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diff --git a/arm_compute/core/NEON/kernels/convolution/winograd/transforms/input.hpp b/arm_compute/core/NEON/kernels/convolution/winograd/transforms/input.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
+{
+  /***************************************************************************/
+  /* Instance-less API */
+  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>::InputTransform<T>::execute(
+    const T *inptr,
+    const Tensor4DShape& input_shape,
+    const PaddingType padding_type,
+    const int tile_M,
+    const int tile_N,
+    T *outptr_base,
+    const int matrix_stride,
+    const int matrix_batch_stride,
+    const int matrix_row_stride
+  )
+  {
+    // Compute the padding required on each edge of the image
+    const bool base_padding = (padding_type == PADDING_SAME) ? 1 : 0;
+    const int pad_top = base_padding;
+    const int pad_left = base_padding;
+    const int tile_overlap = kernel_rows - 1;
+
+    // Compute striding values (assuming NHWC ordered data)
+    const int input_col_stride = input_shape.n_channels;
+    const int input_row_stride = input_shape.n_cols * input_col_stride;
+    const int input_batch_stride = input_shape.n_rows * input_row_stride;
+    const int output_col_stride = matrix_row_stride;
+    const int output_row_stride = tile_N * output_col_stride;
+
+    // Loop over batches
+    for (int batch = 0; batch < input_shape.n_batches; batch++)
+    {
+      // Pointer to the batch
+      const T* const input_base_batch = inptr + batch * input_batch_stride;
+      T* const outptr_base_batch = outptr_base + batch * matrix_batch_stride;
+
+      // Loop over rows of tiles
+      for (int tile_i = 0; tile_i < tile_M; tile_i++)
+      {
+        // Pointer to the row
+        const int row_offset = (tile_i == 0) ?
+          0 : ((padding_type == PADDING_VALID) ? 0 : 1);
+        const T* const input_base_row = (
+          input_base_batch + ((inner_tile_rows - (kernel_rows - 1))*tile_i - row_offset)*input_row_stride
+        );
+        T* const outptr_base_row = outptr_base_batch + tile_i*output_row_stride;
+
+        // Padding (top + bottom) for the row
+        const int row_top = tile_i*(inner_tile_rows - tile_overlap) - pad_top;
+        const int row_bottom = row_top + inner_tile_rows;
+        const int row_pad_top = (tile_i == 0) ? pad_top : 0;
+        const int row_pad_bottom = (row_bottom <= input_shape.n_rows) ? 0 : row_bottom - input_shape.n_rows;
+
+        // Process the row
+        process_tile_row(
+          tile_N, input_shape.n_channels,
+          input_base_row, input_row_stride, input_col_stride,
+          outptr_base_row, matrix_stride, matrix_row_stride,
+          row_pad_top, pad_left, row_pad_bottom, input_shape.n_cols
+        );
+      }
+    }
+  }
+
+  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>::InputTransform<T>::process_tile_row(
+    const int tile_N,
+    int n_channels,
+    const T* const input_base,
+    const int input_row_stride,
+    const int input_col_stride,
+    T* const matrix_base,
+    const int matrix_stride,
+    const int matrix_row_stride,
+    const int pad_top,
+    const int row_pad_left,
+    const int pad_bottom,
+    const int n_cols
+  )
+  {
+    constexpr int tile_overlap = kernel_cols - 1;
+
+    // Loop over columns of tiles
+    for (int tile_j = 0; tile_j < tile_N; tile_j++)
+    {
+      // Padding (left + right) for the tile
+      const int t_pad_left = (tile_j == 0) ? row_pad_left : 0;
+      const int t_start = tile_j*(inner_tile_cols - tile_overlap) - row_pad_left;
+      const int t_end = t_start + inner_tile_cols;
+      const int t_pad_right = (t_end <= n_cols) ? 0 : t_end - n_cols;
+
+      // Get pointers into the inputs and outputs
+      const int col_offset = (tile_j == 0) ? 0 : row_pad_left;
+      const T* const input_base_col = (
+        input_base + ((inner_tile_cols - tile_overlap)*tile_j - col_offset)*input_col_stride
+      );
+      T* const outptr = matrix_base + tile_j*matrix_row_stride;
+
+      // Apply the specific tile processing function
+      tile_fns[pad_top][t_pad_left][pad_bottom][t_pad_right](
+        n_channels,
+        input_base_col,
+        input_row_stride,
+        input_col_stride,
+        outptr,
+        matrix_stride
+      );
+    }
+  }
+
+  /***************************************************************************/
+  template <int otr, int otc, int kr, int kc>
+  template <typename T>
+  WinogradGEMM<otr, otc, kr, kc>::InputTransform<T>::InputTransform(
+    const T* const input,        /** Input tensor data */
+    const int n_batches,         /** Number of batches in input tensor. */
+    const int n_rows,            /** Number of rows in input tensor. */
+    const int n_cols,            /** Number of columns in input tensor. */
+    const int n_channels,        /** Number of channels in input tensor. */
+    const PaddingType padding,   /** Padding type. */
+    T* const output,             /** Base of output matrices. */
+    const int matrix_stride,     /** Stride between output matrices. */
+    const int matrix_row_stride  /** Stride within matrices. */
+  ) : _inptr(input), _outptr(output),
+      _n_batches(n_batches), _n_rows(n_rows), _n_cols(n_cols), _n_channels(n_channels),
+      _matrix_stride(matrix_stride), _matrix_row_stride(matrix_row_stride),
+      _tiles_M(iceildiv((padding == PADDING_SAME) ? n_rows : n_rows - 2, output_tile_rows)),
+      _tiles_N(iceildiv((padding == PADDING_SAME) ? n_cols : n_cols - 2, output_tile_cols)),
+      _padding_type(padding)
+  {
+  }
+
+  template <int otr, int otc, int kr, int kc>
+  template <typename T>
+  unsigned int WinogradGEMM<otr, otc, kr, kc>::InputTransform<T>::get_window() const
+  {
+    // TODO When the input 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 _tiles_M * _n_batches;
+  }
+
+  template <int otr, int otc, int kr, int kc>
+  template <typename T>
+  void WinogradGEMM<otr, otc, kr, kc>::InputTransform<T>::run(
+    const unsigned int start, const unsigned int stop
+  )
+  {
+    // TODO When the input 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 input_shape = {
+      _n_batches, _n_rows, _n_cols, _n_channels, NHWC
+    };
+    execute(
+      _inptr, input_shape, _padding_type, _tiles_M, _tiles_N, _outptr,
+      _matrix_stride, _matrix_row_stride * _tiles_M * _tiles_N, _matrix_row_stride
+    );
+  }
+}