < 6; i++)
@@ -211,11 +215,13 @@ void Transform::process_tile(
     }
 
     // Write out the output tile
+    b = vld1_f32(bptr);
+    bptr += 2;
     for (int i = 0; i < cells_i; i++)
     {
       for (int j = 0; j < cells_j; j++)
       {
-        vst1_f32(outptrs[i][j], f[i][j]);
+        vst1_f32(outptrs[i][j], vadd_f32(f[i][j], b));
         outptrs[i][j] += 2;
       }
     }
@@ -224,7 +230,7 @@ void Transform::process_tile(
   for (; channels_remaining; channels_remaining--)
   {
     // Matrices used and computed during this transform
-    float F[6][6], FZ[6][4], f[4][4];
+    float F[6][6], FZ[6][4], f[4][4], b;
 
     // Read a 6x6 tile in the Winograd domain
     for (int i = 0, m = 0; i < 6; i++)
@@ -255,11 +261,12 @@ void Transform::process_tile(
     }
 
     // Write out the output tile
+    b = *(bptr++);
     for (int i = 0; i < cells_i; i++)
     {
       for (int j = 0; j < cells_j; j++)
       {
-        *(outptrs[i][j]++) = f[i][j];
+        *(outptrs[i][j]++) = f[i][j] + b;
       }
     }
   }
diff --git a/src/core/NEON/kernels/winograd/transforms/weights_2x2_5x5_fp32.cpp b/src/core/NEON/kernels/winograd/transforms/weights_2x2_5x5_fp32.cpp
new file mode 100644
index 0000000000..acf6b913f8
--- /dev/null
+++ b/src/core/NEON/kernels/winograd/transforms/weights_2x2_5x5_fp32.cpp
@@ -0,0 +1,408 @@
+/*
+ * 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.hpp"
+#include "winograd_gemm.hpp"
+#include "transforms/kernel.hpp"
+
+namespace winograd
+{
+  template <>
+  template <>
+  void WinogradGEMM<2, 2, 5, 5>::WeightsTransform<float>::execute(
+    const int n_output_channels,
+    const int n_input_channels,
+    const float* const input,
+    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 auto weight_row_stride = 5 * weight_col_stride;
+    const float *inptrs[5][5];
+    for (int i = 0; i < 5; i++)
+    {
+      for (int j = 0; j < 5; j++)
+      {
+        inptrs[i][j] = input + i*weight_row_stride + 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;
+#ifdef __aarch64__
+      for (; channels_remaining >= 4; channels_remaining -= 4)
+      {
+        // Matrices used and computed in this kernel
+        float32x4_t w[5][5], Ww[6][5], V[6][6];
+
+        // Read weights
+        for (int i = 0; i < 5; i++)
+        {
+          for (int j = 0; j < 5; j++)
+          {
+            w[i][j] = vld1q_f32(inptrs[i][j]);
+            inptrs[i][j] += 4;
+          }
+        }
+
+        // Compute the matrix W w
+        for (int j = 0; j < 5; j++)
+        {
+          // Ww[0][j] = w[0][j]/4.0f;
+          Ww[0][j] = vmulq_n_f32(w[0][j], 1.0f/4.0f);
+
+          // Ww[1][j] = -( w[0][j] + w[1][j] + w[2][j] + w[3][j] + w[4][j])/6.0f;
+          Ww[1][j] = vmulq_n_f32(
+            vaddq_f32(
+              vaddq_f32(
+                vaddq_f32(w[1][j], w[0][j]),
+                vaddq_f32(w[3][j], w[2][j])
+              ),
+              w[4][j]
+            ),
+            -1.0f/6.0f
+          );
+
+          // Ww[2][j] = +(-w[0][j] + w[1][j] - w[2][j] + w[3][j] - w[4][j])/6.0f;
+          // Ww[2][j] = ((w[1][j] - w[0][j]) + (w[3][j] - w[2][j]) - w[4][j])/6.0f;
+          Ww[2][j] = vmulq_n_f32(
+            vsubq_f32(
+              vaddq_f32(
+                vsubq_f32(w[1][j], w[0][j]),
+                vsubq_f32(w[3][j], w[2][j])
+              ),
+              w[4][j]
+            ),
+            1.0f/6.0f
+          );
+
+          // Ww[3][j] = (w[0][j]/8.0f + w[1][j]/4.0f + w[2][j]/2.0f + w[3][j] + 2*w[4][j])/3.0f;
+          Ww[3][j] = vmulq_n_f32(
+            vmlaq_n_f32(
+              vaddq_f32(
+                vaddq_f32(vmulq_n_f32(w[0][j], 1.0f/8.0f), vmulq_n_f32(w[1][j], 1.0f/4.0f)),
+                vaddq_f32(vmulq_n_f32(w[2][j], 1.0f/2.0f), w[3][j])
+              ),
+              w[4][j], 2.0f
+            ),
+            1.0f/3.0f
+          );
+
+          // Ww[4][j] = (w[0][j]/8.0f - w[1][j]/4.0f + w[2][j]/2.0f - w[3][j] + 2*w[4][j])/3.0f;
+          Ww[4][j] = vmulq_n_f32(
+            vmlaq_n_f32(
+              vaddq_f32(
+                vsubq_f32(vmulq_n_f32(w[0][j], 1.0f/8.0f), vmulq_n_f32(w[1][j], 1.0f/4.0f)),
+                vsubq_f32(vmulq_n_f32(w[2][j], 1.0f/2.0f), w[3][j])
+              ),
+              w[4][j], 2.0f
+            ),
+            1.0f/3.0f
+          );
+
+          // Ww[5][j] = w[4][j];
+          Ww[5][j] = w[4][j];
+        }
+
+        // Compute V = W w WT
+        for (int i = 0; i < 6; i++)
+        {
+          // V[i][0] = Ww[i][0]/4.0f;
+          V[i][0] = vmulq_n_f32(Ww[i][0], 1.0f/4.0f);
+
+          // V[i][1] = -( Ww[i][0] + Ww[i][1] + Ww[i][2] + Ww[i][3] + Ww[i][4])/6.0f;
+          V[i][1] = vmulq_n_f32(
+            vaddq_f32(
+              vaddq_f32(
+                vaddq_f32(Ww[i][1], Ww[i][0]),
+                vaddq_f32(Ww[i][3], Ww[i][2])
+              ),
+              Ww[i][4]
+            ),
+            -1.0f/6.0f
+          );
+
+          // V[i][2] = +(-Ww[i][0] + Ww[i][1] - Ww[i][2] + Ww[i][3] - Ww[i][4])/6.0f;
+          // V[i][2] = ((Ww[i][1] - Ww[i][0]) + (Ww[i][3] - Ww[i][2]) - Ww[i][4])/6.0f;
+          V[i][2] = vmulq_n_f32(
+            vsubq_f32(
+              vaddq_f32(
+                vsubq_f32(Ww[i][1], Ww[i][0]),
+                vsubq_f32(Ww[i][3], Ww[i][2])
+              ),
+              Ww[i][4]
+            ),
+            1.0f/6.0f
+          );
+
+          // V[i][3] = (Ww[i][0]/8.0f + Ww[i][1]/4.0f + Ww[i][2]/2.0f + Ww[i][3] + 2*Ww[i][4])/3.0f;
+          V[i][3] = vmulq_n_f32(
+            vmlaq_n_f32(
+              vaddq_f32(
+                vaddq_f32(vmulq_n_f32(Ww[i][0], 1.0f/8.0f), vmulq_n_f32(Ww[i][1], 1.0f/4.0f)),
+                vaddq_f32(vmulq_n_f32(Ww[i][2], 1.0f/2.0f), Ww[i][3])
+              ),
+              Ww[i][4], 2.0f
+            ),
+            1.0f/3.0f
+          );
+
+          // V[i][4] = (Ww[i][0]/8.0f - Ww[i][1]/4.0f + Ww[i][2]/2.0f - Ww[i][3] + 2*Ww[i][4])/3.0f;
+          V[i][4] = vmulq_n_f32(
+            vmlaq_n_f32(
+              vaddq_f32(
+                vsubq_f32(vmulq_n_f32(Ww[i][0], 1.0f/8.0f), vmulq_n_f32(Ww[i][1], 1.0f/4.0f)),
+                vsubq_f32(vmulq_n_f32(Ww[i][2], 1.0f/2.0f), Ww[i][3])
+              ),
+              Ww[i][4], 2.0f
+            ),
+            1.0f/3.0f
+          );
+
+          // V[i][5] = Ww[i][4];
+          V[i][5] = Ww[i][4];
+        }
+
+        // Store the transformed weights
+        for (int i = 0, m = 0; i < 6; i++)
+        {
+          for (int j = 0; j < 6; j++, m++)
+          {
+            vst1q_f32(outptr + m*matrix_stride, V[i][j]);
+          }
+        }
+        outptr += 4;
+      }
+#endif  // __aarch64__
+#ifdef __arm_any__
+      for (; channels_remaining >= 2; channels_remaining -= 2)
+      {
+        // Matrices used and computed in this kernel
+        float32x2_t w[5][5], Ww[6][5], V[6][6];
+
+        // Read weights
+        for (int i = 0; i < 5; i++)
+        {
+          for (int j = 0; j < 5; j++)
+          {
+            w[i][j] = vld1_f32(inptrs[i][j]);
+            inptrs[i][j] += 2;
+          }
+        }
+
+        // Compute the matrix W w
+        for (int j = 0; j < 5; j++)
+        {
+          // Ww[0][j] = w[0][j]/4.0f;
+          Ww[0][j] = vmul_n_f32(w[0][j], 1.0f/4.0f);
+
+          // Ww[1][j] = -( w[0][j] + w[1][j] + w[2][j] + w[3][j] + w[4][j])/6.0f;
+          Ww[1][j] = vmul_n_f32(
+            vadd_f32(
+              vadd_f32(
+                vadd_f32(w[1][j], w[0][j]),
+                vadd_f32(w[3][j], w[2][j])
+              ),
+              w[4][j]
+            ),
+            -1.0f/6.0f
+          );
+
+          // Ww[2][j] = +(-w[0][j] + w[1][j] - w[2][j] + w[3][j] - w[4][j])/6.0f;
+          // Ww[2][j] = ((w[1][j] - w[0][j]) + (w[3][j] - w[2][j]) - w[4][j])/6.0f;
+          Ww[2][j] = vmul_n_f32(
+            vsub_f32(
+              vadd_f32(
+                vsub_f32(w[1][j], w[0][j]),
+                vsub_f32(w[3][j], w[2][j])
+              ),
+              w[4][j]
+            ),
+            1.0f/6.0f
+          );
+
+          // Ww[3][j] = (w[0][j]/8.0f + w[1][j]/4.0f + w[2][j]/2.0f + w[3][j] + 2*w[4][j])/3.0f;
+          Ww[3][j] = vmul_n_f32(
+            vmla_n_f32(
+              vadd_f32(
+                vadd_f32(vmul_n_f32(w[0][j], 1.0f/8.0f), vmul_n_f32(w[1][j], 1.0f/4.0f)),
+                vadd_f32(vmul_n_f32(w[2][j], 1.0f/2.0f), w[3][j])
+              ),
+              w[4][j], 2.0f
+            ),
+            1.0f/3.0f
+          );
+
+          // Ww[4][j] = (w[0][j]/8.0f - w[1][j]/4.0f + w[2][j]/2.0f - w[3][j] + 2*w[4][j])/3.0f;
+          Ww[4][j] = vmul_n_f32(
+            vmla_n_f32(
+              vadd_f32(
+                vsub_f32(vmul_n_f32(w[0][j], 1.0f/8.0f), vmul_n_f32(w[1][j], 1.0f/4.0f)),
+                vsub_f32(vmul_n_f32(w[2][j], 1.0f/2.0f), w[3][j])
+              ),
+              w[4][j], 2.0f
+            ),
+            1.0f/3.0f
+          );
+
+          // Ww[5][j] = w[4][j];
+          Ww[5][j] = w[4][j];
+        }
+
+        // Compute V = W w WT
+        for (int i = 0; i < 6; i++)
+        {
+          // V[i][0] = Ww[i][0]/4.0f;
+          V[i][0] = vmul_n_f32(Ww[i][0], 1.0f/4.0f);
+
+          // V[i][1] = -( Ww[i][0] + Ww[i][1] + Ww[i][2] + Ww[i][3] + Ww[i][4])/6.0f;
+          V[i][1] = vmul_n_f32(
+            vadd_f32(
+              vadd_f32(
+                vadd_f32(Ww[i][1], Ww[i][0]),
+                vadd_f32(Ww[i][3], Ww[i][2])
+              ),
+              Ww[i][4]
+            ),
+            -1.0f/6.0f
+          );
+
+          // V[i][2] = +(-Ww[i][0] + Ww[i][1] - Ww[i][2] + Ww[i][3] - Ww[i][4])/6.0f;
+          // V[i][2] = ((Ww[i][1] - Ww[i][0]) + (Ww[i][3] - Ww[i][2]) - Ww[i][4])/6.0f;
+          V[i][2] = vmul_n_f32(
+            vsub_f32(
+              vadd_f32(
+                vsub_f32(Ww[i][1], Ww[i][0]),
+                vsub_f32(Ww[i][3], Ww[i][2])
+              ),
+              Ww[i][4]
+            ),
+            1.0f/6.0f
+          );
+
+          // V[i][3] = (Ww[i][0]/8.0f + Ww[i][1]/4.0f + Ww[i][2]/2.0f + Ww[i][3] + 2*Ww[i][4])/3.0f;
+          V[i][3] = vmul_n_f32(
+            vmla_n_f32(
+              vadd_f32(
+                vadd_f32(vmul_n_f32(Ww[i][0], 1.0f/8.0f), vmul_n_f32(Ww[i][1], 1.0f/4.0f)),
+                vadd_f32(vmul_n_f32(Ww[i][2], 1.0f/2.0f), Ww[i][3])
+              ),
+              Ww[i][4], 2.0f
+            ),
+            1.0f/3.0f
+          );
+
+          // V[i][4] = (Ww[i][0]/8.0f - Ww[i][1]/4.0f + Ww[i][2]/2.0f - Ww[i][3] + 2*Ww[i][4])/3.0f;
+          V[i][4] = vmul_n_f32(
+            vmla_n_f32(
+              vadd_f32(
+                vsub_f32(vmul_n_f32(Ww[i][0], 1.0f/8.0f), vmul_n_f32(Ww[i][1], 1.0f/4.0f)),
+                vsub_f32(vmul_n_f32(Ww[i][2], 1.0f/2.0f), Ww[i][3])
+              ),
+              Ww[i][4], 2.0f
+            ),
+            1.0f/3.0f
+          );
+
+          // V[i][5] = Ww[i][4];
+          V[i][5] = Ww[i][4];
+        }
+
+        // Store the transformed weights
+        for (int i = 0, m = 0; i < 6; i++)
+        {
+          for (int j = 0; j < 6; j++, m++)
+          {
+            vst1_f32(outptr + m*matrix_stride, V[i][j]);
+          }
+        }
+        outptr += 2;
+      }
+#endif  // __arm_any__
+      for (; channels_remaining; channels_remaining--)
+      {
+        // Matrices used and computed in this kernel
+        float w[5][5], Ww[6][5], V[6][6];
+
+        // Read weights
+        for (int i = 0; i < 5; i++)
+        {
+          for (int j = 0; j < 5; j++)
+          {
+            w[i][j] = *(inptrs[i][j]++);
+          }
+        }
+
+        // Compute the matrix W w
+        for (int j = 0; j < 5; j++)
+        {
+          Ww[0][j] = w[0][j]/4.0f;
+          Ww[1][j] = -( w[0][j] + w[1][j] + w[2][j] + w[3][j] + w[4][j])/6.0f;
+          Ww[2][j] = +(-w[0][j] + w[1][j] - w[2][j] + w[3][j] - w[4][j])/6.0f;
+          Ww[3][j] = (w[0][j]/8.0f + w[1][j]/4.0f + w[2][j]/2.0f + w[3][j] + 2*w[4][j])/3.0f;
+          Ww[4][j] = (w[0][j]/8.0f - w[1][j]/4.0f + w[2][j]/2.0f - w[3][j] + 2*w[4][j])/3.0f;
+          Ww[5][j] = w[4][j];
+        }
+
+        // Compute V = W w WT
+        for (int i = 0; i < 6; i++)
+        {
+          V[i][0] = Ww[i][0]/4.0f;
+          V[i][1] = -( Ww[i][0] + Ww[i][1] + Ww[i][2] + Ww[i][3] + Ww[i][4])/6.0f;
+          V[i][2] = +(-Ww[i][0] + Ww[i][1] - Ww[i][2] + Ww[i][3] - Ww[i][4])/6.0f;
+          V[i][3] = (Ww[i][0]/8.0f + Ww[i][1]/4.0f + Ww[i][2]/2.0f + Ww[i][3] + 2*Ww[i][4])/3.0f;
+          V[i][4] = (Ww[i][0]/8.0f - Ww[i][1]/4.0f + Ww[i][2]/2.0f - Ww[i][3] + 2*Ww[i][4])/3.0f;
+          V[i][5] = Ww[i][4];
+        }
+
+        // Store the transformed weights
+        for (int i = 0, m = 0; i < 6; i++)
+        {
+          for (int j = 0; j < 6; j++, m++)
+          {
+            *(outptr + m*matrix_stride) = V[i][j];
+          }
+        }
+        outptr++;
+      }
+    }
+  }
+
+  template <>
+  template <>
+  int WinogradGEMM<2, 2, 5, 5>::WeightsTransform<float>::ops_performed(const KernelShape &shape)
+  {
+    return 0;  // TODO
+  }
+
+  template class WinogradGEMM<2, 2, 5, 5>::WeightsTransform<float>;
+}  // namespace winograd
diff --git a/src/core/NEON/kernels/winograd/winograd_gemm.cpp b/src/core/NEON/kernels/winograd/winograd_gemm.cpp
index b44a45367f..fcfa635232 100644
--- a/src/core/NEON/kernels/winograd/winograd_gemm.cpp
+++ b/src/core/NEON/kernels/winograd/winograd_gemm.cpp
@@ -372,6 +372,7 @@ void WinogradGEMM<output_tile_rows, output_tile_cols, kernel_rows, kernel_cols>:
 Convolution<TOut, TIn>::execute(
   TOut* const output,
   const TIn* const input,
+  const TOut* const biases,
   void *working_space,
   const int n_threads
 )
@@ -479,7 +480,11 @@ Convolution<TOut, TIn>::execute(
     kernel_matrices[0],
     output_matrices[0]
   );
-  gemms.run(0, gemms.get_window());
+  for (unsigned int i = 0; i < gemms.get_window(); i++)
+  {
+    auto run_gemm = [&] () { gemms.run(i, i+1); };
+    prof("GEMM", run_gemm, 0, 0, 0);
+  }
 
   // If the output tensor needs to be in NCHW form then store the NHWC output
   // tensor in temporary storage and then reorder. If the output tensor needs
@@ -498,6 +503,7 @@ Convolution<TOut, TIn>::execute(
     output_matrices[0],
     out_matrix_stride_bytes / sizeof(TOut),
     out_matrix_row_stride,
+    biases,
     output_nhwc,
     output_shape.n_batches,
     output_shape.n_rows,
@@ -548,13 +554,16 @@ void WinogradGEMM<output_tile_rows, output_tile_cols, kernel_rows, kernel_cols>:
 Convolution<TOut, TIn>::execute(
   TOut* const output,
   const TIn* const input,
+  const TOut* const biases,
   const int n_threads
 )
 {
-  execute(output, input, NULL, n_threads);
+  execute(output, input, biases, NULL, n_threads);
 }
 
 
 // Instantiate required implementations
 template class WinogradGEMM<2, 2, 3, 3>::Convolution<float, float>;
 template class WinogradGEMM<4, 4, 3, 3>::Convolution<float, float>;
+
+template class WinogradGEMM<2, 2, 5, 5>::Convolution<float, float>;
diff --git a/src/core/NEON/kernels/winograd/winograd_layer.cpp b/src/core/NEON/kernels/winograd/winograd_layer.cpp
index 689ecba5fb..f16d62c0ef 100644
--- a/src/core/NEON/kernels/winograd/winograd_layer.cpp
+++ b/src/core/NEON/kernels/winograd/winograd_layer.cpp
@@ -157,6 +157,7 @@ WinogradConvolutionLayer(
   TIn* const winograd_weights,  /** Pointer to storage for weight tensor in the Winograd domain. Must be at least the size returned by `get_weight_storage_size`. */
   const TIn* const input,       /** Pointer to NHWC ordered input tensor, in the spatial domain. */
   TIn* const winograd_input,    /** Pointer to working space for the input tensor in the Winograd domain. Must be at least the size returned by `get_input_storage_size`. */
+  const TOut* const biases,     /** Pointer to biases vector. */
   TOut* const output,           /** Pointer to NHWC ordered output tensor, in the spatial domain. */
   TOut* const winograd_output   /** Pointer to working space for the output tensor in the Winograd domain. Must be at least the size returned by `get_output_storage_size`. */
 ) : _kernel_shape(n_output_channels, KernelRows, KernelCols, n_input_channels),
@@ -193,7 +194,7 @@ WinogradConvolutionLayer(
       winograd_input, winograd_weights, winograd_output
     ),
     output_transform(
-      winograd_output, _output_matrix_stride, _output_matrix_row_stride,
+      winograd_output, _output_matrix_stride, _output_matrix_row_stride, biases,
       output, n_batches, _n_output_rows, _n_output_cols, n_output_channels
     )
 {
@@ -202,3 +203,4 @@ WinogradConvolutionLayer(
 // Instantiate valid implementations.
 template class WinogradConvolutionLayer<2, 2, 3, 3, float, float>;
 template class WinogradConvolutionLayer<4, 4, 3, 3, float, float>;
+template class WinogradConvolutionLayer<2, 2, 5, 5, float, float>;
diff --git a/src/runtime/NEON/functions/NEWinogradLayer.cpp b/src/runtime/NEON/functions/NEWinogradLayer.cpp
index cb1d8b5a48..e8c77412a2 100644
--- a/src/runtime/NEON/functions/NEWinogradLayer.cpp
+++ b/src/runtime/NEON/functions/NEWinogradLayer.cpp
@@ -28,6 +28,8 @@
 #include "arm_compute/runtime/NEON/NEScheduler.h"
 #include "support/ToolchainSupport.h"
 
+#include "arm_compute/core/NEON/kernels/winograd/winograd_gemm.hpp"
+
 namespace
 {
 inline Tensor4DShape internal_get_input_shape(const arm_compute::ITensor *input)
@@ -43,15 +45,15 @@ inline Tensor4DShape internal_get_input_shape(const arm_compute::ITensor *input)
 namespace arm_compute
 {
 NEWinogradLayer::NEWinogradLayer(std::shared_ptr<IMemoryManager> memory_manager)
-    : _memory_group(std::move(memory_manager)), _winograd_kernel(), _permute_input(), _permute_weights(), _permute_output(), _input_workspace(), _output_workspace(), _kernel_storage(), _input_nhwc(),
-      _output_nhwc(), _weights_hwio(), _input(), _weights(), _output(), _reshaped_kernel(false), _conv()
+    : _memory_group(std::move(memory_manager)), _winograd_kernel(), _transform_input_kernel(), _transform_output_kernel(), _transform_weights_kernel(), _permute_input(), _permute_weights(),
+      _permute_output(), _input_workspace(), _output_workspace(), _kernel_storage(), _input_nhwc(), _output_nhwc(), _weights_hwio(), _input(), _weights(), _output(), _reshaped_kernel(false), _conv()
 {
 } /* arm_compute */
 
 void NEWinogradLayer::configure(const ITensor *input, const ITensor *weights, const ITensor *biases, ITensor *output, const PadStrideInfo &conv_info)
 {
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32);
-    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+    ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights, biases);
     ARM_COMPUTE_ERROR_ON_MSG(weights->info()->dimension(1) != 3 || weights->info()->dimension(0) != 3, "Only 3x3 kernels are supported");
     ARM_COMPUTE_ERROR_ON(weights->info()->num_dimensions() > 4);
 
@@ -76,22 +78,22 @@ void NEWinogradLayer::configure(const ITensor *input, const ITensor *weights, co
     const int out_channels = output->info()->dimension(2);
 
     const Tensor4DShape in_shape(internal_get_input_shape(input));
-
+    const size_t        data_type_size = input->info()->element_size();
     // Get the memory required to instantiate a new Winograd operator.
     constexpr size_t storage_alignment   = 64;
-    const size_t     kernel_storage_size = NEWinogradLayerKernel::get_weight_storage_size(out_channels, in_channels) * sizeof(float);
+    const size_t     kernel_storage_size = NEWinogradLayerKernel::get_weight_storage_size(out_channels, in_channels) * data_type_size;
     _kernel_storage.allocator()->init(TensorInfo(TensorShape{ (kernel_storage_size + storage_alignment - 1) }, 1, DataType::U8));
     _memory_group.manage(&_kernel_storage);
     _memory_group.manage(&_input_nhwc);
     _kernel_storage.allocator()->allocate();
     // Input storage
-    const size_t input_storage_size = NEWinogradLayerKernel::get_input_storage_size(in_shape.n_batches, in_shape.n_channels, in_shape.n_rows, in_shape.n_cols, false) * sizeof(float);
+    const size_t input_storage_size = NEWinogradLayerKernel::get_input_storage_size(in_shape.n_batches, in_shape.n_channels, in_shape.n_rows, in_shape.n_cols, false) * data_type_size;
     _input_workspace.allocator()->init(TensorInfo(TensorShape{ (input_storage_size + storage_alignment - 1) }, 1, DataType::U8));
     _memory_group.manage(&_input_workspace);
     _input_workspace.allocator()->allocate();
 
     // Output storage
-    const size_t output_storage_size = NEWinogradLayerKernel::get_output_storage_size(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels, false) * sizeof(float);
+    const size_t output_storage_size = NEWinogradLayerKernel::get_output_storage_size(in_shape.n_batches, in_shape.n_rows, in_shape.n_cols, out_channels, false) * data_type_size;
     _output_workspace.allocator()->init(TensorInfo(TensorShape{ (output_storage_size + storage_alignment - 1) }, 1, DataType::U8));
     _memory_group.manage(&_output_workspace);
     _output_workspace.allocator()->allocate();
@@ -130,7 +132,6 @@ void NEWinogradLayer::configure(const ITensor *input, const ITensor *weights, co
     _permute_input.configure(input, &_input_nhwc, PermutationVector(2U, 0U, 1U));
 
     _input_nhwc.allocator()->allocate();
-
     // Create Winograd operator object
     _conv = support::cpp14::make_unique<Winograd3x3F32>(
                 in_shape.n_batches,
@@ -148,6 +149,20 @@ void NEWinogradLayer::configure(const ITensor *input, const ITensor *weights, co
 
     // Configure the kernel, padding not needed so it's safe to call configure after allocare
     _winograd_kernel.configure(_conv.get());
+    _transform_input_kernel.configure(_conv.get());
+    _transform_weights_kernel.configure(_conv.get());
+
+    //The biases tensor has not been allocated at this point in time, the output transform will add the biases to the final result in the run() method
+    using T                          = winograd::WinogradGEMM<2, 2, 3, 3>::Convolution<float, float>;
+    const int         weights_width  = weights->info()->dimension(0);
+    const int         weights_height = weights->info()->dimension(1);
+    const KernelShape kernel_shape({ out_channels, weights_height, weights_width, in_channels });
+    const int         output_matrix_stride = T::get_output_matrix_stride(kernel_shape, in_shape, PADDING_VALID);
+    const auto        output_shape(T::get_output_shape(kernel_shape, in_shape, PADDING_VALID));
+
+    _transform_output_kernel.configure(biases, reinterpret_cast<float *>(_output_workspace.buffer()),
+                                       output_matrix_stride, reinterpret_cast<float *>(_output_nhwc.buffer()),
+                                       in_shape.n_batches, output_shape.n_rows, output_shape.n_cols, out_channels);
 
     // Reorder the convoluted output to ACL's ordering NCHW
     _permute_output.configure(&_output_nhwc, _output, PermutationVector(1U, 2U, 0U));
@@ -160,16 +175,20 @@ void NEWinogradLayer::run()
     {
         _reshaped_kernel = true;
         _permute_weights.run();
-        _conv->transform_weights();
+        NEScheduler::get().schedule(&_transform_weights_kernel, Window::DimX);
     }
+
     //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
-    _conv->transform_input();
+    NEScheduler::get().schedule(&_transform_input_kernel, Window::DimX);
+
     //Run 16 GEMMs in multiple threads, each kernel runs one or more GEMMs
     NEScheduler::get().schedule(&_winograd_kernel, Window::DimX);
+
     // Transform output tensor to the spatial domain
-    _conv->transform_output();
+    NEScheduler::get().schedule(&_transform_output_kernel, Window::DimX);
+
     // Reorder the convoluted output to ACL's ordering NCHW
     _permute_output.run();
     _memory_group.release();