diff --git a/arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h b/arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h
index 7115710..828e2e5 100644
--- a/arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h
+++ b/arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h
@@ -49,8 +49,7 @@
     /** Set the input and output tensor.
      *
      * @note Winograd filter transform supports the following configurations:
-     *       Output tile size: 2x2, 4x4
-     *       Kernel size: 3x3
+     *       F(output tile, kernel size):F(2x2, 3x3), F(4x4, 3x3), F(4x4, 5x5)
      *       Strides: only unit strides
      *
      * @param[in]  input         Source tensor. The input is a 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] (NCHW data layout). Data types supported: F32.
@@ -61,8 +60,7 @@
     /** Static function to check if given info will lead to a valid configuration of @ref CLWinogradFilterTransformKernel
      *
      * @note Winograd filter transform supports the following configurations:
-     *       Output tile size: 2x2, 4x4
-     *       Kernel size: 3x3
+     *       F(output tile, kernel size):F(2x2, 3x3), F(4x4, 3x3), F(4x4, 5x5)
      *       Strides: only unit strides
      *
      * @param[in]  input         Source tensor. The input is a 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM] (NCHW data layout). Data types supported: F32.
diff --git a/arm_compute/core/CL/kernels/CLWinogradInputTransformKernel.h b/arm_compute/core/CL/kernels/CLWinogradInputTransformKernel.h
index 2d1eadf..b92ff2f 100644
--- a/arm_compute/core/CL/kernels/CLWinogradInputTransformKernel.h
+++ b/arm_compute/core/CL/kernels/CLWinogradInputTransformKernel.h
@@ -47,8 +47,7 @@
     /** Set the input and output of the kernel.
      *
      * @note Winograd input transform supports the following configurations:
-     *       Output tile size: 2x2
-     *       Kernel size: 3x3
+     *       F(output tile, kernel size):F(2x2, 3x3), F(4x4, 3x3), F(4x4, 5x5)
      *       Strides: only unit strides
      *
      * @param[in] input         The input tensor to transform. Data types supported: F32
@@ -59,8 +58,7 @@
     /** Static function to check if given info will lead to a valid configuration of @ref CLWinogradInputTransformKernel
      *
      * @note Winograd input transform supports the following configurations:
-     *       Output tile size: 2x2
-     *       Kernel size: 3x3
+     *       F(output tile, kernel size):F(2x2, 3x3), F(4x4, 3x3), F(4x4, 5x5)
      *       Strides: only unit strides
      *
      * @param[in] input         The input tensor to transform. Data types supported: F32
diff --git a/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h b/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h
index b0d0bbe..5e64a82 100644
--- a/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h
+++ b/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h
@@ -49,11 +49,10 @@
     /** Set the input and output tensor.
      *
      * @note Winograd output transform supports the following configurations:
-     *       Output tile size: 2x2
-     *       Kernel size: 3x3
+     *       F(output tile, kernel size):F(2x2, 3x3), F(4x4, 3x3), F(4x4, 5x5)
      *       Strides: only unit strides
      *
-     * @param[in]  input         Source tensor with shape [C, N, 16, batches]. Data types supported: F32.
+     * @param[in]  input         Source tensor with shape [C, N, K, batches]. Data types supported: F32.
      * @param[in]  bias          Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. It can be a nullptr. Data type supported: as @p input
      * @param[out] output        The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_output_transform_shape. Data types supported: Same as @p input
      * @param[in]  winograd_info Contains Winograd's information described in @ref WinogradInfo
@@ -62,11 +61,10 @@
     /** Static function to check if given info will lead to a valid configuration of @ref CLWinogradOutputTransformKernel
      *
      * @note Winograd output transform supports the following configurations:
-     *       Output tile size: 2x2
-     *       Kernel size: 3x3
+     *       F(output tile, kernel size):F(2x2, 3x3), F(4x4, 3x3), F(4x4, 5x5)
      *       Strides: only unit strides
      *
-     * @param[in]  input         Source tensor with shape [C, N, 16, batches]. Data types supported: F32.
+     * @param[in]  input         Source tensor with shape [C, N, K, batches]. Data types supported: F32.
      * @param[in]  bias          Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. It can be a nullptr. Data type supported: as @p input
      * @param[out] output        The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_output_transform_shape. Data types supported: Same as @p input
      * @param[in]  winograd_info Contains Winograd's information described in @ref WinogradInfo
diff --git a/arm_compute/runtime/CL/functions/CLConvolutionLayer.h b/arm_compute/runtime/CL/functions/CLConvolutionLayer.h
index 7df7659..a1cd155 100644
--- a/arm_compute/runtime/CL/functions/CLConvolutionLayer.h
+++ b/arm_compute/runtime/CL/functions/CLConvolutionLayer.h
@@ -26,6 +26,7 @@
 
 #include "arm_compute/runtime/CL/functions/CLDirectConvolutionLayer.h"
 #include "arm_compute/runtime/CL/functions/CLGEMMConvolutionLayer.h"
+#include "arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h"
 #include "arm_compute/runtime/IFunction.h"
 #include "arm_compute/runtime/IMemoryManager.h"
 
@@ -84,7 +85,6 @@
      *                         while every optional dimension from 4 and above represent a batch of inputs.
      *                         Data types supported: QS8/QASYMM8/QS16/F16/F32.
      * @param[in] weights      Weights tensor. Weights are 4D tensor with dimensions [kernel_x, kernel_y, IFM, OFM]. Data type supported:Same as @p input.
-     * @param[in] biases       Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. Data type supported:Same as @p input.
      * @param[in] output       Destination tensor. 3 lower dimensions represent a single output [width, height, OFM], while the rest represent batch of outputs.
      *                         Data types supported: Same as @p input.
      * @param[in] conv_info    Contains padding and stride information described in @ref PadStrideInfo.
@@ -95,7 +95,7 @@
      *
      * @return a status
      */
-    static ConvolutionMethod get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
+    static ConvolutionMethod get_convolution_method(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *output, const PadStrideInfo &conv_info,
                                                     const WeightsInfo &weights_info, const ActivationLayerInfo &act_info, const GPUTarget gpu_target, const Size2D &dilation = Size2D(1U, 1U));
     // Inherited methods overridden:
     void run() override;
diff --git a/arm_compute/runtime/CL/functions/CLWinogradInputTransform.h b/arm_compute/runtime/CL/functions/CLWinogradInputTransform.h
index 0e0d6bf..64d3e80 100644
--- a/arm_compute/runtime/CL/functions/CLWinogradInputTransform.h
+++ b/arm_compute/runtime/CL/functions/CLWinogradInputTransform.h
@@ -40,8 +40,7 @@
     /** Set the input and output tensors.
      *
      * @note Winograd input transform supports the following configurations:
-     *       Output tile size: 2x2
-     *       Kernel size: 3x3
+     *       F(output tile, kernel size):F(2x2, 3x3), F(4x4, 3x3), F(4x4, 5x5)
      *       Strides: only unit strides
      *
      * @param[in] input         The input tensor to transform. Data types supported: F32
@@ -52,8 +51,7 @@
     /**  Static function to check if given info will lead to a valid configuration of @ref CLWinogradInputTransform.
      *
      * @note Winograd input transform supports the following configurations:
-     *       Output tile size: 2x2
-     *       Kernel size: 3x3
+     *       F(output tile, kernel size):F(2x2, 3x3), F(4x4, 3x3), F(4x4, 5x5)
      *       Strides: only unit strides
      *
      * @param[in] input         The input tensor to transform. Data types supported: F32