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diff --git a/arm_compute/core/NEON/kernels/convolution/winograd/transforms/output.hpp b/arm_compute/core/NEON/kernels/convolution/winograd/transforms/output.hpp
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+/*
+ * 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.
+ */
+
+#pragma once
+#include "arm_compute/core/NEON/kernels/convolution/winograd/winograd_gemm.hpp"
+
+namespace winograd
+{
+  template <int output_tile_rows, int output_tile_cols,
+            int kernel_rows, int kernel_cols>
+  template <typename T>
+  void WinogradGEMM<output_tile_rows, output_tile_cols, kernel_rows, kernel_cols>::OutputTransform<T>::execute(
+    const Tensor4DShape &output_shape,
+    const T* const matrix_base,
+    const int matrix_stride,
+    const int matrix_row_stride,
+    const T* const biases,
+    T* const output
+  )
+  {
+    // Compute the number of tiles and hence the padding required on the bottom
+    // and right of the image.
+    const int tile_M = iceildiv(output_shape.n_rows, output_tile_rows);
+    const int tile_N = iceildiv(output_shape.n_cols, output_tile_cols);
+    const int pad_bottom = output_tile_rows*tile_M - output_shape.n_rows;
+    const int pad_right = output_tile_cols*tile_N - output_shape.n_cols;
+
+    const int matrix_tile_row_stride = tile_N * matrix_row_stride;
+    const int matrix_batch_stride = tile_M * matrix_tile_row_stride;
+    const int output_col_stride = output_shape.n_channels;
+    const int output_row_stride = output_shape.n_cols * output_col_stride;
+    const int output_batch_stride = output_shape.n_rows * output_row_stride;
+
+    // Perform the output transformation for each batch
+    for (int batch = 0; batch < output_shape.n_batches; batch++)
+    {
+      // Get batch offset for input and outputs.
+      const T* const matrix_batch = matrix_base + batch*matrix_batch_stride;
+      T* const outptr_batch = output + batch*output_batch_stride;
+
+      // Perform the output transformation for each row of the output tensor.
+      for (int tile_i = 0; tile_i < tile_M; tile_i++)
+      {
+        // Compute properties of this row of output tiles
+        const int row_pad_bottom = (tile_i < tile_M - 1) ? 0: pad_bottom;
+        const T* const matrix_tile_row = matrix_batch + tile_i * matrix_tile_row_stride;
+        T* const outptr_row = outptr_batch + output_tile_rows*tile_i*output_row_stride;
+
+        // Process the row
+        process_tile_row(
+          tile_N, output_shape.n_channels, matrix_tile_row, matrix_stride,
+          matrix_row_stride, biases,
+          outptr_row, output_row_stride, output_col_stride, row_pad_bottom,
+          pad_right
+        );
+      }
+    }
+  }
+
+  template <int output_tile_rows, int output_tile_cols,
+            int kernel_rows, int kernel_cols>
+  template <typename T>
+  void WinogradGEMM<output_tile_rows, output_tile_cols, kernel_rows, kernel_cols>::OutputTransform<T>::process_tile_row(
+    const int tile_N,
+    const int n_channels,
+    const T* const matrix_base,
+    const int matrix_stride,
+    const int matrix_row_stride,
+    const T* const biases,
+    T* const output,
+    const int output_row_stride,
+    const int output_col_stride,
+    const int row_pad_bottom,
+    const int row_pad_right
+  )
+  {
+    // Loop over columns of tiles
+    for (int tile_j = 0; tile_j < tile_N; tile_j++)
+    {
+      // Properties of this tile
+      const int tile_pad_right = (tile_j < tile_N - 1) ? 0 : row_pad_right;
+      const T* const matrix_row = matrix_base + tile_j * matrix_row_stride;
+      T* const outptr = output + output_tile_cols*tile_j*output_col_stride;
+
+      // Perform the output transformation
+      tile_fns[row_pad_bottom][tile_pad_right](
+        n_channels, matrix_row, matrix_stride, biases,
+        outptr, output_row_stride, output_col_stride
+      );
+    }
+  }
+
+  template <int output_tile_rows, int output_tile_cols, int kr, int kc>
+  template <typename T>
+  size_t WinogradGEMM<output_tile_rows, output_tile_cols, kr, kc>::OutputTransform<T>::bytes_read(const Tensor4DShape &shape)
+  {
+    const int M = iceildiv(shape.n_rows, output_tile_rows) *
+                  iceildiv(shape.n_cols, output_tile_cols);
+    const int N = shape.n_channels;
+    return inner_tile_rows * inner_tile_cols * M * N * sizeof(T);
+  }
+
+  template <int otr, int otc, int kr, int kc>
+  template <typename T>
+  size_t WinogradGEMM<otr, otc, kr, kc>::OutputTransform<T>::bytes_written(const Tensor4DShape &shape)
+  {
+    return shape.size() * sizeof(T);
+  }
+
+  template <int output_tile_rows, int output_tile_cols, int kr, int kc>
+  template <typename T>
+  WinogradGEMM<output_tile_rows, output_tile_cols, kr, kc>::OutputTransform<T>::OutputTransform(
+    const T* const matrix_base,
+    const int matrix_stride,
+    const int matrix_row_stride,
+    const T* const biases,
+    T* const output,
+    const int n_batches,
+    const int n_rows,
+    const int n_cols,
+    const int n_channels
+  ) : _matrix_base(matrix_base), _biases(biases),
+      _matrix_stride(matrix_stride), _matrix_row_stride(matrix_row_stride),
+      _outptr(output), _n_batches(n_batches), _n_rows(n_rows), _n_cols(n_cols),
+      _n_channels(n_channels), _tile_M(iceildiv(n_rows, output_tile_rows)),
+      _tile_N(iceildiv(n_cols, output_tile_cols))
+  {
+  }
+
+  template <int otr, int otc, int kr, int kc>
+  template <typename T>
+  unsigned int WinogradGEMM<otr, otc, kr, kc>::OutputTransform<T>::get_window() const
+  {
+    // TODO When the output transform supports multithreading, return the total
+    // number of tile rows (allowing for multiple batches). For now we return 1
+    // to indicate that the activations must be transformed as a single block.
+    return 1;  // TODO _tile_M * _n_batches;
+  }
+
+  template <int otr, int otc, int kr, int kc>
+  template <typename T>
+  void WinogradGEMM<otr, otc, kr, kc>::OutputTransform<T>::run(
+    const unsigned int start, const unsigned int stop
+  )
+  {
+    // TODO When the output transform supports multithreading call execute for a
+    // portion of the tile rows.
+    (void) start;
+    (void) stop;
+
+    // For now, just do all of the work.
+    const Tensor4DShape output_shape = {
+      _n_batches, _n_rows, _n_cols, _n_channels, NHWC
+    };
+    execute(
+      output_shape, _matrix_base, _matrix_stride, _matrix_row_stride, _biases,
+      _outptr
+    );
+  }
+}  // namespace winograd