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diff --git a/src/core/NEON/kernels/convolution/winograd/weight_transform.hpp b/src/core/NEON/kernels/convolution/winograd/weight_transform.hpp
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+++ b/src/core/NEON/kernels/convolution/winograd/weight_transform.hpp
@@ -0,0 +1,145 @@
+/*
+ * Copyright (c) 2022-2023 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 "winograd.hpp"
+#include <algorithm>
+#include <functional>
+
+namespace arm_conv {
+namespace winograd {
+namespace weight_transform {
+
+/* Driver class for the Winograd weight transforms.
+ */
+template <typename TIn, typename TOut=TIn>
+class Transform : public ITransform
+{
+  using Kernel = std::function<void(
+    unsigned int n_channels,  // Number of channels to transform
+    const TIn *inptr, size_t ld_in_row, size_t ld_in_col,
+    TOut *outptr, size_t ld_out_matrix
+  )>;
+
+  const std::string m_name;
+  const unsigned int m_kernel_rows, m_kernel_cols;
+  const unsigned int m_transformed_tile_rows, m_transformed_tile_cols;
+  const Kernel m_kernel;
+
+  void execute_internal(
+    const ConvolutionArgs &args,
+    const TIn *inptr, size_t ld_in_row, size_t ld_in_col, size_t ld_input_channel,
+    TOut *outptr, size_t ld_out_matrix, size_t ld_out_row,
+    unsigned int thread_id, unsigned int n_threads
+  ) const
+  {
+    // Stripe groups of input channels over threads, this should reduce false
+    // sharing of the output matrix.
+    constexpr auto n_input_channels_per_thread = 16u;
+
+    // Get the initial offset for the input and output pointers
+    const auto offset = thread_id * n_input_channels_per_thread;
+    inptr += offset * ld_input_channel;
+    outptr += offset * ld_out_row;
+
+    for (auto start_ic = thread_id * n_input_channels_per_thread;
+         start_ic < args.n_input_channels;
+         start_ic += n_threads * n_input_channels_per_thread)
+    {
+      // Now iterate over the input channels assigned to this thread.
+      const auto end_ic = std::min(args.n_input_channels,
+                                   start_ic + n_input_channels_per_thread);
+      for (auto ic = start_ic; ic < end_ic; ic++)
+      {
+        m_kernel(args.n_output_channels, inptr, ld_in_row, ld_in_col,
+                 outptr, ld_out_matrix);
+        inptr += ld_input_channel;
+        outptr += ld_out_row;
+      }
+
+      // Progress the pointers to the account for the work not performed by
+      // this thread.
+      const auto skip = (n_threads - 1) * n_input_channels_per_thread;
+      inptr += skip * ld_input_channel;
+      outptr += skip * ld_out_row;
+    }
+  }
+
+  public:
+  Transform(
+    const std::string &name,
+    unsigned int kernel_rows, unsigned int kernel_cols,
+    unsigned int transformed_tile_rows, unsigned int transformed_tile_cols,
+    const Kernel kernel
+  )
+  : m_name(name),
+    m_kernel_rows(kernel_rows), m_kernel_cols(kernel_cols),
+    m_transformed_tile_rows(transformed_tile_rows), m_transformed_tile_cols(transformed_tile_cols),
+    m_kernel(kernel)
+  {
+  }
+
+  const std::string &get_name(void) const override { return m_name; }
+
+  unsigned int get_kernel_rows(void) const override { return m_kernel_rows; }
+  unsigned int get_kernel_cols(void) const override { return m_kernel_cols; }
+
+  unsigned int get_transformed_tile_rows(void) const override { return m_transformed_tile_rows; }
+  unsigned int get_transformed_tile_cols(void) const override { return m_transformed_tile_cols; }
+
+  void execute(
+    const ConvolutionArgs &args,
+    const void *inptr, size_t ld_in_row, size_t ld_in_col, size_t ld_input_channel,
+    void *outptr, size_t ld_out_matrix, size_t ld_out_row,
+    unsigned int thread_id, unsigned int n_threads
+  ) const override
+  {
+    execute_internal(
+      args,
+      reinterpret_cast<const TIn *>(inptr), ld_in_row, ld_in_col, ld_input_channel,
+      reinterpret_cast<TOut *>(outptr), ld_out_matrix, ld_out_row,
+      thread_id, n_threads
+    );
+  }
+
+  /* Utility method to get a transposed variant of a kernel, this transposed
+   * version simply calls the original kernel with the input row and column
+   * strides swapped.
+   */
+  static constexpr Kernel get_transposed_kernel(const Kernel &kernel)
+  {
+    return [kernel] (
+      const unsigned int n_channels,
+      const TIn *const inptr, const size_t ld_in_row, const size_t ld_in_col,
+      TOut *const outptr, const size_t ld_out
+    ) {
+      kernel(n_channels, inptr, ld_in_col, ld_in_row, outptr, ld_out);
+    };
+  }
+};
+
+}  // namespace weight_transform
+}  // namespace winograd
+}  // namespace arm_conv