diff --git a/arm_compute/core/CL/CLHelpers.h b/arm_compute/core/CL/CLHelpers.h
index 1054f9a..3b025cc 100644
--- a/arm_compute/core/CL/CLHelpers.h
+++ b/arm_compute/core/CL/CLHelpers.h
@@ -109,5 +109,15 @@
  * @return True if the extension is supported
  */
 bool dot8_supported(const cl::Device &device);
+
+/** This function checks if the Winograd configuration (defined through the output tile, kernel size and the data layout) is supported on OpenCL
+ *
+ * @param[in] output_tile Output tile for the Winograd filtering algorithm
+ * @param[in] kernel_size Kernel size for the Winograd filtering algorithm
+ * @param[in] data_layout Data layout of the input tensor
+ *
+ * @return True if the configuration is supported
+ */
+bool cl_winograd_convolution_layer_supported(const Size2D &output_tile, const Size2D &kernel_size, DataLayout data_layout);
 }
 #endif /* __ARM_COMPUTE_CLHELPERS_H__ */
diff --git a/arm_compute/core/Helpers.h b/arm_compute/core/Helpers.h
index 7d922ae..a3cbfb9 100644
--- a/arm_compute/core/Helpers.h
+++ b/arm_compute/core/Helpers.h
@@ -111,6 +111,28 @@
 };
 }
 
+/** Calculate the number of output tiles required by Winograd Convolution layer. This utility function can be used by the Winograd input transform
+ *  to know the number of tiles on the x and y direction
+ *
+ * @param[in] in_dims          Spatial dimensions of the input tensor of convolution layer
+ * @param[in] kernel_size      Kernel size
+ * @param[in] output_tile_size Size of a single output tile
+ * @param[in] conv_info        Convolution info (i.e. pad, stride,...)
+ *
+ * @return the number of output tiles along the x and y directions of size "output_tile_size"
+ */
+inline Size2D compute_winograd_convolution_tiles(const Size2D &in_dims, const Size2D &kernel_size, const Size2D &output_tile_size, const PadStrideInfo &conv_info)
+{
+    int num_tiles_x = std::ceil((in_dims.width - (kernel_size.width - 1) + conv_info.pad_left() + conv_info.pad_right()) / static_cast<float>(output_tile_size.width));
+    int num_tiles_y = std::ceil((in_dims.height - (kernel_size.height - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / static_cast<float>(output_tile_size.height));
+
+    // Clamp in case we provide paddings but we have 1D convolution
+    num_tiles_x = std::min(num_tiles_x, static_cast<int>(in_dims.width));
+    num_tiles_y = std::min(num_tiles_y, static_cast<int>(in_dims.height));
+
+    return Size2D(num_tiles_x, num_tiles_y);
+}
+
 /** Computes bilinear interpolation using the pointer to the top-left pixel and the pixel's distance between
  * the real coordinates and the smallest following integer coordinates. Input must be in single channel format.
  *
diff --git a/arm_compute/core/utils/misc/ShapeCalculator.h b/arm_compute/core/utils/misc/ShapeCalculator.h
index 115cbe6..2213876 100644
--- a/arm_compute/core/utils/misc/ShapeCalculator.h
+++ b/arm_compute/core/utils/misc/ShapeCalculator.h
@@ -255,12 +255,14 @@
     const size_t idx_h = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::HEIGHT);
     const size_t idx_c = get_data_layout_dimension_index(input.data_layout(), DataLayoutDimension::CHANNEL);
 
-    // Compute height
-    const unsigned int num_tiles_x = std::ceil((input.tensor_shape()[idx_w] - (kernel_size.width - 1) + conv_info.pad_left() + conv_info.pad_right()) / static_cast<float>(output_tile_size.width));
-    const unsigned int num_tiles_y = std::ceil((input.tensor_shape()[idx_h] - (kernel_size.height - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / static_cast<float>(output_tile_size.height));
+    // Compute the number of output tiles along the x and y direction of size "output_tile_size"
+    const Size2D num_tiles = compute_winograd_convolution_tiles(Size2D(input.tensor_shape()[idx_w], input.tensor_shape()[idx_h]),
+                                                                kernel_size,
+                                                                output_tile_size,
+                                                                conv_info);
 
     const unsigned int width  = input.tensor_shape()[idx_c];
-    const unsigned int height = num_tiles_x * num_tiles_y;
+    const unsigned int height = num_tiles.area();
     const unsigned int depth  = input_tile_size.area();
 
     TensorShape output_shape{ input.tensor_shape() };
diff --git a/src/core/CL/CLHelpers.cpp b/src/core/CL/CLHelpers.cpp
index 23c24c0..df06aff 100644
--- a/src/core/CL/CLHelpers.cpp
+++ b/src/core/CL/CLHelpers.cpp
@@ -27,6 +27,7 @@
 #include "arm_compute/core/Log.h"
 #include "arm_compute/core/Types.h"
 
+#include <utility>
 #include <vector>
 
 namespace arm_compute
@@ -164,4 +165,41 @@
     return (pos != std::string::npos);
 }
 
+bool cl_winograd_convolution_layer_supported(const Size2D &output_tile, const Size2D &kernel_size, DataLayout data_layout)
+{
+    ARM_COMPUTE_ERROR_ON(data_layout == DataLayout::UNKNOWN);
+
+    using WinogradConfiguration = std::pair<std::pair<int, int>, std::pair<int, int>>;
+
+    std::vector<WinogradConfiguration> winograd_filter_transform_nchw =
+    {
+        WinogradConfiguration(std::pair<int, int>(1, 2), std::pair<int, int>(1, 3)),
+        WinogradConfiguration(std::pair<int, int>(1, 4), std::pair<int, int>(1, 3)),
+        WinogradConfiguration(std::pair<int, int>(2, 1), std::pair<int, int>(3, 1)),
+        WinogradConfiguration(std::pair<int, int>(4, 1), std::pair<int, int>(3, 1)),
+        WinogradConfiguration(std::pair<int, int>(2, 2), std::pair<int, int>(3, 3)),
+        WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(3, 3)),
+        WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5))
+    };
+
+    std::vector<WinogradConfiguration> winograd_filter_transform_nhwc =
+    {
+        WinogradConfiguration(std::pair<int, int>(2, 2), std::pair<int, int>(3, 3)),
+        WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(3, 3)),
+        WinogradConfiguration(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5))
+    };
+
+    auto p = std::make_pair(std::pair<int, int>(output_tile.width, output_tile.height),
+                            std::pair<int, int>(kernel_size.width, kernel_size.height));
+
+    // Return true if supported
+    if(data_layout == DataLayout::NCHW)
+    {
+        return (std::find(winograd_filter_transform_nchw.begin(), winograd_filter_transform_nchw.end(), p) != winograd_filter_transform_nchw.end());
+    }
+    else
+    {
+        return (std::find(winograd_filter_transform_nhwc.begin(), winograd_filter_transform_nhwc.end(), p) != winograd_filter_transform_nhwc.end());
+    }
+}
 } // namespace arm_compute
diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index aa11edf..2bcacad 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -372,18 +372,32 @@
     { "warp_perspective_nearest_neighbour", "warp_perspective.cl" },
     { "warp_perspective_bilinear", "warp_perspective.cl" },
     { "winograd_filter_transform_2x2_3x3_nchw", "winograd.cl" },
+    { "winograd_filter_transform_2x1_3x1_nchw", "winograd.cl" },
+    { "winograd_filter_transform_1x2_1x3_nchw", "winograd.cl" },
     { "winograd_filter_transform_4x4_3x3_nchw", "winograd.cl" },
+    { "winograd_filter_transform_4x1_3x1_nchw", "winograd.cl" },
+    { "winograd_filter_transform_1x4_1x3_nchw", "winograd.cl" },
     { "winograd_filter_transform_4x4_5x5_nchw", "winograd.cl" },
     { "winograd_filter_transform_4x4_3x3_nhwc", "winograd.cl" },
     { "winograd_filter_transform_4x4_5x5_nhwc", "winograd.cl" },
-    { "winograd_input_transform_4x4_5x5_stepz1_nchw", "winograd.cl" },
     { "winograd_input_transform_2x2_3x3_stepz1_nchw", "winograd.cl" },
     { "winograd_input_transform_2x2_3x3_stepz2_nchw", "winograd.cl" },
+    { "winograd_input_transform_2x1_3x1_stepz1_nchw", "winograd.cl" },
+    { "winograd_input_transform_2x1_3x1_stepz2_nchw", "winograd.cl" },
+    { "winograd_input_transform_1x2_1x3_stepz1_nchw", "winograd.cl" },
+    { "winograd_input_transform_1x2_1x3_stepz2_nchw", "winograd.cl" },
     { "winograd_input_transform_4x4_3x3_stepz1_nchw", "winograd.cl" },
+    { "winograd_input_transform_4x1_3x1_stepz1_nchw", "winograd.cl" },
+    { "winograd_input_transform_1x4_1x3_stepz1_nchw", "winograd.cl" },
+    { "winograd_input_transform_4x4_5x5_stepz1_nchw", "winograd.cl" },
     { "winograd_input_transform_4x4_3x3_stepz1_nhwc", "winograd.cl" },
     { "winograd_input_transform_4x4_5x5_stepz1_nhwc", "winograd.cl" },
     { "winograd_output_transform_2x2_3x3_nchw", "winograd.cl" },
+    { "winograd_output_transform_2x1_3x1_nchw", "winograd.cl" },
+    { "winograd_output_transform_1x2_1x3_nchw", "winograd.cl" },
     { "winograd_output_transform_4x4_3x3_nchw", "winograd.cl" },
+    { "winograd_output_transform_4x1_3x1_nchw", "winograd.cl" },
+    { "winograd_output_transform_1x4_1x3_nchw", "winograd.cl" },
     { "winograd_output_transform_4x4_5x5_nchw", "winograd.cl" },
     { "winograd_output_transform_4x4_3x3_nhwc", "winograd.cl" },
     { "winograd_output_transform_4x4_5x5_nhwc", "winograd.cl" },
diff --git a/src/core/CL/cl_kernels/winograd.cl b/src/core/CL/cl_kernels/winograd.cl
index 93e038f..ce48d28 100644
--- a/src/core/CL/cl_kernels/winograd.cl
+++ b/src/core/CL/cl_kernels/winograd.cl
@@ -25,9 +25,11 @@
 
 #if defined(SRC_DIM_Z)
 
-/** This OpenCL kernel performs Winograd filter transform 3x3 when the data layout is NCHW and the output tile is 2x2
+/** This OpenCL kernel performs Winograd filter transform 3x3/3x1/1x3 when the data layout is NCHW and the output tile is 2x2/2x1/1x2
  *
  * @note In order to correctly split the input tensor in batches, its dimension across the Z axis (channels for NCHW, height for NHWC) must be passed at compile time using -DSRC_DIM_Z: e.g. -DSRC_DIM_Z=64
+ * @note If this kernel is used to perform Winograd filter transform 3x1, -DWINOGRAD_FILTER_TRANSFORM_HORIZONTAL has to be passed at compile time
+ * @note If this kernel is used to perform Winograd filter transform 1x3, -DWINOGRAD_FILTER_TRANSFORM_VERTICAL has to be passed at compile time
  *
  * @param[in]  src_ptr                           Pointer to the source tensor. Supported data types: F32
  * @param[in]  src_stride_x                      Stride of the source tensor in X dimension (in bytes)
@@ -57,39 +59,47 @@
     const __global uchar *src_addr = tensor4D_offset(&src, 0, 0, 0, 0);
 
     // Load the values from the input tensor
+#if defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL)
+    float3 w0 = vload3(0, (__global float *)(src_addr));
+#elif defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
+    float3 w0 = (float3)(*((__global float *)(src_addr + 0 * src_stride_y)),
+                         *((__global float *)(src_addr + 1 * src_stride_y)),
+                         *((__global float *)(src_addr + 2 * src_stride_y)));
+#else  // defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
     float3 w0 = vload3(0, (__global float *)(src_addr + 0 * src_stride_y));
     float3 w1 = vload3(0, (__global float *)(src_addr + 1 * src_stride_y));
     float3 w2 = vload3(0, (__global float *)(src_addr + 2 * src_stride_y));
-
-    // Transform the 3x3 tile in a 4x4 tile
-    float4 out0 = 0.0f;
-    float4 out1 = 0.0f;
-    float4 out2 = 0.0f;
-    float4 out3 = 0.0f;
+#endif // defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL)
 
     // Row 0
-    out0.s0 = (w0.s0);
-    out0.s1 = (w0.s0 + w0.s1 + w0.s2) * 0.5f;
-    out0.s2 = (w0.s0 + w0.s2 - w0.s1) * 0.5f;
-    out0.s3 = (w0.s2);
+    float4 out0 = 0.0f;
+    out0.s0     = (w0.s0);
+    out0.s1     = (w0.s0 + w0.s1 + w0.s2) * 0.5f;
+    out0.s2     = (w0.s0 + w0.s2 - w0.s1) * 0.5f;
+    out0.s3     = (w0.s2);
 
+#if !defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
     // Row 1
-    out1.s0 = (w0.s0 + w1.s0 + w2.s0) * 0.5f;
-    out1.s1 = (w0.s0 + w1.s0 + w2.s0 + w0.s1 + w1.s1 + w2.s1 + w0.s2 + w1.s2 + w2.s2) * 0.25f;
-    out1.s2 = (w0.s0 + w1.s0 + w2.s0 + w0.s2 + w1.s2 + w2.s2 - w0.s1 - w1.s1 - w2.s1) * 0.25f;
-    out1.s3 = (w0.s2 + w1.s2 + w2.s2) * 0.5f;
+    float4 out1 = 0.0f;
+    out1.s0     = (w0.s0 + w1.s0 + w2.s0) * 0.5f;
+    out1.s1     = (w0.s0 + w1.s0 + w2.s0 + w0.s1 + w1.s1 + w2.s1 + w0.s2 + w1.s2 + w2.s2) * 0.25f;
+    out1.s2     = (w0.s0 + w1.s0 + w2.s0 + w0.s2 + w1.s2 + w2.s2 - w0.s1 - w1.s1 - w2.s1) * 0.25f;
+    out1.s3     = (w0.s2 + w1.s2 + w2.s2) * 0.5f;
 
     // Row 2
-    out2.s0 = (w0.s0 + w2.s0 - w1.s0) * 0.5f;
-    out2.s1 = (w0.s0 + w2.s0 + w0.s1 + w2.s1 + w0.s2 + w2.s2 - w1.s0 - w1.s1 - w1.s2) * 0.25f;
-    out2.s2 = (w0.s0 + w2.s0 + w1.s1 + w0.s2 + w2.s2 - w1.s0 - w0.s1 - w2.s1 - w1.s2) * 0.25f;
-    out2.s3 = (w0.s2 + w2.s2 - w1.s2) * 0.5f;
+    float4 out2 = 0.0f;
+    out2.s0     = (w0.s0 + w2.s0 - w1.s0) * 0.5f;
+    out2.s1     = (w0.s0 + w2.s0 + w0.s1 + w2.s1 + w0.s2 + w2.s2 - w1.s0 - w1.s1 - w1.s2) * 0.25f;
+    out2.s2     = (w0.s0 + w2.s0 + w1.s1 + w0.s2 + w2.s2 - w1.s0 - w0.s1 - w2.s1 - w1.s2) * 0.25f;
+    out2.s3     = (w0.s2 + w2.s2 - w1.s2) * 0.5f;
 
     // Row 3
-    out3.s0 = (w2.s0);
-    out3.s1 = (w2.s0 + w2.s1 + w2.s2) * 0.5f;
-    out3.s2 = (w2.s0 + w2.s2 - w2.s1) * 0.5f;
-    out3.s3 = (w2.s2);
+    float4 out3 = 0.0f;
+    out3.s0     = (w2.s0);
+    out3.s1     = (w2.s0 + w2.s1 + w2.s2) * 0.5f;
+    out3.s2     = (w2.s0 + w2.s2 - w2.s1) * 0.5f;
+    out3.s3     = (w2.s2);
+#endif // !defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
 
     int z  = get_global_id(2);
     int x0 = z / SRC_DIM_Z; // idx filter
@@ -98,11 +108,15 @@
     // Get output address
     __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x0 * dst_stride_x + y0 * dst_stride_y;
 
-    // Store the 16 values across the 16 channels
-    *(__global float *)(dst_addr + 0 * dst_stride_z)  = out0.s0;
-    *(__global float *)(dst_addr + 1 * dst_stride_z)  = out0.s1;
-    *(__global float *)(dst_addr + 2 * dst_stride_z)  = out0.s2;
-    *(__global float *)(dst_addr + 3 * dst_stride_z)  = out0.s3;
+    // Store the values across the channels
+    // 16 channels for 3x3 kernels
+    // 4 channels for 3x1 or 1x3 kernels
+    *(__global float *)(dst_addr + 0 * dst_stride_z) = out0.s0;
+    *(__global float *)(dst_addr + 1 * dst_stride_z) = out0.s1;
+    *(__global float *)(dst_addr + 2 * dst_stride_z) = out0.s2;
+    *(__global float *)(dst_addr + 3 * dst_stride_z) = out0.s3;
+
+#if !defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
     *(__global float *)(dst_addr + 4 * dst_stride_z)  = out1.s0;
     *(__global float *)(dst_addr + 5 * dst_stride_z)  = out1.s1;
     *(__global float *)(dst_addr + 6 * dst_stride_z)  = out1.s2;
@@ -115,11 +129,14 @@
     *(__global float *)(dst_addr + 13 * dst_stride_z) = out3.s1;
     *(__global float *)(dst_addr + 14 * dst_stride_z) = out3.s2;
     *(__global float *)(dst_addr + 15 * dst_stride_z) = out3.s3;
+#endif // !defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
 }
 
-/** This OpenCL kernel performs Winograd filter transform 3x3 when the data layout is NCHW and the output tile is 4x4
+/** This OpenCL kernel performs Winograd filter transform 3x3/3x1/1x3 when the data layout is NCHW and the output tile is 4x4/4x1/1x4
  *
  * @note In order to correctly split the input tensor in batches, its dimension across the Z axis (channels for NCHW, height for NHWC) must be passed at compile time using -DSRC_DIM_Z: e.g. -DSRC_DIM_Z=64
+ * @note If this kernel is used to perform Winograd filter transform 3x1, -DWINOGRAD_FILTER_TRANSFORM_HORIZONTAL has to be passed at compile time
+ * @note If this kernel is used to perform Winograd filter transform 1x3, -DWINOGRAD_FILTER_TRANSFORM_VERTICAL has to be passed at compile time
  *
  * @param[in]  src_ptr                           Pointer to the source tensor. Supported data types: F32
  * @param[in]  src_stride_x                      Stride of the source tensor in X dimension (in bytes)
@@ -149,65 +166,73 @@
     const __global uchar *src_addr = tensor4D_offset(&src, 0, 0, 0, 0);
 
     // Load the values from the input tensor
+#if defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL)
+    float3 w0 = vload3(0, (__global float *)(src_addr));
+#elif defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
+    float3 w0 = (float3)(*((__global float *)(src_addr + 0 * src_stride_y)),
+                         *((__global float *)(src_addr + 1 * src_stride_y)),
+                         *((__global float *)(src_addr + 2 * src_stride_y)));
+#else  // defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
     float3 w0 = vload3(0, (__global float *)(src_addr + 0 * src_stride_y));
     float3 w1 = vload3(0, (__global float *)(src_addr + 1 * src_stride_y));
     float3 w2 = vload3(0, (__global float *)(src_addr + 2 * src_stride_y));
-
-    // Transform the 3x3 tile in a 6x6 tile
-    float8 out0 = 0.0f;
-    float8 out1 = 0.0f;
-    float8 out2 = 0.0f;
-    float8 out3 = 0.0f;
-    float8 out4 = 0.0f;
-    float8 out5 = 0.0f;
+#endif // defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL)
 
     // Row 0
-    out0.s0 = (w0.s0) / 16.f;
-    out0.s1 = (-w0.s0 - w0.s1 - w0.s2) / 24.f;
-    out0.s2 = (-w0.s0 + w0.s1 - w0.s2) / 24.f;
-    out0.s3 = (w0.s0 + 2.f * w0.s1 + 4.f * w0.s2) / 96.f;
-    out0.s4 = (w0.s0 - 2.f * w0.s1 + 4.f * w0.s2) / 96.f;
-    out0.s5 = (w0.s2) / 4.f;
+    float8 out0 = 0.0f;
+    out0.s0     = (w0.s0) / 16.f;
+    out0.s1     = (-w0.s0 - w0.s1 - w0.s2) / 24.f;
+    out0.s2     = (-w0.s0 + w0.s1 - w0.s2) / 24.f;
+    out0.s3     = (w0.s0 + 2.f * w0.s1 + 4.f * w0.s2) / 96.f;
+    out0.s4     = (w0.s0 - 2.f * w0.s1 + 4.f * w0.s2) / 96.f;
+    out0.s5     = (w0.s2) / 4.f;
 
+#if !defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
     // Row 1
-    out1.s0 = (-w0.s0 - w1.s0 - w2.s0) / 24.f;
-    out1.s1 = (w0.s0 + w1.s0 + w2.s0 + w0.s1 + w1.s1 + w2.s1 + w0.s2 + w1.s2 + w2.s2) / 36.f;
-    out1.s2 = (w0.s0 + w1.s0 + w2.s0 - w0.s1 - w1.s1 - w2.s1 + w0.s2 + w1.s2 + w2.s2) / 36.f;
-    out1.s3 = (-w0.s0 - w1.s0 - w2.s0 + 2.f * (-w0.s1 - w1.s1 - w2.s1) + 4.f * (-w0.s2 - w1.s2 - w2.s2)) / 144.f;
-    out1.s4 = (-w0.s0 - w1.s0 - w2.s0 + 2.f * (w0.s1 + w1.s1 + w2.s1) + 4.f * (-w0.s2 - w1.s2 - w2.s2)) / 144.f;
-    out1.s5 = (-w0.s2 - w1.s2 - w2.s2) / 6.f;
+    float8 out1 = 0.0f;
+    out1.s0     = (-w0.s0 - w1.s0 - w2.s0) / 24.f;
+    out1.s1     = (w0.s0 + w1.s0 + w2.s0 + w0.s1 + w1.s1 + w2.s1 + w0.s2 + w1.s2 + w2.s2) / 36.f;
+    out1.s2     = (w0.s0 + w1.s0 + w2.s0 - w0.s1 - w1.s1 - w2.s1 + w0.s2 + w1.s2 + w2.s2) / 36.f;
+    out1.s3     = (-w0.s0 - w1.s0 - w2.s0 + 2.f * (-w0.s1 - w1.s1 - w2.s1) + 4.f * (-w0.s2 - w1.s2 - w2.s2)) / 144.f;
+    out1.s4     = (-w0.s0 - w1.s0 - w2.s0 + 2.f * (w0.s1 + w1.s1 + w2.s1) + 4.f * (-w0.s2 - w1.s2 - w2.s2)) / 144.f;
+    out1.s5     = (-w0.s2 - w1.s2 - w2.s2) / 6.f;
 
     // Row 2
-    out2.s0 = (-w0.s0 + w1.s0 - w2.s0) / 24.f;
-    out2.s1 = (w0.s0 - w1.s0 + w2.s0 + w0.s1 - w1.s1 + w2.s1 + w0.s2 - w1.s2 + w2.s2) / 36.f;
-    out2.s2 = (w0.s0 - w1.s0 + w2.s0 - w0.s1 + w1.s1 - w2.s1 + w0.s2 - w1.s2 + w2.s2) / 36.f;
-    out2.s3 = (-w0.s0 + w1.s0 - w2.s0 + 2.f * (-w0.s1 + w1.s1 - w2.s1) + 4.f * (-w0.s2 + w1.s2 - w2.s2)) / 144.f;
-    out2.s4 = (-w0.s0 + w1.s0 - w2.s0 + 2.f * (w0.s1 - w1.s1 + w2.s1) + 4.f * (-w0.s2 + w1.s2 - w2.s2)) / 144.f;
-    out2.s5 = (-w0.s2 + w1.s2 - w2.s2) / 6.f;
+    float8 out2 = 0.0f;
+    out2.s0     = (-w0.s0 + w1.s0 - w2.s0) / 24.f;
+    out2.s1     = (w0.s0 - w1.s0 + w2.s0 + w0.s1 - w1.s1 + w2.s1 + w0.s2 - w1.s2 + w2.s2) / 36.f;
+    out2.s2     = (w0.s0 - w1.s0 + w2.s0 - w0.s1 + w1.s1 - w2.s1 + w0.s2 - w1.s2 + w2.s2) / 36.f;
+    out2.s3     = (-w0.s0 + w1.s0 - w2.s0 + 2.f * (-w0.s1 + w1.s1 - w2.s1) + 4.f * (-w0.s2 + w1.s2 - w2.s2)) / 144.f;
+    out2.s4     = (-w0.s0 + w1.s0 - w2.s0 + 2.f * (w0.s1 - w1.s1 + w2.s1) + 4.f * (-w0.s2 + w1.s2 - w2.s2)) / 144.f;
+    out2.s5     = (-w0.s2 + w1.s2 - w2.s2) / 6.f;
 
     // Row 3
-    out3.s0 = (w0.s0 + 2.f * w1.s0 + 4.f * w2.s0) / 96.f;
-    out3.s1 = (-w0.s0 - 2.f * w1.s0 - 4.f * w2.s0 - w0.s1 - 2.f * w1.s1 - 4.f * w2.s1 - w0.s2 - 2.f * w1.s2 - 4.f * w2.s2) / 144.f;
-    out3.s2 = (-w0.s0 - 2.f * w1.s0 - 4.f * w2.s0 + w0.s1 + 2.f * w1.s1 + 4.f * w2.s1 - w0.s2 - 2.f * w1.s2 - 4.f * w2.s2) / 144.f;
-    out3.s3 = ((w0.s0 + 2.f * w1.s0 + 4.f * w2.s0) + 2.f * (w0.s1 + 2.f * w1.s1 + 4.f * w2.s1) + 4.f * (w0.s2 + 2.f * w1.s2 + 4.f * w2.s2)) / 576.f;
-    out3.s4 = ((w0.s0 + 2.f * w1.s0 + 4.f * w2.s0) + 2.f * (-w0.s1 - 2.f * w1.s1 - 4.f * w2.s1) + 4.f * (w0.s2 + 2.f * w1.s2 + 4.f * w2.s2)) / 576.f;
-    out3.s5 = (w0.s2 + 2.f * w1.s2 + 4.f * w2.s2) / 24.f;
+    float8 out3 = 0.0f;
+    out3.s0     = (w0.s0 + 2.f * w1.s0 + 4.f * w2.s0) / 96.f;
+    out3.s1     = (-w0.s0 - 2.f * w1.s0 - 4.f * w2.s0 - w0.s1 - 2.f * w1.s1 - 4.f * w2.s1 - w0.s2 - 2.f * w1.s2 - 4.f * w2.s2) / 144.f;
+    out3.s2     = (-w0.s0 - 2.f * w1.s0 - 4.f * w2.s0 + w0.s1 + 2.f * w1.s1 + 4.f * w2.s1 - w0.s2 - 2.f * w1.s2 - 4.f * w2.s2) / 144.f;
+    out3.s3     = ((w0.s0 + 2.f * w1.s0 + 4.f * w2.s0) + 2.f * (w0.s1 + 2.f * w1.s1 + 4.f * w2.s1) + 4.f * (w0.s2 + 2.f * w1.s2 + 4.f * w2.s2)) / 576.f;
+    out3.s4     = ((w0.s0 + 2.f * w1.s0 + 4.f * w2.s0) + 2.f * (-w0.s1 - 2.f * w1.s1 - 4.f * w2.s1) + 4.f * (w0.s2 + 2.f * w1.s2 + 4.f * w2.s2)) / 576.f;
+    out3.s5     = (w0.s2 + 2.f * w1.s2 + 4.f * w2.s2) / 24.f;
 
     // Row 4
-    out4.s0 = (w0.s0 - 2.f * w1.s0 + 4.f * w2.s0) / 96.f;
-    out4.s1 = (-w0.s0 + 2.f * w1.s0 - 4.f * w2.s0 - w0.s1 + 2.f * w1.s1 - 4.f * w2.s1 - w0.s2 + 2.f * w1.s2 - 4.f * w2.s2) / 144.f;
-    out4.s2 = (-w0.s0 + 2.f * w1.s0 - 4.f * w2.s0 + w0.s1 - 2.f * w1.s1 + 4.f * w2.s1 - w0.s2 + 2.f * w1.s2 - 4.f * w2.s2) / 144.f;
-    out4.s3 = ((w0.s0 - 2.f * w1.s0 + 4.f * w2.s0) + 2.f * (w0.s1 - 2.f * w1.s1 + 4.f * w2.s1) + 4.f * (w0.s2 - 2.f * w1.s2 + 4.f * w2.s2)) / 576.f;
-    out4.s4 = ((w0.s0 - 2.f * w1.s0 + 4.f * w2.s0) + 2.f * (-w0.s1 + 2.f * w1.s1 - 4.f * w2.s1) + 4.f * (w0.s2 - 2.f * w1.s2 + 4.f * w2.s2)) / 576.f;
-    out4.s5 = (w0.s2 - 2.f * w1.s2 + 4.f * w2.s2) / 24.f;
+    float8 out4 = 0.0f;
+    out4.s0     = (w0.s0 - 2.f * w1.s0 + 4.f * w2.s0) / 96.f;
+    out4.s1     = (-w0.s0 + 2.f * w1.s0 - 4.f * w2.s0 - w0.s1 + 2.f * w1.s1 - 4.f * w2.s1 - w0.s2 + 2.f * w1.s2 - 4.f * w2.s2) / 144.f;
+    out4.s2     = (-w0.s0 + 2.f * w1.s0 - 4.f * w2.s0 + w0.s1 - 2.f * w1.s1 + 4.f * w2.s1 - w0.s2 + 2.f * w1.s2 - 4.f * w2.s2) / 144.f;
+    out4.s3     = ((w0.s0 - 2.f * w1.s0 + 4.f * w2.s0) + 2.f * (w0.s1 - 2.f * w1.s1 + 4.f * w2.s1) + 4.f * (w0.s2 - 2.f * w1.s2 + 4.f * w2.s2)) / 576.f;
+    out4.s4     = ((w0.s0 - 2.f * w1.s0 + 4.f * w2.s0) + 2.f * (-w0.s1 + 2.f * w1.s1 - 4.f * w2.s1) + 4.f * (w0.s2 - 2.f * w1.s2 + 4.f * w2.s2)) / 576.f;
+    out4.s5     = (w0.s2 - 2.f * w1.s2 + 4.f * w2.s2) / 24.f;
 
     // Row 5
-    out5.s0 = (w2.s0) / 4.f;
-    out5.s1 = (-w2.s0 - w2.s1 - w2.s2) / 6.f;
-    out5.s2 = (-w2.s0 + w2.s1 - w2.s2) / 6.f;
-    out5.s3 = (w2.s0 + 2.f * w2.s1 + 4.f * w2.s2) / 24.f;
-    out5.s4 = (w2.s0 - 2.f * w2.s1 + 4.f * w2.s2) / 24.f;
-    out5.s5 = (w2.s2);
+    float8 out5 = 0.0f;
+    out5.s0     = (w2.s0) / 4.f;
+    out5.s1     = (-w2.s0 - w2.s1 - w2.s2) / 6.f;
+    out5.s2     = (-w2.s0 + w2.s1 - w2.s2) / 6.f;
+    out5.s3     = (w2.s0 + 2.f * w2.s1 + 4.f * w2.s2) / 24.f;
+    out5.s4     = (w2.s0 - 2.f * w2.s1 + 4.f * w2.s2) / 24.f;
+    out5.s5     = (w2.s2);
+#endif // !defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
 
     int z  = get_global_id(2);
     int x0 = z / SRC_DIM_Z; // idx filter
@@ -216,13 +241,17 @@
     // Get output address
     __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x0 * dst_stride_x + y0 * dst_stride_y;
 
-    // Store the 36 values across the 36 channels
-    *(__global float *)(dst_addr + 0 * dst_stride_z)  = out0.s0;
-    *(__global float *)(dst_addr + 1 * dst_stride_z)  = out0.s1;
-    *(__global float *)(dst_addr + 2 * dst_stride_z)  = out0.s2;
-    *(__global float *)(dst_addr + 3 * dst_stride_z)  = out0.s3;
-    *(__global float *)(dst_addr + 4 * dst_stride_z)  = out0.s4;
-    *(__global float *)(dst_addr + 5 * dst_stride_z)  = out0.s5;
+    // Store the values across the channels
+    // 36 channels for 3x3 kernels
+    // 6 channels for 3x1 or 1x3 kernels
+    *(__global float *)(dst_addr + 0 * dst_stride_z) = out0.s0;
+    *(__global float *)(dst_addr + 1 * dst_stride_z) = out0.s1;
+    *(__global float *)(dst_addr + 2 * dst_stride_z) = out0.s2;
+    *(__global float *)(dst_addr + 3 * dst_stride_z) = out0.s3;
+    *(__global float *)(dst_addr + 4 * dst_stride_z) = out0.s4;
+    *(__global float *)(dst_addr + 5 * dst_stride_z) = out0.s5;
+
+#if !defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
     *(__global float *)(dst_addr + 6 * dst_stride_z)  = out1.s0;
     *(__global float *)(dst_addr + 7 * dst_stride_z)  = out1.s1;
     *(__global float *)(dst_addr + 8 * dst_stride_z)  = out1.s2;
@@ -253,8 +282,205 @@
     *(__global float *)(dst_addr + 33 * dst_stride_z) = out5.s3;
     *(__global float *)(dst_addr + 34 * dst_stride_z) = out5.s4;
     *(__global float *)(dst_addr + 35 * dst_stride_z) = out5.s5;
+#endif // !defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
 }
 
+#if defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL)
+/** This OpenCL kernel performs Winograd filter transform 3x1 when the data layout is NCHW and the output tile is 2x1
+ *
+ * @note In order to correctly split the input tensor in batches, its dimension across the Z axis (channels for NCHW, height for NHWC) must be passed at compile time using -DSRC_DIM_Z: e.g. -DSRC_DIM_Z=64
+ * @note -DWINOGRAD_FILTER_TRANSFORM_HORIZONTAL has to be passed at compile time to perform Winograd Filter Transform
+ *
+ * @param[in]  src_ptr                           Pointer to the source tensor. Supported data types: F32
+ * @param[in]  src_stride_x                      Stride of the source tensor in X dimension (in bytes)
+ * @param[in]  src_step_x                        src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  src_stride_y                      Stride of the source tensor in Y dimension (in bytes)
+ * @param[in]  src_step_y                        src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  src_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  src_step_z                        src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  src_stride_w                      Stride of the source tensor in W dimension (in bytes)
+ * @param[in]  src_step_w                        src_stride_w * number of elements along W processed per workitem(in bytes)
+ * @param[in]  src_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] dst_ptr                           Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in]  dst_stride_x                      Stride of the destination tensor in X dimension (in bytes)
+ * @param[in]  dst_step_x                        dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  dst_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in]  dst_step_y                        dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  src_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  src_step_z                        src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void winograd_filter_transform_2x1_3x1_nchw(
+    TENSOR4D_DECLARATION(src),
+    TENSOR3D_DECLARATION(dst))
+{
+    winograd_filter_transform_2x2_3x3_nchw(src_ptr,
+                                           src_stride_x,
+                                           src_step_x,
+                                           src_stride_y,
+                                           src_step_y,
+                                           src_stride_z,
+                                           src_step_z,
+                                           src_stride_w,
+                                           src_step_w,
+                                           src_offset_first_element_in_bytes,
+                                           dst_ptr,
+                                           dst_stride_x,
+                                           dst_step_x,
+                                           dst_stride_y,
+                                           dst_step_y,
+                                           dst_stride_z,
+                                           dst_step_z,
+                                           dst_offset_first_element_in_bytes);
+}
+
+/** This OpenCL kernel performs Winograd filter transform 3x1 when the data layout is NCHW and the output tile is 4x1
+ *
+ * @note In order to correctly split the input tensor in batches, its dimension across the Z axis (channels for NCHW, height for NHWC) must be passed at compile time using -DSRC_DIM_Z: e.g. -DSRC_DIM_Z=64
+ * @note -DWINOGRAD_FILTER_TRANSFORM_HORIZONTAL has to be passed at compile time to perform Winograd Filter Transform
+ *
+ * @param[in]  src_ptr                           Pointer to the source tensor. Supported data types: F32
+ * @param[in]  src_stride_x                      Stride of the source tensor in X dimension (in bytes)
+ * @param[in]  src_step_x                        src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  src_stride_y                      Stride of the source tensor in Y dimension (in bytes)
+ * @param[in]  src_step_y                        src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  src_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  src_step_z                        src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  src_stride_w                      Stride of the source tensor in W dimension (in bytes)
+ * @param[in]  src_step_w                        src_stride_w * number of elements along W processed per workitem(in bytes)
+ * @param[in]  src_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] dst_ptr                           Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in]  dst_stride_x                      Stride of the destination tensor in X dimension (in bytes)
+ * @param[in]  dst_step_x                        dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  dst_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in]  dst_step_y                        dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  src_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  src_step_z                        src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void winograd_filter_transform_4x1_3x1_nchw(
+    TENSOR4D_DECLARATION(src),
+    TENSOR3D_DECLARATION(dst))
+{
+    winograd_filter_transform_4x4_3x3_nchw(src_ptr,
+                                           src_stride_x,
+                                           src_step_x,
+                                           src_stride_y,
+                                           src_step_y,
+                                           src_stride_z,
+                                           src_step_z,
+                                           src_stride_w,
+                                           src_step_w,
+                                           src_offset_first_element_in_bytes,
+                                           dst_ptr,
+                                           dst_stride_x,
+                                           dst_step_x,
+                                           dst_stride_y,
+                                           dst_step_y,
+                                           dst_stride_z,
+                                           dst_step_z,
+                                           dst_offset_first_element_in_bytes);
+}
+#endif // defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL)
+
+#if defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
+/** This OpenCL kernel performs Winograd filter transform 1x3 when the data layout is NCHW and the output tile is 1x2
+ *
+ * @note In order to correctly split the input tensor in batches, its dimension across the Z axis (channels for NCHW, height for NHWC) must be passed at compile time using -DSRC_DIM_Z: e.g. -DSRC_DIM_Z=64
+ * @note -DWINOGRAD_FILTER_TRANSFORM_VERTICAL has to be passed at compile time to perform Winograd Filter Transform
+ *
+ * @param[in]  src_ptr                           Pointer to the source tensor. Supported data types: F32
+ * @param[in]  src_stride_x                      Stride of the source tensor in X dimension (in bytes)
+ * @param[in]  src_step_x                        src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  src_stride_y                      Stride of the source tensor in Y dimension (in bytes)
+ * @param[in]  src_step_y                        src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  src_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  src_step_z                        src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  src_stride_w                      Stride of the source tensor in W dimension (in bytes)
+ * @param[in]  src_step_w                        src_stride_w * number of elements along W processed per workitem(in bytes)
+ * @param[in]  src_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] dst_ptr                           Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in]  dst_stride_x                      Stride of the destination tensor in X dimension (in bytes)
+ * @param[in]  dst_step_x                        dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  dst_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in]  dst_step_y                        dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  src_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  src_step_z                        src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void winograd_filter_transform_1x2_1x3_nchw(
+    TENSOR4D_DECLARATION(src),
+    TENSOR3D_DECLARATION(dst))
+{
+    winograd_filter_transform_2x2_3x3_nchw(src_ptr,
+                                           src_stride_x,
+                                           src_step_x,
+                                           src_stride_y,
+                                           src_step_y,
+                                           src_stride_z,
+                                           src_step_z,
+                                           src_stride_w,
+                                           src_step_w,
+                                           src_offset_first_element_in_bytes,
+                                           dst_ptr,
+                                           dst_stride_x,
+                                           dst_step_x,
+                                           dst_stride_y,
+                                           dst_step_y,
+                                           dst_stride_z,
+                                           dst_step_z,
+                                           dst_offset_first_element_in_bytes);
+}
+
+/** This OpenCL kernel performs Winograd filter transform 1x3 when the data layout is NCHW and the output tile is 1x4
+ *
+ * @note In order to correctly split the input tensor in batches, its dimension across the Z axis (channels for NCHW, height for NHWC) must be passed at compile time using -DSRC_DIM_Z: e.g. -DSRC_DIM_Z=64
+ * @note -DWINOGRAD_FILTER_TRANSFORM_VERTICAL has to be passed at compile time to perform Winograd Filter Transform
+ *
+ * @param[in]  src_ptr                           Pointer to the source tensor. Supported data types: F32
+ * @param[in]  src_stride_x                      Stride of the source tensor in X dimension (in bytes)
+ * @param[in]  src_step_x                        src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  src_stride_y                      Stride of the source tensor in Y dimension (in bytes)
+ * @param[in]  src_step_y                        src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  src_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  src_step_z                        src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  src_stride_w                      Stride of the source tensor in W dimension (in bytes)
+ * @param[in]  src_step_w                        src_stride_w * number of elements along W processed per workitem(in bytes)
+ * @param[in]  src_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] dst_ptr                           Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in]  dst_stride_x                      Stride of the destination tensor in X dimension (in bytes)
+ * @param[in]  dst_step_x                        dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  dst_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in]  dst_step_y                        dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  src_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  src_step_z                        src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void winograd_filter_transform_1x4_1x3_nchw(
+    TENSOR4D_DECLARATION(src),
+    TENSOR3D_DECLARATION(dst))
+{
+    winograd_filter_transform_4x4_3x3_nchw(src_ptr,
+                                           src_stride_x,
+                                           src_step_x,
+                                           src_stride_y,
+                                           src_step_y,
+                                           src_stride_z,
+                                           src_step_z,
+                                           src_stride_w,
+                                           src_step_w,
+                                           src_offset_first_element_in_bytes,
+                                           dst_ptr,
+                                           dst_stride_x,
+                                           dst_step_x,
+                                           dst_stride_y,
+                                           dst_step_y,
+                                           dst_stride_z,
+                                           dst_step_z,
+                                           dst_offset_first_element_in_bytes);
+}
+#endif // defined(WINOGRAD_FILTER_TRANSFORM_VERTICAL)
+
 /** This OpenCL kernel performs Winograd filter transform 3x3 when the data layout is NHWC and the output tile is 4x4
  *
  * @note In order to correctly split the input tensor in batches, its dimension across the Z axis (channels for NCHW, height for NHWC) must be passed at compile time using -DSRC_DIM_Z: e.g. -DSRC_DIM_Z=64
@@ -928,11 +1154,15 @@
 }
 #endif // defined(SRC_DIM_Z)
 
-#if defined(NUM_TILES_X) && defined(PAD_LEFT) && defined(PAD_TOP)
-/** This OpenCL kernel computes the input transform when the kernel size is 3x3 and the output tile is 2x2
+#if defined(NUM_TILES_X) && defined(PAD_LEFT) && defined(PAD_TOP) && defined(OUTPUT_TILE_W) && defined(OUTPUT_TILE_H)
+/** This OpenCL kernel computes the input transform when the kernel size is 3x3/3x1 or 1x3 and the output tile is 2x2/2x1 or 1x2
  *
  * @note The number of tiles in the x axis must be passed at compile time using -DNUM_TILES_X (i.e.-DNUM_TILES_X=5).
  * @note The pad left and pad top must be passed at compile time using -DPAD_LEFT and -DPAD_TOP (i.e.-DPAD_LEFT=1 and -DPAD_TOP=0).
+ * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=2
+ * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=2
+ * @note If this kernel is used to perform Winograd input transform 3x1, -DWINOGRAD_INPUT_TRANSFORM_HORIZONTAL has to be passed at compile time
+ * @note If this kernel is used to perform Winograd input transform 1x3, -DWINOGRAD_INPUT_TRANSFORM_VERTICAL has to be passed at compile time
  *
  * @param[in] src_ptr                           Pointer to the source image. Supported data types: F32
  * @param[in] src_stride_x                      Stride of the source image in X dimension (in bytes)
@@ -960,25 +1190,40 @@
     int z = get_global_id(2);
 
     // Compute input address
-    __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * 2 * src_stride_x + y * 2 * src_stride_y + z * src_stride_z;
+    __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * OUTPUT_TILE_W * sizeof(float) + y * OUTPUT_TILE_H * src_stride_y + z * src_stride_z;
 
-    src_addr = src_addr - ((int)PAD_LEFT * src_stride_x) - ((int)PAD_TOP * src_stride_y);
+    src_addr = src_addr - ((int)PAD_LEFT * sizeof(float)) - ((int)PAD_TOP * src_stride_y);
 
+#if defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL)
+    float4 in_row0 = vload4(0, (__global float *)(src_addr));
+#elif defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL) // !defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL)
+    float4 in_row0 = (float4)(*((__global float *)(src_addr + 0 * src_stride_y)),
+                              *((__global float *)(src_addr + 1 * src_stride_y)),
+                              *((__global float *)(src_addr + 2 * src_stride_y)),
+                              *((__global float *)(src_addr + 3 * src_stride_y)));
+#else                                            // !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
     float4 in_row0 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y));
     float4 in_row1 = vload4(0, (__global float *)(src_addr + 1 * src_stride_y));
     float4 in_row2 = vload4(0, (__global float *)(src_addr + 2 * src_stride_y));
     float4 in_row3 = vload4(0, (__global float *)(src_addr + 3 * src_stride_y));
+#endif                                           // !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
 
-    float4 tmp0 = in_row0 - in_row2;
-    float4 tmp1 = in_row1 + in_row2;
-    float4 tmp2 = in_row2 - in_row1;
-    float4 tmp3 = in_row1 - in_row3;
+    float4 tmp0 = in_row0;
+
+#if !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
+    tmp0 -= in_row2;
+#endif // !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
 
     float out00 = tmp0.s0 - tmp0.s2;
     float out01 = tmp0.s1 + tmp0.s2;
     float out02 = tmp0.s2 - tmp0.s1;
     float out03 = tmp0.s1 - tmp0.s3;
 
+#if !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
+    float4 tmp1 = in_row1 + in_row2;
+    float4 tmp2 = in_row2 - in_row1;
+    float4 tmp3 = in_row1 - in_row3;
+
     float out10 = tmp1.s0 - tmp1.s2;
     float out11 = tmp1.s1 + tmp1.s2;
     float out12 = tmp1.s2 - tmp1.s1;
@@ -993,13 +1238,16 @@
     float out31 = tmp3.s1 + tmp3.s2;
     float out32 = tmp3.s2 - tmp3.s1;
     float out33 = tmp3.s1 - tmp3.s3;
+#endif // !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
 
-    __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + z * dst_stride_x + (x + y * (int)NUM_TILES_X) * dst_stride_y;
+    __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + z * sizeof(float) + (x + y * (int)NUM_TILES_X) * dst_stride_y;
 
-    *((__global float *)(dst_addr + 0 * dst_stride_z))  = out00;
-    *((__global float *)(dst_addr + 1 * dst_stride_z))  = out01;
-    *((__global float *)(dst_addr + 2 * dst_stride_z))  = out02;
-    *((__global float *)(dst_addr + 3 * dst_stride_z))  = out03;
+    *((__global float *)(dst_addr + 0 * dst_stride_z)) = out00; // in_row0.s0; out00;
+    *((__global float *)(dst_addr + 1 * dst_stride_z)) = out01; // in_row0.s1; out01;
+    *((__global float *)(dst_addr + 2 * dst_stride_z)) = out02; // in_row0.s2; out02;
+    *((__global float *)(dst_addr + 3 * dst_stride_z)) = out03; // in_row0.s3; out03;
+
+#if !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
     *((__global float *)(dst_addr + 4 * dst_stride_z))  = out10;
     *((__global float *)(dst_addr + 5 * dst_stride_z))  = out11;
     *((__global float *)(dst_addr + 6 * dst_stride_z))  = out12;
@@ -1012,12 +1260,17 @@
     *((__global float *)(dst_addr + 13 * dst_stride_z)) = out31;
     *((__global float *)(dst_addr + 14 * dst_stride_z)) = out32;
     *((__global float *)(dst_addr + 15 * dst_stride_z)) = out33;
+#endif // !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
 }
 
-/** This OpenCL kernel computes the input transform when the kernel size is 3x3, the output tile is 2x2 and the number of channels is multiple of 2
+/** This OpenCL kernel computes the input transform when the kernel size is 3x3/3x1 or 1x3, the output tile is 2x2/2x1 or 1x2 and the number of channels is multiple of 2
  *
  * @note The number of tiles in the x axis must be passed at compile time using -DNUM_TILES_X (i.e.-DNUM_TILES_X=5).
  * @note The pad left and pad top must be passed at compile time using -DPAD_LEFT and -DPAD_TOP (i.e.-DPAD_LEFT=1 and -DPAD_TOP=0).
+ * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=2
+ * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=2
+ * @note If this kernel is used to perform Winograd input transform 3x1, -DWINOGRAD_INPUT_TRANSFORM_HORIZONTAL has to be passed at compile time
+ * @note If this kernel is used to perform Winograd input transform 1x3, -DWINOGRAD_INPUT_TRANSFORM_VERTICAL has to be passed at compile time
  *
  * @param[in] src_ptr                           Pointer to the source image. Supported data types: F32
  * @param[in] src_stride_x                      Stride of the source image in X dimension (in bytes)
@@ -1045,36 +1298,61 @@
     int z = get_global_id(2) * 2;
 
     // Compute input address
-    __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * 2 * src_stride_x + y * 2 * src_stride_y + z * src_stride_z;
+    __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * OUTPUT_TILE_W * sizeof(float) + y * OUTPUT_TILE_H * src_stride_y + z * src_stride_z;
 
-    src_addr = src_addr - ((int)PAD_LEFT * src_stride_x) - ((int)PAD_TOP * src_stride_y);
+    src_addr = src_addr - ((int)PAD_LEFT * sizeof(float)) - ((int)PAD_TOP * src_stride_y);
 
+#if defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL)
+    float4 in_row0 = vload4(0, (__global float *)(src_addr));
+#elif defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL) // !defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL)
+    float4 in_row0 = (float4)(*((__global float *)(src_addr + 0 * src_stride_y)),
+                              *((__global float *)(src_addr + 1 * src_stride_y)),
+                              *((__global float *)(src_addr + 2 * src_stride_y)),
+                              *((__global float *)(src_addr + 3 * src_stride_y)));
+#else                                            // !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
     float4 in_row0 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y));
     float4 in_row1 = vload4(0, (__global float *)(src_addr + 1 * src_stride_y));
     float4 in_row2 = vload4(0, (__global float *)(src_addr + 2 * src_stride_y));
     float4 in_row3 = vload4(0, (__global float *)(src_addr + 3 * src_stride_y));
+#endif                                           // !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
 
     src_addr += src_stride_z;
+#if defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL)
+    float4 in_row4 = vload4(0, (__global float *)(src_addr));
+#elif defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL) // !defined(WINOGRAD_FILTER_TRANSFORM_HORIZONTAL)
+    float4 in_row4 = (float4)(*((__global float *)(src_addr + 0 * src_stride_y)),
+                              *((__global float *)(src_addr + 1 * src_stride_y)),
+                              *((__global float *)(src_addr + 2 * src_stride_y)),
+                              *((__global float *)(src_addr + 3 * src_stride_y)));
+#else                                            // !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
     float4 in_row4 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y));
     float4 in_row5 = vload4(0, (__global float *)(src_addr + 1 * src_stride_y));
     float4 in_row6 = vload4(0, (__global float *)(src_addr + 2 * src_stride_y));
     float4 in_row7 = vload4(0, (__global float *)(src_addr + 3 * src_stride_y));
+#endif                                           // !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
 
-    float4 tmp0 = in_row0 - in_row2;
-    float4 tmp1 = in_row1 + in_row2;
-    float4 tmp2 = in_row2 - in_row1;
-    float4 tmp3 = in_row1 - in_row3;
+    float4 tmp0 = in_row0;
+    float4 tmp4 = in_row4;
 
-    float4 tmp4 = in_row4 - in_row6;
-    float4 tmp5 = in_row5 + in_row6;
-    float4 tmp6 = in_row6 - in_row5;
-    float4 tmp7 = in_row5 - in_row7;
+#if !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
+    tmp0 -= in_row2;
+    tmp4 -= in_row6;
+#endif // !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
 
     float2 out00 = (float2)(tmp0.s0 - tmp0.s2, tmp4.s0 - tmp4.s2);
     float2 out01 = (float2)(tmp0.s1 + tmp0.s2, tmp4.s1 + tmp4.s2);
     float2 out02 = (float2)(tmp0.s2 - tmp0.s1, tmp4.s2 - tmp4.s1);
     float2 out03 = (float2)(tmp0.s1 - tmp0.s3, tmp4.s1 - tmp4.s3);
 
+#if !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
+    float4 tmp1 = in_row1 + in_row2;
+    float4 tmp2 = in_row2 - in_row1;
+    float4 tmp3 = in_row1 - in_row3;
+
+    float4 tmp5 = in_row5 + in_row6;
+    float4 tmp6 = in_row6 - in_row5;
+    float4 tmp7 = in_row5 - in_row7;
+
     float2 out10 = (float2)(tmp1.s0 - tmp1.s2, tmp5.s0 - tmp5.s2);
     float2 out11 = (float2)(tmp1.s1 + tmp1.s2, tmp5.s1 + tmp5.s2);
     float2 out12 = (float2)(tmp1.s2 - tmp1.s1, tmp5.s2 - tmp5.s1);
@@ -1089,13 +1367,16 @@
     float2 out31 = (float2)(tmp3.s1 + tmp3.s2, tmp7.s1 + tmp7.s2);
     float2 out32 = (float2)(tmp3.s2 - tmp3.s1, tmp7.s2 - tmp7.s1);
     float2 out33 = (float2)(tmp3.s1 - tmp3.s3, tmp7.s1 - tmp7.s3);
+#endif // !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
 
-    __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + z * dst_stride_x + (x + y * (int)NUM_TILES_X) * dst_stride_y;
+    __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + z * sizeof(float) + (x + y * (int)NUM_TILES_X) * dst_stride_y;
 
     vstore2(out00, 0, (__global float *)(dst_addr + 0 * dst_stride_z));
     vstore2(out01, 0, (__global float *)(dst_addr + 1 * dst_stride_z));
     vstore2(out02, 0, (__global float *)(dst_addr + 2 * dst_stride_z));
     vstore2(out03, 0, (__global float *)(dst_addr + 3 * dst_stride_z));
+
+#if !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
     vstore2(out10, 0, (__global float *)(dst_addr + 4 * dst_stride_z));
     vstore2(out11, 0, (__global float *)(dst_addr + 5 * dst_stride_z));
     vstore2(out12, 0, (__global float *)(dst_addr + 6 * dst_stride_z));
@@ -1108,12 +1389,17 @@
     vstore2(out31, 0, (__global float *)(dst_addr + 13 * dst_stride_z));
     vstore2(out32, 0, (__global float *)(dst_addr + 14 * dst_stride_z));
     vstore2(out33, 0, (__global float *)(dst_addr + 15 * dst_stride_z));
+#endif // !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
 }
 
 /** This OpenCL kernel computes the input transform when the output tile is 4x4, the filter size 3x3 and the data layout is NCHW
  *
  * @note The number of tiles in the x axis must be passed at compile time using -DNUM_TILES_X (i.e.-DNUM_TILES_X=5).
  * @note The pad left and pad top must be passed at compile time using -DPAD_LEFT and -DPAD_TOP (i.e.-DPAD_LEFT=1 and -DPAD_TOP=0).
+ * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=2
+ * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=2
+ * @note If this kernel is used to perform Winograd input transform 3x1, -DWINOGRAD_INPUT_TRANSFORM_HORIZONTAL has to be passed at compile time
+ * @note If this kernel is used to perform Winograd input transform 1x3, -DWINOGRAD_INPUT_TRANSFORM_VERTICAL has to be passed at compile time
  *
  * @param[in] src_ptr                           Pointer to the source image. Supported data types: F32
  * @param[in] src_stride_x                      Stride of the source image in X dimension (in bytes)
@@ -1141,14 +1427,45 @@
     int z = get_global_id(2);
 
     // Compute input address
-    __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * 4 * src_stride_x + y * 4 * src_stride_y + z * src_stride_z;
+    __global uchar *src_addr = src_ptr + src_offset_first_element_in_bytes + x * OUTPUT_TILE_W * sizeof(float) + y * OUTPUT_TILE_H * src_stride_y + z * src_stride_z;
 
-    src_addr = src_addr - ((int)PAD_LEFT * src_stride_x) - ((int)PAD_TOP * src_stride_y);
+    src_addr = src_addr - ((int)PAD_LEFT * sizeof(float)) - ((int)PAD_TOP * src_stride_y);
 
+#if defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
+    // Row0
+    float4 d00 = (float4)(*((__global float *)(src_addr + 0 * src_stride_y)),
+                          *((__global float *)(src_addr + 1 * src_stride_y)),
+                          *((__global float *)(src_addr + 2 * src_stride_y)),
+                          *((__global float *)(src_addr + 3 * src_stride_y)));
+    float2 d01 = (float2)(*((__global float *)(src_addr + 4 * src_stride_y)),
+                          *((__global float *)(src_addr + 5 * src_stride_y)));
+#else  // defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
+    // Row0
+    float4 d00 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y));
+    float2 d01 = vload2(2, (__global float *)(src_addr + 0 * src_stride_y));
+#endif // defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
+
+    float out0 = 0.0f;
+    float out1 = 0.0f;
+    float out2 = 0.0f;
+    float out3 = 0.0f;
+    float out4 = 0.0f;
+    float out5 = 0.0f;
+
+    // Channels [0, 5]: [out00, out01, out02, out03, out04, out05]
+    out0 += 16.0f * d00.s0 - 20.0f * d00.s2 + 4.0f * d01.s0;
+    out1 += -16.0f * d00.s1 - 16.0f * d00.s2 + 4.0f * d00.s3 + 4.0f * d01.s0;
+    out2 += 16.0f * d00.s1 - 16.0f * d00.s2 - 4.0f * d00.s3 + 4.0f * d01.s0;
+    out3 += -8.0f * d00.s1 - 4.0f * d00.s2 + 8.0f * d00.s3 + 4.0f * d01.s0;
+    out4 += 8.0f * d00.s1 - 4.0f * d00.s2 - 8.0f * d00.s3 + 4.0f * d01.s0;
+    out5 += 16.0f * d00.s1 - 20.0f * d00.s3 + 4.0f * d01.s1;
+
+#if !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
     // Row4
     float4 d40 = vload4(0, (__global float *)(src_addr + 4 * src_stride_y));
     float2 d41 = vload2(2, (__global float *)(src_addr + 4 * src_stride_y));
 
+    // k0, k1, k2, k3, k4, k5 are common terms for row0, row1, row2, row3 and row4
     float k0 = d41.s0;
     float k1 = d41.s0;
     float k2 = d41.s0;
@@ -1163,25 +1480,44 @@
     k4 += 2.0f * d40.s1 - 2.0f * d40.s3 - d40.s2;
     k5 += 4.0f * d40.s1 - 5.0f * d40.s3 + d41.s1;
 
-    // Row0
-    float4 d00 = vload4(0, (__global float *)(src_addr + 0 * src_stride_y));
-    float2 d01 = vload2(2, (__global float *)(src_addr + 0 * src_stride_y));
+    out0 += k0;
+    out1 += k1;
+    out2 += k2;
+    out3 += k3;
+    out4 += k4;
+    out5 += k5;
 
     // Row2
     float4 d20 = vload4(0, (__global float *)(src_addr + 2 * src_stride_y));
     float2 d21 = vload2(2, (__global float *)(src_addr + 2 * src_stride_y));
 
+    out0 += -20.0f * d20.s0 + 25.0f * d20.s2 - 5.0f * d21.s0;
+    out1 += +20.0f * d20.s1 + 20.0f * d20.s2 - 5.0f * d20.s3 - 5.0f * d21.s0;
+    out2 += -20.0f * d20.s1 + 20.0f * d20.s2 + 5.0f * d20.s3 - 5.0f * d21.s0;
+    out3 += +10.0f * d20.s1 + 5.0f * d20.s2 - 10.0f * d20.s3 - 5.0f * d21.s0;
+    out4 += -10.0f * d20.s1 + 5.0f * d20.s2 + 10.0f * d20.s3 - 5.0f * d21.s0;
+    out5 += -20.0f * d20.s1 + 25.0f * d20.s3 - 5.0f * d21.s1;
+#endif // #if !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
+
     // Compute destination address
-    __global float *dst_addr = (__global float *)(dst_ptr + dst_offset_first_element_in_bytes + z * dst_stride_x + (x + y * (int)NUM_TILES_X) * dst_stride_y);
+    __global float *dst_addr = (__global float *)(dst_ptr + dst_offset_first_element_in_bytes + z * sizeof(float) + (x + y * (int)NUM_TILES_X) * dst_stride_y);
 
     uint dst_plane_stride = dst_stride_z / sizeof(float);
 
-    float out0  = k0;
-    float out1  = k1;
-    float out2  = k2;
-    float out3  = k3;
-    float out4  = k4;
-    float out5  = k5;
+    *(dst_addr) = out0;
+    dst_addr += dst_plane_stride;
+    *(dst_addr) = out1;
+    dst_addr += dst_plane_stride;
+    *(dst_addr) = out2;
+    dst_addr += dst_plane_stride;
+    *(dst_addr) = out3;
+    dst_addr += dst_plane_stride;
+    *(dst_addr) = out4;
+    dst_addr += dst_plane_stride;
+    *(dst_addr) = out5;
+    dst_addr += dst_plane_stride;
+
+#if !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
     float out6  = k0;
     float out7  = k1;
     float out8  = k2;
@@ -1207,27 +1543,6 @@
     float out28 = k4;
     float out29 = k5;
 
-    // Channels [0, 5]: [out00, out01, out02, out03, out04, out05]
-    out0 += 16.0f * d00.s0 - 20.0f * d00.s2 - 20.0f * d20.s0 + 25.0f * d20.s2 + 4.0f * d01.s0 - 5.0f * d21.s0;
-    out1 += -16.0f * d00.s1 - 16.0f * d00.s2 + 4.0f * d00.s3 + 20.0f * d20.s1 + 20.0f * d20.s2 - 5.0f * d20.s3 + 4.0f * d01.s0 - 5.0f * d21.s0;
-    out2 += 16.0f * d00.s1 - 16.0f * d00.s2 - 4.0f * d00.s3 - 20.0f * d20.s1 + 20.0f * d20.s2 + 5.0f * d20.s3 + 4.0f * d01.s0 - 5.0f * d21.s0;
-    out3 += -8.0f * d00.s1 - 4.0f * d00.s2 + 8.0f * d00.s3 + 10.0f * d20.s1 + 5.0f * d20.s2 - 10.0f * d20.s3 + 4.0f * d01.s0 - 5.0f * d21.s0;
-    out4 += 8.0f * d00.s1 - 4.0f * d00.s2 - 8.0f * d00.s3 - 10.0f * d20.s1 + 5.0f * d20.s2 + 10.0f * d20.s3 + 4.0f * d01.s0 - 5.0f * d21.s0;
-    out5 += 16.0f * d00.s1 - 20.0f * d00.s3 - 20.0f * d20.s1 + 4.0f * d01.s1 + 25.0f * d20.s3 - 5.0f * d21.s1;
-
-    *(dst_addr) = out0;
-    dst_addr += dst_plane_stride;
-    *(dst_addr) = out1;
-    dst_addr += dst_plane_stride;
-    *(dst_addr) = out2;
-    dst_addr += dst_plane_stride;
-    *(dst_addr) = out3;
-    dst_addr += dst_plane_stride;
-    *(dst_addr) = out4;
-    dst_addr += dst_plane_stride;
-    *(dst_addr) = out5;
-    dst_addr += dst_plane_stride;
-
     // Row1
     float4 d10 = vload4(0, (__global float *)(src_addr + 1 * src_stride_y));
     float2 d11 = vload2(2, (__global float *)(src_addr + 1 * src_stride_y));
@@ -1367,6 +1682,7 @@
     dst_addr += dst_plane_stride;
     *(dst_addr) = out5;
     dst_addr += dst_plane_stride;
+#endif // #if !defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
 }
 
 #if defined(SRC_DIM_1) && defined(SRC_DIM_2)
@@ -1711,7 +2027,7 @@
     dst_addr += dst_plane_stride;
 }
 
-#endif /* defined(SRC_DIM_1) && defined(SRC_DIM_2) */
+#endif // defined(SRC_DIM_1) && defined(SRC_DIM_2)
 
 #define OUTPUT_ROW_4x4_5x5(out, tmp, comm_fact)                     \
     ({                                                              \
@@ -1733,7 +2049,7 @@
         out.s7 = tmp.s7 - tmp.s1 + 5.25f * tmp.s3 - 5.25f * tmp.s5; \
     })
 
-/** This OpenCL kernel computes the input transform when the kernel size is 5x5 and the output tile is 4x4
+/** This OpenCL kernel computes the input transform when the kernel size is 5x5 and the output tile is 4x4 when the data layout is NCHW
  *
  * @note The number of tiles in the x axis must be passed at compile time using -DNUM_TILES_X (i.e.-DNUM_TILES_X=5).
  * @note The pad left and pad top must be passed at compile time using -DPAD_LEFT and -DPAD_TOP (i.e.-DPAD_LEFT=1 and -DPAD_TOP=0).
@@ -1882,14 +2198,299 @@
     *((__global float *)(dst_addr + 63 * dst_stride_z)) = out7.s7;
 }
 
-#if defined(SRC_DIM_1) && defined(SRC_DIM_2)
-
-/** This OpenCL kernel computes the input transform when the kernel size is 5x5, the output tile is 4x4 and data layout is NHWC
+#if defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL)
+/** This OpenCL kernel computes the input transform when the kernel size is 3x1 and the output tile is 2x1
  *
  * @note The number of tiles in the x axis must be passed at compile time using -DNUM_TILES_X (i.e.-DNUM_TILES_X=5).
  * @note The pad left and pad top must be passed at compile time using -DPAD_LEFT and -DPAD_TOP (i.e.-DPAD_LEFT=1 and -DPAD_TOP=0).
- * @note Dimension one of the input tensor (width for NHWC data layout) must be passed at compile time using -DSRC_DIM1 (e.g. -DSRC_DIM_1=112)
- * @note Dimension two of the input tensor (height for NHWC data layout) must be passed at compile time using -DSRC_DIM2 (e.g. -DSRC_DIM_2=112)
+ * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=2
+ * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=1
+ * @note -DWINOGRAD_INPUT_TRANSFORM_HORIZONTAL has to be passed at compile time
+ *
+ * @param[in] src_ptr                           Pointer to the source image. Supported data types: F32
+ * @param[in] src_stride_x                      Stride of the source image in X dimension (in bytes)
+ * @param[in] src_step_x                        src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y                      Stride of the source image in Y dimension (in bytes)
+ * @param[in] src_step_y                        src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source image
+ * @param[in] src_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z                        src_stride_z * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_ptr                           Pointer to the destination tensor. Supported data types: as @p src_ptr
+ * @param[in] dst_stride_x                      Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] dst_step_x                        dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in] dst_step_y                        dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_stride_z                      Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in] dst_step_z                        dst_stride_z * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void winograd_input_transform_2x1_3x1_stepz1_nchw(
+    TENSOR3D_DECLARATION(src),
+    TENSOR3D_DECLARATION(dst))
+{
+    winograd_input_transform_2x2_3x3_stepz1_nchw(src_ptr,
+                                                 src_stride_x,
+                                                 src_step_x,
+                                                 src_stride_y,
+                                                 src_step_y,
+                                                 src_stride_z,
+                                                 src_step_z,
+                                                 src_offset_first_element_in_bytes,
+                                                 dst_ptr,
+                                                 dst_stride_x,
+                                                 dst_step_x,
+                                                 dst_stride_y,
+                                                 dst_step_y,
+                                                 dst_stride_z,
+                                                 dst_step_z,
+                                                 dst_offset_first_element_in_bytes);
+}
+
+/** This OpenCL kernel computes the input transform when the kernel size is 3x1, the output tile is 2x1 and the number of channels is multiple of 2
+ *
+ * @note The number of tiles in the x axis must be passed at compile time using -DNUM_TILES_X (i.e.-DNUM_TILES_X=5).
+ * @note The pad left and pad top must be passed at compile time using -DPAD_LEFT and -DPAD_TOP (i.e.-DPAD_LEFT=1 and -DPAD_TOP=0).
+ * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=2
+ * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=1
+ * @note -DWINOGRAD_INPUT_TRANSFORM_HORIZONTAL has to be passed at compile time
+ *
+ * @param[in] src_ptr                           Pointer to the source image. Supported data types: F32
+ * @param[in] src_stride_x                      Stride of the source image in X dimension (in bytes)
+ * @param[in] src_step_x                        src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y                      Stride of the source image in Y dimension (in bytes)
+ * @param[in] src_step_y                        src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source image
+ * @param[in] src_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z                        src_stride_z * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_ptr                           Pointer to the destination tensor. Supported data types: as @p src_ptr
+ * @param[in] dst_stride_x                      Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] dst_step_x                        dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in] dst_step_y                        dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_stride_z                      Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in] dst_step_z                        dst_stride_z * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void winograd_input_transform_2x1_3x1_stepz2_nchw(
+    TENSOR3D_DECLARATION(src),
+    TENSOR3D_DECLARATION(dst))
+{
+    winograd_input_transform_2x2_3x3_stepz2_nchw(src_ptr,
+                                                 src_stride_x,
+                                                 src_step_x,
+                                                 src_stride_y,
+                                                 src_step_y,
+                                                 src_stride_z,
+                                                 src_step_z,
+                                                 src_offset_first_element_in_bytes,
+                                                 dst_ptr,
+                                                 dst_stride_x,
+                                                 dst_step_x,
+                                                 dst_stride_y,
+                                                 dst_step_y,
+                                                 dst_stride_z,
+                                                 dst_step_z,
+                                                 dst_offset_first_element_in_bytes);
+}
+
+/** This OpenCL kernel computes the input transform when the kernel size is 3x1 and the output tile is 4x1
+ *
+ * @note The number of tiles in the x axis must be passed at compile time using -DNUM_TILES_X (i.e.-DNUM_TILES_X=5).
+ * @note The pad left and pad top must be passed at compile time using -DPAD_LEFT and -DPAD_TOP (i.e.-DPAD_LEFT=1 and -DPAD_TOP=0).
+ * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=4
+ * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=1
+ * @note -DWINOGRAD_INPUT_TRANSFORM_HORIZONTAL has to be passed at compile time
+ *
+ * @param[in] src_ptr                           Pointer to the source image. Supported data types: F32
+ * @param[in] src_stride_x                      Stride of the source image in X dimension (in bytes)
+ * @param[in] src_step_x                        src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y                      Stride of the source image in Y dimension (in bytes)
+ * @param[in] src_step_y                        src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source image
+ * @param[in] src_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z                        src_stride_z * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_ptr                           Pointer to the destination tensor. Supported data types: as @p src_ptr
+ * @param[in] dst_stride_x                      Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] dst_step_x                        dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in] dst_step_y                        dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_stride_z                      Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in] dst_step_z                        dst_stride_z * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void winograd_input_transform_4x1_3x1_stepz1_nchw(
+    TENSOR3D_DECLARATION(src),
+    TENSOR3D_DECLARATION(dst))
+{
+    winograd_input_transform_4x4_3x3_stepz1_nchw(src_ptr,
+                                                 src_stride_x,
+                                                 src_step_x,
+                                                 src_stride_y,
+                                                 src_step_y,
+                                                 src_stride_z,
+                                                 src_step_z,
+                                                 src_offset_first_element_in_bytes,
+                                                 dst_ptr,
+                                                 dst_stride_x,
+                                                 dst_step_x,
+                                                 dst_stride_y,
+                                                 dst_step_y,
+                                                 dst_stride_z,
+                                                 dst_step_z,
+                                                 dst_offset_first_element_in_bytes);
+}
+#endif // defined(WINOGRAD_INPUT_TRANSFORM_HORIZONTAL)
+
+#if defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
+/** This OpenCL kernel computes the input transform when the kernel size is 1x3 and the output tile is 1x2
+ *
+ * @note The number of tiles in the x axis must be passed at compile time using -DNUM_TILES_X (i.e.-DNUM_TILES_X=5).
+ * @note The pad left and pad top must be passed at compile time using -DPAD_LEFT and -DPAD_TOP (i.e.-DPAD_LEFT=1 and -DPAD_TOP=0).
+ * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=1
+ * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=2
+ * @note -DWINOGRAD_INPUT_TRANSFORM_VERTICAL has to be passed at compile time
+ *
+ * @param[in] src_ptr                           Pointer to the source image. Supported data types: F32
+ * @param[in] src_stride_x                      Stride of the source image in X dimension (in bytes)
+ * @param[in] src_step_x                        src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y                      Stride of the source image in Y dimension (in bytes)
+ * @param[in] src_step_y                        src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source image
+ * @param[in] src_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z                        src_stride_z * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_ptr                           Pointer to the destination tensor. Supported data types: as @p src_ptr
+ * @param[in] dst_stride_x                      Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] dst_step_x                        dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in] dst_step_y                        dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_stride_z                      Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in] dst_step_z                        dst_stride_z * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void winograd_input_transform_1x2_1x3_stepz1_nchw(
+    TENSOR3D_DECLARATION(src),
+    TENSOR3D_DECLARATION(dst))
+{
+    winograd_input_transform_2x2_3x3_stepz1_nchw(src_ptr,
+                                                 src_stride_x,
+                                                 src_step_x,
+                                                 src_stride_y,
+                                                 src_step_y,
+                                                 src_stride_z,
+                                                 src_step_z,
+                                                 src_offset_first_element_in_bytes,
+                                                 dst_ptr,
+                                                 dst_stride_x,
+                                                 dst_step_x,
+                                                 dst_stride_y,
+                                                 dst_step_y,
+                                                 dst_stride_z,
+                                                 dst_step_z,
+                                                 dst_offset_first_element_in_bytes);
+}
+
+/** This OpenCL kernel computes the input transform when the kernel size is 1x3, the output tile is 1x2 and the number of channels is multiple of 2
+ *
+ * @note The number of tiles in the x axis must be passed at compile time using -DNUM_TILES_X (i.e.-DNUM_TILES_X=5).
+ * @note The pad left and pad top must be passed at compile time using -DPAD_LEFT and -DPAD_TOP (i.e.-DPAD_LEFT=1 and -DPAD_TOP=0).
+ * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=1
+ * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=2
+ * @note -DWINOGRAD_INPUT_TRANSFORM_VERTICAL has to be passed at compile time
+ *
+ * @param[in] src_ptr                           Pointer to the source image. Supported data types: F32
+ * @param[in] src_stride_x                      Stride of the source image in X dimension (in bytes)
+ * @param[in] src_step_x                        src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y                      Stride of the source image in Y dimension (in bytes)
+ * @param[in] src_step_y                        src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source image
+ * @param[in] src_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z                        src_stride_z * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_ptr                           Pointer to the destination tensor. Supported data types: as @p src_ptr
+ * @param[in] dst_stride_x                      Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] dst_step_x                        dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in] dst_step_y                        dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_stride_z                      Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in] dst_step_z                        dst_stride_z * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void winograd_input_transform_1x2_1x3_stepz2_nchw(
+    TENSOR3D_DECLARATION(src),
+    TENSOR3D_DECLARATION(dst))
+{
+    winograd_input_transform_2x2_3x3_stepz2_nchw(src_ptr,
+                                                 src_stride_x,
+                                                 src_step_x,
+                                                 src_stride_y,
+                                                 src_step_y,
+                                                 src_stride_z,
+                                                 src_step_z,
+                                                 src_offset_first_element_in_bytes,
+                                                 dst_ptr,
+                                                 dst_stride_x,
+                                                 dst_step_x,
+                                                 dst_stride_y,
+                                                 dst_step_y,
+                                                 dst_stride_z,
+                                                 dst_step_z,
+                                                 dst_offset_first_element_in_bytes);
+}
+
+/** This OpenCL kernel computes the input transform when the kernel size is 1x3 and the output tile is 1x4
+ *
+ * @note The number of tiles in the x axis must be passed at compile time using -DNUM_TILES_X (i.e.-DNUM_TILES_X=5).
+ * @note The pad left and pad top must be passed at compile time using -DPAD_LEFT and -DPAD_TOP (i.e.-DPAD_LEFT=1 and -DPAD_TOP=0).
+ * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=1
+ * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=4
+ * @note -DWINOGRAD_INPUT_TRANSFORM_VERTICAL has to be passed at compile time
+ *
+ * @param[in] src_ptr                           Pointer to the source image. Supported data types: F32
+ * @param[in] src_stride_x                      Stride of the source image in X dimension (in bytes)
+ * @param[in] src_step_x                        src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y                      Stride of the source image in Y dimension (in bytes)
+ * @param[in] src_step_y                        src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes The offset of the first element in the source image
+ * @param[in] src_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z                        src_stride_z * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_ptr                           Pointer to the destination tensor. Supported data types: as @p src_ptr
+ * @param[in] dst_stride_x                      Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] dst_step_x                        dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in] dst_step_y                        dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_stride_z                      Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in] dst_step_z                        dst_stride_z * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void winograd_input_transform_1x4_1x3_stepz1_nchw(
+    TENSOR3D_DECLARATION(src),
+    TENSOR3D_DECLARATION(dst))
+{
+    winograd_input_transform_4x4_3x3_stepz1_nchw(src_ptr,
+                                                 src_stride_x,
+                                                 src_step_x,
+                                                 src_stride_y,
+                                                 src_step_y,
+                                                 src_stride_z,
+                                                 src_step_z,
+                                                 src_offset_first_element_in_bytes,
+                                                 dst_ptr,
+                                                 dst_stride_x,
+                                                 dst_step_x,
+                                                 dst_stride_y,
+                                                 dst_step_y,
+                                                 dst_stride_z,
+                                                 dst_step_z,
+                                                 dst_offset_first_element_in_bytes);
+}
+#endif // defined(WINOGRAD_INPUT_TRANSFORM_VERTICAL)
+
+#if defined(SRC_DIM_1) && defined(SRC_DIM_2)
+/** This OpenCL kernel computes the input transform when the kernel size is 5x5 and the output tile is 4x4 when the data layout is NHWC
+ *
+ * @note The number of tiles in the x axis must be passed at compile time using -DNUM_TILES_X (i.e.-DNUM_TILES_X=5).
+ * @note The pad left and pad top must be passed at compile time using -DPAD_LEFT and -DPAD_TOP (i.e.-DPAD_LEFT=1 and -DPAD_TOP=0).
+ * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=4
+ * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=4
  *
  * @param[in] src_ptr                           Pointer to the source image. Supported data types: F32
  * @param[in] src_stride_x                      Stride of the source image in X dimension (in bytes)
@@ -2150,12 +2751,16 @@
     *((__global float *)(dst_addr + 63 * dst_stride_z)) = out7.s7;
 }
 #endif // defined(SRC_DIM_1) && defined(SRC_DIM_2)
-#endif // defined(NUM_TILES_X) && defined(PAD_LEFT) && defined(PAD_TOP)
+#endif // defined(NUM_TILES_X) && defined(PAD_LEFT) && defined(PAD_TOP) && defined(OUTPUT_TILE_W) && defined(OUTPUT_TILE_H)
 
-#if defined(NUM_TILES_X)
-/** This OpenCL kernel performs Winograd output transform when the output tile is 2x2, the filter size 3x3 and the data layout is NCHW
+#if defined(NUM_TILES_X) && defined(OUTPUT_TILE_W) && defined(OUTPUT_TILE_H)
+/** This OpenCL kernel performs Winograd output transform when the output tile is 2x2/2x1 or 1x2, the filter size 3x3/3x1 or 1x3 and the data layout is NCHW
  *
  * @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16
+ * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=2
+ * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=2
+ * @note If this kernel is used to perform Winograd output transform 3x1, -DWINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL has to be passed at compile time
+ * @note If this kernel is used to perform Winograd output transform 1x3, -DWINOGRAD_OUTPUT_TRANSFORM_VERTICAL has to be passed at compile time
  *
  * @param[in]  src_ptr                           Pointer to the source tensor. Supported data types: F32
  * @param[in]  src_stride_x                      Stride of the source tensor in X dimension (in bytes)
@@ -2183,21 +2788,29 @@
 #endif // defined(HAS_BIAS)
 )
 {
-    // Each thread stores a 2x2 tile
+    // Each thread stores a 2x2/2x1 or 1x2 tile accordingly with the filter size
     Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src);
 
     const __global uchar *src_addr = tensor3D_offset(&src, 0, 0, 0);
 
-    // Load the values across the 16 channels to compose the 4x4 tile
+    // Load the values across the 16 or 4 channels to compose the 4x4 or 4x1 tile
     float d00 = *((__global float *)(src_addr + 0 * src_stride_z));
     float d01 = *((__global float *)(src_addr + 1 * src_stride_z));
     float d02 = *((__global float *)(src_addr + 2 * src_stride_z));
     float d03 = *((__global float *)(src_addr + 3 * src_stride_z));
 
-    float d10 = *((__global float *)(src_addr + 4 * src_stride_z));
-    float d11 = *((__global float *)(src_addr + 5 * src_stride_z));
-    float d12 = *((__global float *)(src_addr + 6 * src_stride_z));
-    float d13 = *((__global float *)(src_addr + 7 * src_stride_z));
+#if defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
+    // Compute the 2x1 or 1x2 output tile
+    // out00 = d00 + d01 + d02
+    // out01 = d01 - d02 - d03
+
+    float out00 = d00 + d01 + d02;
+    float out01 = d01 - d02 - d03;
+#else  // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
+    float  d10 = *((__global float *)(src_addr + 4 * src_stride_z));
+    float  d11 = *((__global float *)(src_addr + 5 * src_stride_z));
+    float  d12 = *((__global float *)(src_addr + 6 * src_stride_z));
+    float  d13 = *((__global float *)(src_addr + 7 * src_stride_z));
 
     float d20 = *((__global float *)(src_addr + 8 * src_stride_z));
     float d21 = *((__global float *)(src_addr + 9 * src_stride_z));
@@ -2229,10 +2842,11 @@
     out01 += k0 - k1 - (d03 + d23);
     out10 += -d20 - d30 + k2 + k3;
     out11 += k2 - k3 + d23 + d33;
+#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
 
     int y_in  = get_global_id(1);
-    int x_out = (y_in % NUM_TILES_X) * 2;
-    int y_out = (y_in / NUM_TILES_X) * 2;
+    int x_out = (y_in % NUM_TILES_X) * OUTPUT_TILE_W;
+    int y_out = (y_in / NUM_TILES_X) * OUTPUT_TILE_H;
     int z_out = get_global_id(0);
 
 #if defined(HAS_BIAS)
@@ -2243,21 +2857,37 @@
 
     out00 += (float)b;
     out01 += (float)b;
+#endif // defined(HAS_BIAS)
+
+    // Get output address
+    __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_out * sizeof(float) + y_out * dst_stride_y + z_out * dst_stride_z;
+
+    // Store the output tile
+#if defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
+    *((__global float *)(dst_addr + 0 * dst_stride_y)) = out00;
+    *((__global float *)(dst_addr + 1 * dst_stride_y)) = out01;
+#else  // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
+    vstore2((float2)(out00, out01), 0, (__global float *)(dst_addr + 0 * dst_stride_y));
+#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
+
+#if !defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
+#if defined(HAS_BIAS)
+    // Add bias
     out10 += (float)b;
     out11 += (float)b;
 #endif // defined(HAS_BIAS)
 
-    // Get output address
-    __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_out * dst_stride_x + y_out * dst_stride_y + z_out * dst_stride_z;
-
-    // Store the 2x2 output tile
-    vstore2((float2)(out00, out01), 0, (__global float *)(dst_addr + 0 * dst_stride_y));
     vstore2((float2)(out10, out11), 0, (__global float *)(dst_addr + 1 * dst_stride_y));
+#endif // !defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
 }
 
 /** This OpenCL kernel performs Winograd output transform when the output tile is 4x4, the filter size 3x3 and the data layout is NCHW
  *
  * @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16
+ * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=4
+ * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=4
+ * @note If this kernel is used to perform Winograd output transform 3x1, -DWINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL has to be passed at compile time
+ * @note If this kernel is used to perform Winograd output transform 1x3, -DWINOGRAD_OUTPUT_TRANSFORM_VERTICAL has to be passed at compile time
  *
  * @param[in]  src_ptr                           Pointer to the source tensor. Supported data types: F32
  * @param[in]  src_stride_x                      Stride of the source tensor in X dimension (in bytes)
@@ -2285,12 +2915,12 @@
 #endif // defined(HAS_BIAS)
 )
 {
-    // Each thread stores a 4x4 tile
+    // Each thread stores a 4x4/4x1 or 1x4 tile
     Tensor3D src = CONVERT_TO_TENSOR3D_STRUCT(src);
 
     const __global uchar *src_addr = tensor3D_offset(&src, 0, 0, 0);
 
-    // Load the values across the 36 channels to compose the 6x6 tile
+    // Load the values across the channels to compose the 6x6 or 6x1 tile
     float d00 = *((__global float *)(src_addr + 0 * src_stride_z));
     float d01 = *((__global float *)(src_addr + 1 * src_stride_z));
     float d02 = *((__global float *)(src_addr + 2 * src_stride_z));
@@ -2298,6 +2928,13 @@
     float d04 = *((__global float *)(src_addr + 4 * src_stride_z));
     float d05 = *((__global float *)(src_addr + 5 * src_stride_z));
 
+#if defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
+    // Compute out00, out01, out02 and out03
+    float out00 = d00 + d01 + d02 + d03 + d04;
+    float out01 = d01 - d02 + 2.0f * d03 - 2.0f * d04;
+    float out02 = d01 + d02 + 4.0f * d03 + 4.0f * d04;
+    float out03 = d01 - d02 + 8.0f * d03 - 8.0f * d04 + d05;
+#else  // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
     float d10 = *((__global float *)(src_addr + 6 * src_stride_z));
     float d11 = *((__global float *)(src_addr + 7 * src_stride_z));
     float d12 = *((__global float *)(src_addr + 8 * src_stride_z));
@@ -2388,10 +3025,11 @@
     out31 += k1 - d12 + d22 - 8.0f * d32 + 8.0f * d42 - d52;
     out32 += 4.0f * k0 + d12 - d22 + 8.0f * d32 - 8.0f * d42 + d52;
     out33 += 4.0f * k1 - d12 + d15 + d22 - d25 - 8.0f * d32 + 8.0f * d35 + 8.0f * d42 - 8.0f * d45 - d52 + d55;
+#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
 
     int y_in  = get_global_id(1);
-    int x_out = (y_in % NUM_TILES_X) * 4;
-    int y_out = (y_in / NUM_TILES_X) * 4;
+    int x_out = (y_in % NUM_TILES_X) * OUTPUT_TILE_W;
+    int y_out = (y_in / NUM_TILES_X) * OUTPUT_TILE_H;
     int z_out = get_global_id(0);
 
 #if defined(HAS_BIAS)
@@ -2404,7 +3042,24 @@
     out01 += (float)b;
     out02 += (float)b;
     out03 += (float)b;
+#endif // defined(HAS_BIAS)
 
+    // Get output address
+    __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_out * sizeof(float) + y_out * dst_stride_y + z_out * dst_stride_z;
+
+    // Store the output tile
+#if defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
+    *((__global float *)(dst_addr + 0 * dst_stride_y)) = out00;
+    *((__global float *)(dst_addr + 1 * dst_stride_y)) = out01;
+    *((__global float *)(dst_addr + 2 * dst_stride_y)) = out02;
+    *((__global float *)(dst_addr + 3 * dst_stride_y)) = out03;
+#else  // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
+    vstore4((float4)(out00, out01, out02, out03), 0, (__global float *)(dst_addr + 0 * dst_stride_y));
+#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
+
+#if !defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
+#if defined(HAS_BIAS)
+    // Add bias
     out10 += (float)b;
     out11 += (float)b;
     out12 += (float)b;
@@ -2419,19 +3074,253 @@
     out31 += (float)b;
     out32 += (float)b;
     out33 += (float)b;
-
 #endif // defined(HAS_BIAS)
-
-    // Get output address
-    __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_out * dst_stride_x + y_out * dst_stride_y + z_out * dst_stride_z;
-
-    // Store the 4x4 output tile
-    vstore4((float4)(out00, out01, out02, out03), 0, (__global float *)(dst_addr + 0 * dst_stride_y));
     vstore4((float4)(out10, out11, out12, out13), 0, (__global float *)(dst_addr + 1 * dst_stride_y));
     vstore4((float4)(out20, out21, out22, out23), 0, (__global float *)(dst_addr + 2 * dst_stride_y));
     vstore4((float4)(out30, out31, out32, out33), 0, (__global float *)(dst_addr + 3 * dst_stride_y));
+#endif // !defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
 }
 
+#if defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL)
+/** This OpenCL kernel performs Winograd output transform when the output tile is 2x1, the filter size 3x1 and the data layout is NCHW
+ *
+ * @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16
+ * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=2
+ * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=1
+ * @note -DWINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL has to be passed at compile time
+ *
+ * @param[in]  src_ptr                           Pointer to the source tensor. Supported data types: F32
+ * @param[in]  src_stride_x                      Stride of the source tensor in X dimension (in bytes)
+ * @param[in]  src_step_x                        src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  src_stride_y                      Stride of the source tensor in Y dimension (in bytes)
+ * @param[in]  src_step_y                        src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  src_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  src_step_z                        src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  src_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] dst_ptr                           Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in]  dst_stride_x                      Stride of the destination tensor in X dimension (in bytes)
+ * @param[in]  dst_step_x                        dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  dst_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in]  dst_step_y                        dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  src_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  src_step_z                        src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void winograd_output_transform_2x1_3x1_nchw(
+    TENSOR3D_DECLARATION(src),
+    TENSOR3D_DECLARATION(dst)
+#if defined(HAS_BIAS)
+    ,
+    VECTOR_DECLARATION(bias)
+#endif // defined(HAS_BIAS)
+)
+{
+    winograd_output_transform_2x2_3x3_nchw(src_ptr,
+                                           src_stride_x,
+                                           src_step_x,
+                                           src_stride_y,
+                                           src_step_y,
+                                           src_stride_z,
+                                           src_step_z,
+                                           src_offset_first_element_in_bytes,
+                                           dst_ptr,
+                                           dst_stride_x,
+                                           dst_step_x,
+                                           dst_stride_y,
+                                           dst_step_y,
+                                           dst_stride_z,
+                                           dst_step_z,
+                                           dst_offset_first_element_in_bytes
+#if defined(HAS_BIAS)
+                                           ,
+                                           bias_ptr,
+                                           bias_stride_x,
+                                           bias_step_x,
+                                           bias_offset_first_element_in_bytes
+#endif // defined(HAS_BIAS)
+                                          );
+}
+
+/** This OpenCL kernel performs Winograd output transform when the output tile is 4x1, the filter size 3x1 and the data layout is NCHW
+ *
+ * @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16
+ * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=4
+ * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=1
+ * @note -DWINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL has to be passed at compile time
+ *
+ * @param[in]  src_ptr                           Pointer to the source tensor. Supported data types: F32
+ * @param[in]  src_stride_x                      Stride of the source tensor in X dimension (in bytes)
+ * @param[in]  src_step_x                        src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  src_stride_y                      Stride of the source tensor in Y dimension (in bytes)
+ * @param[in]  src_step_y                        src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  src_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  src_step_z                        src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  src_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] dst_ptr                           Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in]  dst_stride_x                      Stride of the destination tensor in X dimension (in bytes)
+ * @param[in]  dst_step_x                        dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  dst_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in]  dst_step_y                        dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  src_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  src_step_z                        src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void winograd_output_transform_4x1_3x1_nchw(
+    TENSOR3D_DECLARATION(src),
+    TENSOR3D_DECLARATION(dst)
+#if defined(HAS_BIAS)
+    ,
+    VECTOR_DECLARATION(bias)
+#endif // defined(HAS_BIAS)
+)
+{
+    winograd_output_transform_4x4_3x3_nchw(src_ptr,
+                                           src_stride_x,
+                                           src_step_x,
+                                           src_stride_y,
+                                           src_step_y,
+                                           src_stride_z,
+                                           src_step_z,
+                                           src_offset_first_element_in_bytes,
+                                           dst_ptr,
+                                           dst_stride_x,
+                                           dst_step_x,
+                                           dst_stride_y,
+                                           dst_step_y,
+                                           dst_stride_z,
+                                           dst_step_z,
+                                           dst_offset_first_element_in_bytes
+#if defined(HAS_BIAS)
+                                           ,
+                                           bias_ptr,
+                                           bias_stride_x,
+                                           bias_step_x,
+                                           bias_offset_first_element_in_bytes
+#endif // defined(HAS_BIAS)
+                                          );
+}
+#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL)
+
+#if defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
+/** This OpenCL kernel performs Winograd output transform when the output tile is 1x2, the filter size 1x3 and the data layout is NCHW
+ *
+ * @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16
+ * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=1
+ * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=2
+ * @note -DWINOGRAD_OUTPUT_TRANSFORM_VERTICAL has to be passed at compile time
+ *
+ * @param[in]  src_ptr                           Pointer to the source tensor. Supported data types: F32
+ * @param[in]  src_stride_x                      Stride of the source tensor in X dimension (in bytes)
+ * @param[in]  src_step_x                        src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  src_stride_y                      Stride of the source tensor in Y dimension (in bytes)
+ * @param[in]  src_step_y                        src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  src_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  src_step_z                        src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  src_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] dst_ptr                           Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in]  dst_stride_x                      Stride of the destination tensor in X dimension (in bytes)
+ * @param[in]  dst_step_x                        dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  dst_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in]  dst_step_y                        dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  src_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  src_step_z                        src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void winograd_output_transform_1x2_1x3_nchw(
+    TENSOR3D_DECLARATION(src),
+    TENSOR3D_DECLARATION(dst)
+#if defined(HAS_BIAS)
+    ,
+    VECTOR_DECLARATION(bias)
+#endif // defined(HAS_BIAS)
+)
+{
+    winograd_output_transform_2x2_3x3_nchw(src_ptr,
+                                           src_stride_x,
+                                           src_step_x,
+                                           src_stride_y,
+                                           src_step_y,
+                                           src_stride_z,
+                                           src_step_z,
+                                           src_offset_first_element_in_bytes,
+                                           dst_ptr,
+                                           dst_stride_x,
+                                           dst_step_x,
+                                           dst_stride_y,
+                                           dst_step_y,
+                                           dst_stride_z,
+                                           dst_step_z,
+                                           dst_offset_first_element_in_bytes
+#if defined(HAS_BIAS)
+                                           ,
+                                           bias_ptr,
+                                           bias_stride_x,
+                                           bias_step_x,
+                                           bias_offset_first_element_in_bytes
+#endif // defined(HAS_BIAS)
+                                          );
+}
+
+/** This OpenCL kernel performs Winograd output transform when the output tile is 1x4, the filter size 1x3 and the data layout is NCHW
+ *
+ * @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16
+ * @note The width of the output tile must be passed at compile time using -DOUTPUT_TILE_W: e.g. -DOUTPUT_TILE_W=1
+ * @note The height of the output tile must be passed at compile time using -DOUTPUT_TILE_H: e.g. -DOUTPUT_TILE_H=4
+ * @note -DWINOGRAD_OUTPUT_TRANSFORM_VERTICAL has to be passed at compile time
+ *
+ * @param[in]  src_ptr                           Pointer to the source tensor. Supported data types: F32
+ * @param[in]  src_stride_x                      Stride of the source tensor in X dimension (in bytes)
+ * @param[in]  src_step_x                        src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  src_stride_y                      Stride of the source tensor in Y dimension (in bytes)
+ * @param[in]  src_step_y                        src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  src_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  src_step_z                        src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  src_offset_first_element_in_bytes The offset of the first element in the source tensor
+ * @param[out] dst_ptr                           Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in]  dst_stride_x                      Stride of the destination tensor in X dimension (in bytes)
+ * @param[in]  dst_step_x                        dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  dst_stride_y                      Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in]  dst_step_y                        dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  src_stride_z                      Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  src_step_z                        src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  dst_offset_first_element_in_bytes The offset of the first element in the destination tensor
+ */
+__kernel void winograd_output_transform_1x4_1x3_nchw(
+    TENSOR3D_DECLARATION(src),
+    TENSOR3D_DECLARATION(dst)
+#if defined(HAS_BIAS)
+    ,
+    VECTOR_DECLARATION(bias)
+#endif // defined(HAS_BIAS)
+)
+{
+    winograd_output_transform_4x4_3x3_nchw(src_ptr,
+                                           src_stride_x,
+                                           src_step_x,
+                                           src_stride_y,
+                                           src_step_y,
+                                           src_stride_z,
+                                           src_step_z,
+                                           src_offset_first_element_in_bytes,
+                                           dst_ptr,
+                                           dst_stride_x,
+                                           dst_step_x,
+                                           dst_stride_y,
+                                           dst_step_y,
+                                           dst_stride_z,
+                                           dst_step_z,
+                                           dst_offset_first_element_in_bytes
+#if defined(HAS_BIAS)
+                                           ,
+                                           bias_ptr,
+                                           bias_stride_x,
+                                           bias_step_x,
+                                           bias_offset_first_element_in_bytes
+#endif // defined(HAS_BIAS)
+                                          );
+}
+#endif // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
+
 /** This OpenCL kernel performs Winograd output transform when the output tile is 4x4, the filter size 3x3 and the data layout is NHWC
  *
  * @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16
@@ -2815,7 +3704,7 @@
     *(__global float *)(dst_addr + 3 * dst_stride_x + 3 * dst_stride_y) = out_col3.s3;
 }
 
-/** This OpenCL kernel performs Winograd output transform when the output tile is 4x4, the filter size 5x5 and the data format is NHWC
+/** This OpenCL kernel performs Winograd output transform when the output tile is 4x4, the filter size 5x5 and the data layout is NHWC
  *
  * @note The number of tiles along the X direction must be passed at compile time using -DNUM_TILES_X: e.g. -DNUM_TILES_X=16
  *
@@ -2990,4 +3879,4 @@
     *(__global float *)(dst_ptr + mult_y.s0 * 2 * dst_stride_y + offset.s3) = out_col2.s3;
     *(__global float *)(dst_ptr + mult_y.s0 * 3 * dst_stride_y + offset.s3) = out_col3.s3;
 }
-#endif // defined(NUM_TILES_X)
+#endif // defined(NUM_TILES_X) && defined(OUTPUT_TILE_W) && defined(OUTPUT_TILE_H)
diff --git a/src/core/CL/kernels/CLWinogradFilterTransformKernel.cpp b/src/core/CL/kernels/CLWinogradFilterTransformKernel.cpp
index 779df63..e6c713e 100644
--- a/src/core/CL/kernels/CLWinogradFilterTransformKernel.cpp
+++ b/src/core/CL/kernels/CLWinogradFilterTransformKernel.cpp
@@ -25,7 +25,6 @@
 
 #include "arm_compute/core/AccessWindowStatic.h"
 #include "arm_compute/core/CL/CLHelpers.h"
-#include "arm_compute/core/CL/CLHelpers.h"
 #include "arm_compute/core/CL/CLKernelLibrary.h"
 #include "arm_compute/core/CL/ICLTensor.h"
 #include "arm_compute/core/Helpers.h"
@@ -54,12 +53,7 @@
     const size_t idx_w = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::WIDTH);
     const size_t idx_h = get_data_layout_dimension_index(input->data_layout(), DataLayoutDimension::HEIGHT);
 
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(kernel_size != Size2D(3U, 3U) && kernel_size != Size2D(5U, 5U), "Winograd filter transform only supports 3x3 and 5x5 kernels");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_layout() == DataLayout::NHWC && output_tile_size != Size2D(4U, 4U), "Winograd filter transform only supports 4x4 output tile for NHWC data layout");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(kernel_size == Size2D(3U, 3U) && output_tile_size != Size2D(2U, 2U)
-                                    && output_tile_size != Size2D(4U, 4U),
-                                    "Winograd filter transform only supports 2x2 or 4x4 output tile for 3x3 kernels");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(kernel_size == Size2D(5U, 5U) && output_tile_size != Size2D(4U, 4U), "Winograd filter transform only supports 4x4 output tile for 5x5 kernels");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(!cl_winograd_convolution_layer_supported(output_tile_size, kernel_size, input->data_layout()), "Winograd filter transform not supported");
     ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(idx_w) != kernel_size.width || input->dimension(idx_h) != kernel_size.height);
     ARM_COMPUTE_RETURN_ERROR_ON(input->num_dimensions() > 4);
 
@@ -115,6 +109,8 @@
     // Set build options
     CLBuildOptions build_opts;
     build_opts.add_option("-DSRC_DIM_Z=" + support::cpp11::to_string(input->info()->dimension(2)));
+    build_opts.add_option_if(winograd_info.kernel_size.height == 1, "-DWINOGRAD_FILTER_TRANSFORM_HORIZONTAL");
+    build_opts.add_option_if(winograd_info.kernel_size.width == 1, "-DWINOGRAD_FILTER_TRANSFORM_VERTICAL");
 
     const Size2D kernel_size      = winograd_info.kernel_size;
     const Size2D output_tile_size = winograd_info.output_tile_size;
diff --git a/src/core/CL/kernels/CLWinogradInputTransformKernel.cpp b/src/core/CL/kernels/CLWinogradInputTransformKernel.cpp
index 274c9e7..bb484af 100644
--- a/src/core/CL/kernels/CLWinogradInputTransformKernel.cpp
+++ b/src/core/CL/kernels/CLWinogradInputTransformKernel.cpp
@@ -30,6 +30,7 @@
 #include "arm_compute/core/Error.h"
 #include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/Types.h"
+#include "arm_compute/core/Utils.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "support/ToolchainSupport.h"
 
@@ -45,12 +46,8 @@
     const Size2D        output_tile_size = winograd_info.output_tile_size;
     const Size2D        kernel_size      = winograd_info.kernel_size;
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(conv_info.stride().first != 1 || conv_info.stride().second != 1, "Winograd input transform only supports unit strides");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(kernel_size != Size2D(3U, 3U) && kernel_size != Size2D(5U, 5U), "Winograd input transform only supports 3x3 and 5x5 kernels");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_layout() == DataLayout::NHWC && output_tile_size != Size2D(4U, 4U), "Winograd input transform only supports 4x4 output tile for NHWC data layout");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(kernel_size == Size2D(3U, 3U) && output_tile_size != Size2D(2U, 2U)
-                                    && output_tile_size != Size2D(4U, 4U),
-                                    "Winograd input transform only supports 2x2 or 4x4 output tile for 3x3 kernels");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(kernel_size == Size2D(5U, 5U) && output_tile_size != Size2D(4U, 4U), "Winograd input transform only supports 4x4 output tile for 5x5 kernels");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(!cl_winograd_convolution_layer_supported(output_tile_size, kernel_size, input->data_layout()), "Winograd input transform not supported");
+
     ARM_COMPUTE_UNUSED(conv_info);
     ARM_COMPUTE_UNUSED(output_tile_size);
     ARM_COMPUTE_UNUSED(kernel_size);
@@ -131,8 +128,6 @@
     const int num_elements_x = input->info()->dimension(idx_w) - (kernel_size.width - 1) + conv_info.pad_left() + conv_info.pad_right();
     const int num_elements_y = input->info()->dimension(idx_h) - (kernel_size.height - 1) + conv_info.pad_top() + conv_info.pad_bottom();
 
-    _input  = input;
-    _output = output;
     if(input->info()->data_layout() == DataLayout::NCHW)
     {
         // Check if we need to extend the right or bottom border
@@ -145,8 +140,17 @@
     {
         _border_size = BorderSize(1U, 0U, 1U, 0);
     }
-    _num_tiles_x = std::ceil(num_elements_x / static_cast<float>(output_tile_size.width));
-    _num_tiles_y = std::ceil(num_elements_y / static_cast<float>(output_tile_size.height));
+
+    // Compute the number of output tiles along the x and y direction of size "output_tile_size"
+    const Size2D num_tiles = compute_winograd_convolution_tiles(Size2D(input->info()->dimension(idx_w), input->info()->dimension(idx_h)),
+                                                                kernel_size,
+                                                                output_tile_size,
+                                                                conv_info);
+
+    _input       = input;
+    _output      = output;
+    _num_tiles_x = num_tiles.width;
+    _num_tiles_y = num_tiles.height;
 
     const TensorShape output_shape = misc::shape_calculator::compute_winograd_input_transform_shape(*input->info(), winograd_info);
 
@@ -159,6 +163,10 @@
     build_opts.add_option("-DNUM_TILES_X=" + support::cpp11::to_string(_num_tiles_x));
     build_opts.add_option("-DPAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left()));
     build_opts.add_option("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top()));
+    build_opts.add_option("-DOUTPUT_TILE_W=" + support::cpp11::to_string(output_tile_size.width));
+    build_opts.add_option("-DOUTPUT_TILE_H=" + support::cpp11::to_string(output_tile_size.height));
+    build_opts.add_option_if(winograd_info.kernel_size.height == 1, "-DWINOGRAD_INPUT_TRANSFORM_HORIZONTAL");
+    build_opts.add_option_if(winograd_info.kernel_size.width == 1, "-DWINOGRAD_INPUT_TRANSFORM_VERTICAL");
 
     if(input->info()->data_layout() == DataLayout::NHWC)
     {
@@ -169,8 +177,11 @@
     // Create kernel
     std::string kernel_name = "winograd_input_transform_" + output_tile_size.to_string() + "_" + kernel_size.to_string();
 
+    // Get the maximum dimension from the tile size
+    const unsigned int tile_max_dim = std::max(output_tile_size.width, output_tile_size.height);
+
     // Check optimized kernel if output_dims == 2x2
-    if(output_tile_size == Size2D(2U, 2U))
+    if(tile_max_dim == 2)
     {
         _step_z = (_input->info()->dimension(2) % 2) != 0 ? 1 : 2;
     }
@@ -199,6 +210,8 @@
     _config_id += support::cpp11::to_string(conv_info.pad_left());
     _config_id += "_";
     _config_id += support::cpp11::to_string(conv_info.pad_top());
+    _config_id += "_";
+    _config_id += lower_string(string_from_data_layout(input->info()->data_layout()));
 }
 
 Status CLWinogradInputTransformKernel::validate(const ITensorInfo *input, const ITensorInfo *output, const WinogradInfo &winograd_info)
diff --git a/src/core/CL/kernels/CLWinogradOutputTransformKernel.cpp b/src/core/CL/kernels/CLWinogradOutputTransformKernel.cpp
index 980498c..40d5f65 100644
--- a/src/core/CL/kernels/CLWinogradOutputTransformKernel.cpp
+++ b/src/core/CL/kernels/CLWinogradOutputTransformKernel.cpp
@@ -55,20 +55,19 @@
     const Size2D        output_tile_size = winograd_info.output_tile_size;
     const Size2D        kernel_size      = winograd_info.kernel_size;
     const Size2D        input_dimensions = winograd_info.input_dimensions;
+    const unsigned int  num_channels     = (winograd_info.kernel_size.width + winograd_info.output_tile_size.width - 1) * (winograd_info.kernel_size.height + winograd_info.output_tile_size.height - 1);
 
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(kernel_size != Size2D(3U, 3U) && kernel_size != Size2D(5U, 5U), "Only 3x3 and 5x5 kernels are supported");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->data_layout() == DataLayout::NHWC && output_tile_size != Size2D(4U, 4U), "Only 4x4 output tile supported for NHWC data layout");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(kernel_size == Size2D(3U, 3U) && output_tile_size == Size2D(2U, 2U) && input->dimension(2) != 16, "Wrong number of batches");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(kernel_size == Size2D(3U, 3U) && output_tile_size == Size2D(4U, 4U) && input->dimension(2) != 36, "Wrong number of batches");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(kernel_size == Size2D(5U, 5U) && output_tile_size == Size2D(4U, 4U) && input->dimension(2) != 64, "Wrong number of batches");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(!cl_winograd_convolution_layer_supported(output_tile_size, kernel_size, winograd_info.output_data_layout), "Winograd output transform not supported");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(input->dimension(2) != num_channels, "Wrong number of channels");
 
     // Compute number of elements to process in the X and Y direction
-    const int num_elements_x = input_dimensions.width - (kernel_size.width - 1) + conv_info.pad_left() + conv_info.pad_right();
-    const int num_elements_y = input_dimensions.height - (kernel_size.height - 1) + conv_info.pad_top() + conv_info.pad_bottom();
-    const int num_tiles_x    = std::ceil(num_elements_x / static_cast<float>(output_tile_size.width));
-    const int num_tiles_y    = std::ceil(num_elements_y / static_cast<float>(output_tile_size.height));
+    // Compute the number of output tiles along the x and y direction of size "output_tile_size"
+    const Size2D num_tiles = compute_winograd_convolution_tiles(input_dimensions,
+                                                                kernel_size,
+                                                                output_tile_size,
+                                                                conv_info);
 
-    ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(1) != static_cast<unsigned int>((num_tiles_x * num_tiles_y)));
+    ARM_COMPUTE_RETURN_ERROR_ON(input->dimension(1) != static_cast<unsigned int>((num_tiles.area())));
 
     if(bias != nullptr)
     {
@@ -150,13 +149,21 @@
     const Size2D        kernel_size      = winograd_info.kernel_size;
     const Size2D        output_tile_size = winograd_info.output_tile_size;
     const PadStrideInfo conv_info        = winograd_info.convolution_info;
-    const int           num_elements_x   = input_dimensions.width - (kernel_size.width - 1) + conv_info.pad_left() + conv_info.pad_right();
-    const int           num_tiles_x      = std::ceil(num_elements_x / static_cast<float>(output_tile_size.width));
+
+    // Compute the number of output tiles along the x and y direction of size "output_tile_size"
+    const Size2D num_tiles = compute_winograd_convolution_tiles(input_dimensions,
+                                                                kernel_size,
+                                                                output_tile_size,
+                                                                conv_info);
 
     // Set build options
     CLBuildOptions build_opts;
     build_opts.add_option_if(_bias != nullptr, std::string("-DHAS_BIAS"));
-    build_opts.add_option("-DNUM_TILES_X=" + support::cpp11::to_string(num_tiles_x));
+    build_opts.add_option("-DNUM_TILES_X=" + support::cpp11::to_string(num_tiles.width));
+    build_opts.add_option("-DOUTPUT_TILE_W=" + support::cpp11::to_string(output_tile_size.width));
+    build_opts.add_option("-DOUTPUT_TILE_H=" + support::cpp11::to_string(output_tile_size.height));
+    build_opts.add_option_if(winograd_info.kernel_size.height == 1, "-DWINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL");
+    build_opts.add_option_if(winograd_info.kernel_size.width == 1, "-DWINOGRAD_OUTPUT_TRANSFORM_VERTICAL");
 
     // Create kernel
     std::string kernel_name = "winograd_output_transform_" + output_tile_size.to_string() + "_" + kernel_size.to_string() + "_" + lower_string(string_from_data_layout(winograd_info.output_data_layout));
@@ -179,6 +186,8 @@
     _config_id += support::cpp11::to_string(output->info()->dimension(0));
     _config_id += "_";
     _config_id += support::cpp11::to_string(output->info()->dimension(1));
+    _config_id += "_";
+    _config_id += lower_string(string_from_data_layout(winograd_info.output_data_layout));
 }
 
 Status CLWinogradOutputTransformKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info)
diff --git a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp
index 49753ad..11714fa 100644
--- a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp
@@ -37,11 +37,27 @@
 {
     Size2D output_tile = Size2D{};
 
-    if(kernel_dims == Size2D(3U, 3U))
+    const unsigned int kernel_max_dim = std::max(kernel_dims.width, kernel_dims.height);
+
+    // Check if the input spatial dimensions are smaller than 4
+    const bool is_input_lt4 = (input_dims.width <= 4 && input_dims.height <= 4);
+
+    if(kernel_max_dim == 3U)
     {
-        output_tile = (input_dims.width <= 4 && input_dims.height <= 4) ? Size2D(2U, 2U) : Size2D(4U, 4U);
+        if(kernel_dims == Size2D(3U, 3U))
+        {
+            output_tile = is_input_lt4 ? Size2D(2U, 2U) : Size2D(4U, 4U);
+        }
+        else if(kernel_dims == Size2D(3U, 1U))
+        {
+            output_tile = is_input_lt4 ? Size2D(2U, 1U) : Size2D(4U, 1U);
+        }
+        else
+        {
+            output_tile = is_input_lt4 ? Size2D(1U, 2U) : Size2D(1U, 4U);
+        }
     }
-    else if(kernel_dims == Size2D(5U, 5U))
+    else if(kernel_max_dim == 5U)
     {
         output_tile = Size2D(4U, 4U);
     }
diff --git a/tests/datasets/LargeConvolutionLayerDataset.h b/tests/datasets/LargeConvolutionLayerDataset.h
index 36b3d60..ae25c8c 100644
--- a/tests/datasets/LargeConvolutionLayerDataset.h
+++ b/tests/datasets/LargeConvolutionLayerDataset.h
@@ -59,6 +59,50 @@
     }
 };
 
+class LargeWinogradConvolutionLayer3x1Dataset final : public ConvolutionLayerDataset
+{
+public:
+    LargeWinogradConvolutionLayer3x1Dataset()
+    {
+        // Kernel size 3
+        // Batch size 1
+        add_config(TensorShape(224U, 222U, 64U), TensorShape(3U, 1U, 64U, 64U), TensorShape(64U), TensorShape(224U, 222U, 64U), PadStrideInfo(1, 1, 1, 0));
+        add_config(TensorShape(112U, 113U, 64U), TensorShape(3U, 1U, 64U, 128U), TensorShape(128U), TensorShape(112U, 113U, 128U), PadStrideInfo(1, 1, 1, 0));
+        add_config(TensorShape(112U, 112U, 128U), TensorShape(3U, 1U, 128U, 129U), TensorShape(129U), TensorShape(112U, 112U, 129U), PadStrideInfo(1, 1, 1, 0));
+        add_config(TensorShape(53U, 56U, 125U), TensorShape(3U, 1U, 125U, 256U), TensorShape(256U), TensorShape(51U, 56U, 256U), PadStrideInfo(1, 1, 0, 0));
+        add_config(TensorShape(56U, 56U, 256U), TensorShape(3U, 1U, 256U, 256U), TensorShape(256U), TensorShape(56U, 56U, 256U), PadStrideInfo(1, 1, 1, 0));
+        add_config(TensorShape(28U, 28U, 257U), TensorShape(3U, 1U, 257U, 512U), TensorShape(512U), TensorShape(26U, 28U, 512U), PadStrideInfo(1, 1, 0, 0));
+        add_config(TensorShape(28U, 28U, 512U), TensorShape(3U, 1U, 512U, 512U), TensorShape(512U), TensorShape(28U, 28U, 512U), PadStrideInfo(1, 1, 1, 0));
+        add_config(TensorShape(14U, 14U, 512U), TensorShape(3U, 1U, 512U, 512U), TensorShape(512U), TensorShape(12U, 14U, 512U), PadStrideInfo(1, 1, 0, 0));
+        // Batch size 3, 2 and 4
+        add_config(TensorShape(224U, 222U, 64U, 3U), TensorShape(3U, 1U, 64U, 64U), TensorShape(64U), TensorShape(224U, 222U, 64U, 3U), PadStrideInfo(1, 1, 1, 0));
+        add_config(TensorShape(112U, 113U, 64U, 2U), TensorShape(3U, 1U, 64U, 128U), TensorShape(128U), TensorShape(110U, 113U, 128U, 2U), PadStrideInfo(1, 1, 0, 0));
+        add_config(TensorShape(111U, 112U, 127U, 4U), TensorShape(3U, 1U, 127U, 128U), TensorShape(128U), TensorShape(111U, 112U, 128U, 4U), PadStrideInfo(1, 1, 1, 0));
+    }
+};
+
+class LargeWinogradConvolutionLayer1x3Dataset final : public ConvolutionLayerDataset
+{
+public:
+    LargeWinogradConvolutionLayer1x3Dataset()
+    {
+        // Kernel size 3
+        // Batch size 1
+        add_config(TensorShape(224U, 222U, 64U), TensorShape(1U, 3U, 64U, 64U), TensorShape(64U), TensorShape(224U, 222U, 64U), PadStrideInfo(1, 1, 0, 1));
+        add_config(TensorShape(112U, 113U, 64U), TensorShape(1U, 3U, 64U, 128U), TensorShape(128U), TensorShape(112U, 113U, 128U), PadStrideInfo(1, 1, 0, 1));
+        add_config(TensorShape(112U, 112U, 128U), TensorShape(1U, 3U, 128U, 129U), TensorShape(129U), TensorShape(112U, 110U, 129U), PadStrideInfo(1, 1, 0, 0));
+        add_config(TensorShape(53U, 56U, 125U), TensorShape(1U, 3U, 125U, 256U), TensorShape(256U), TensorShape(53U, 56U, 256U), PadStrideInfo(1, 1, 0, 1));
+        add_config(TensorShape(56U, 56U, 256U), TensorShape(1U, 3U, 256U, 256U), TensorShape(256U), TensorShape(56U, 54U, 256U), PadStrideInfo(1, 1, 0, 0));
+        add_config(TensorShape(28U, 28U, 257U), TensorShape(1U, 3U, 257U, 512U), TensorShape(512U), TensorShape(28U, 28U, 512U), PadStrideInfo(1, 1, 0, 1));
+        add_config(TensorShape(28U, 28U, 512U), TensorShape(1U, 3U, 512U, 512U), TensorShape(512U), TensorShape(28U, 28U, 512U), PadStrideInfo(1, 1, 0, 1));
+        add_config(TensorShape(14U, 14U, 512U), TensorShape(1U, 3U, 512U, 512U), TensorShape(512U), TensorShape(14U, 12U, 512U), PadStrideInfo(1, 1, 0, 0));
+        // Batch size 3, 2 and 4
+        add_config(TensorShape(224U, 222U, 64U, 3U), TensorShape(1U, 3U, 64U, 64U), TensorShape(64U), TensorShape(224U, 222U, 64U, 3U), PadStrideInfo(1, 1, 0, 1));
+        add_config(TensorShape(112U, 113U, 64U, 2U), TensorShape(1U, 3U, 64U, 128U), TensorShape(128U), TensorShape(112U, 113U, 128U, 2U), PadStrideInfo(1, 1, 0, 1));
+        add_config(TensorShape(111U, 112U, 127U, 4U), TensorShape(1U, 3U, 127U, 128U), TensorShape(128U), TensorShape(111U, 112U, 128U, 4U), PadStrideInfo(1, 1, 0, 1));
+    }
+};
+
 class LargeWinogradConvolutionLayer5x5Dataset final : public ConvolutionLayerDataset
 {
 public:
diff --git a/tests/datasets/ShapeDatasets.h b/tests/datasets/ShapeDatasets.h
index a5620ff..68263c7 100644
--- a/tests/datasets/ShapeDatasets.h
+++ b/tests/datasets/ShapeDatasets.h
@@ -388,6 +388,38 @@
     }
 };
 
+/** Data set containing small 3x1 tensor shapes. */
+class Small3x1Shapes final : public ShapeDataset
+{
+public:
+    Small3x1Shapes()
+        : ShapeDataset("Shape",
+    {
+        TensorShape{ 3U, 1U, 7U, 4U },
+                     TensorShape{ 3U, 1U, 4U, 13U },
+                     TensorShape{ 3U, 1U, 9U, 2U },
+                     TensorShape{ 3U, 1U, 3U, 5U },
+    })
+    {
+    }
+};
+
+/** Data set containing small 1x3 tensor shapes. */
+class Small1x3Shapes final : public ShapeDataset
+{
+public:
+    Small1x3Shapes()
+        : ShapeDataset("Shape",
+    {
+        TensorShape{ 1U, 3U, 7U, 4U },
+                     TensorShape{ 1U, 3U, 4U, 13U },
+                     TensorShape{ 1U, 3U, 9U, 2U },
+                     TensorShape{ 1U, 3U, 3U, 5U },
+    })
+    {
+    }
+};
+
 /** Data set containing large 3x3 tensor shapes. */
 class Large3x3Shapes final : public ShapeDataset
 {
@@ -404,6 +436,38 @@
     }
 };
 
+/** Data set containing large 3x1 tensor shapes. */
+class Large3x1Shapes final : public ShapeDataset
+{
+public:
+    Large3x1Shapes()
+        : ShapeDataset("Shape",
+    {
+        TensorShape{ 3U, 1U, 32U, 64U },
+                     TensorShape{ 3U, 1U, 51U, 13U },
+                     TensorShape{ 3U, 1U, 53U, 47U },
+                     TensorShape{ 3U, 1U, 128U, 384U },
+    })
+    {
+    }
+};
+
+/** Data set containing large 1x3 tensor shapes. */
+class Large1x3Shapes final : public ShapeDataset
+{
+public:
+    Large1x3Shapes()
+        : ShapeDataset("Shape",
+    {
+        TensorShape{ 1U, 3U, 32U, 64U },
+                     TensorShape{ 1U, 3U, 51U, 13U },
+                     TensorShape{ 1U, 3U, 53U, 47U },
+                     TensorShape{ 1U, 3U, 128U, 384U },
+    })
+    {
+    }
+};
+
 /** Data set containing small 5x5 tensor shapes. */
 class Small5x5Shapes final : public ShapeDataset
 {
diff --git a/tests/datasets/SmallConvolutionLayerDataset.h b/tests/datasets/SmallConvolutionLayerDataset.h
index fed36de..f05cc15 100644
--- a/tests/datasets/SmallConvolutionLayerDataset.h
+++ b/tests/datasets/SmallConvolutionLayerDataset.h
@@ -52,6 +52,36 @@
     }
 };
 
+class SmallWinogradConvolutionLayer3x1Dataset final : public ConvolutionLayerDataset
+{
+public:
+    SmallWinogradConvolutionLayer3x1Dataset()
+    {
+        // Channel size big enough to force multithreaded execution of the input transform
+        add_config(TensorShape(8U, 8U, 32U), TensorShape(3U, 1U, 32U, 1U), TensorShape(1U), TensorShape(6U, 8U, 1U), PadStrideInfo(1, 1, 0, 0));
+        // Batch size 1
+        add_config(TensorShape(8U, 8U, 2U), TensorShape(3U, 1U, 2U, 1U), TensorShape(1U), TensorShape(6U, 8U, 1U), PadStrideInfo(1, 1, 0, 0));
+        // Batch size 4
+        add_config(TensorShape(23U, 27U, 5U, 4U), TensorShape(3U, 1U, 5U, 21U), TensorShape(21U), TensorShape(21U, 27U, 21U, 4U), PadStrideInfo(1, 1, 0, 0));
+        add_config(TensorShape(8U, 8U, 2U), TensorShape(3U, 1U, 2U, 1U), TensorShape(1U), TensorShape(8U, 8U, 1U), PadStrideInfo(1, 1, 1, 0));
+    }
+};
+
+class SmallWinogradConvolutionLayer1x3Dataset final : public ConvolutionLayerDataset
+{
+public:
+    SmallWinogradConvolutionLayer1x3Dataset()
+    {
+        // Channel size big enough to force multithreaded execution of the input transform
+        add_config(TensorShape(8U, 8U, 32U), TensorShape(1U, 3U, 32U, 1U), TensorShape(1U), TensorShape(8U, 6U, 1U), PadStrideInfo(1, 1, 0, 0));
+        // Batch size 1
+        add_config(TensorShape(8U, 8U, 2U), TensorShape(1U, 3U, 2U, 1U), TensorShape(1U), TensorShape(8U, 6U, 1U), PadStrideInfo(1, 1, 0, 0));
+        // Batch size 4
+        add_config(TensorShape(23U, 27U, 5U, 4U), TensorShape(1U, 3U, 5U, 21U), TensorShape(21U), TensorShape(23U, 25U, 21U, 4U), PadStrideInfo(1, 1, 0, 0));
+        add_config(TensorShape(8U, 8U, 2U), TensorShape(1U, 3U, 2U, 1U), TensorShape(1U), TensorShape(8U, 8U, 1U), PadStrideInfo(1, 1, 0, 1));
+    }
+};
+
 class SmallWinogradConvolutionLayer5x5Dataset final : public ConvolutionLayerDataset
 {
 public:
diff --git a/tests/datasets/WinogradInputTransformDataset.h b/tests/datasets/WinogradInputTransformDataset.h
index e365f96..ca23984 100644
--- a/tests/datasets/WinogradInputTransformDataset.h
+++ b/tests/datasets/WinogradInputTransformDataset.h
@@ -112,6 +112,36 @@
     }
 };
 
+class SmallWinogradInputTransformDataset2x1_3x1 final : public WinogradInputTransformDataset
+{
+public:
+    SmallWinogradInputTransformDataset2x1_3x1()
+    {
+        add_config(TensorShape(9U, 9U), WinogradInfo(Size2D(2U, 1U), Size2D(3U, 1U), Size2D(9U, 9U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
+        add_config(TensorShape(27U, 13U, 2U), WinogradInfo(Size2D(2U, 1U), Size2D(3U, 1U), Size2D(27U, 13U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(128U, 64U, 1U, 3U), WinogradInfo(Size2D(2U, 1U), Size2D(3U, 1U), Size2D(128U, 64U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
+        add_config(TensorShape(9U, 9U, 3U, 4U), WinogradInfo(Size2D(2U, 1U), Size2D(3U, 1U), Size2D(9U, 9U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(27U, 13U, 2U, 4U), WinogradInfo(Size2D(2U, 1U), Size2D(3U, 1U), Size2D(27U, 13U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
+        add_config(TensorShape(9U, 9U, 3U, 5U), WinogradInfo(Size2D(2U, 1U), Size2D(3U, 1U), Size2D(9U, 9U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(14U, 14U, 512U, 2U), WinogradInfo(Size2D(2U, 1U), Size2D(3U, 1U), Size2D(14U, 14U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
+    }
+};
+
+class SmallWinogradInputTransformDataset1x2_1x3 final : public WinogradInputTransformDataset
+{
+public:
+    SmallWinogradInputTransformDataset1x2_1x3()
+    {
+        add_config(TensorShape(9U, 9U), WinogradInfo(Size2D(1U, 2U), Size2D(1U, 3U), Size2D(9U, 9U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
+        add_config(TensorShape(27U, 13U, 2U), WinogradInfo(Size2D(1U, 2U), Size2D(1U, 3U), Size2D(27U, 13U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(128U, 64U, 1U, 3U), WinogradInfo(Size2D(1U, 2U), Size2D(1U, 3U), Size2D(128U, 64U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
+        add_config(TensorShape(9U, 9U, 3U, 4U), WinogradInfo(Size2D(1U, 2U), Size2D(1U, 3U), Size2D(9U, 9U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(27U, 13U, 2U, 4U), WinogradInfo(Size2D(1U, 2U), Size2D(1U, 3U), Size2D(27U, 13U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
+        add_config(TensorShape(9U, 9U, 3U, 5U), WinogradInfo(Size2D(1U, 2U), Size2D(1U, 3U), Size2D(9U, 9U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(14U, 14U, 512U, 2U), WinogradInfo(Size2D(1U, 2U), Size2D(1U, 3U), Size2D(14U, 14U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
+    }
+};
+
 class SmallWinogradInputTransformDataset4x4_3x3 final : public WinogradInputTransformDataset
 {
 public:
@@ -127,6 +157,36 @@
     }
 };
 
+class SmallWinogradInputTransformDataset4x1_3x1 final : public WinogradInputTransformDataset
+{
+public:
+    SmallWinogradInputTransformDataset4x1_3x1()
+    {
+        add_config(TensorShape(9U, 9U), WinogradInfo(Size2D(4U, 1U), Size2D(3U, 1U), Size2D(9U, 9U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
+        add_config(TensorShape(27U, 13U, 2U), WinogradInfo(Size2D(4U, 1U), Size2D(3U, 1U), Size2D(27U, 13U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(128U, 64U, 1U, 3U), WinogradInfo(Size2D(4U, 1U), Size2D(3U, 1U), Size2D(128U, 64U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
+        add_config(TensorShape(9U, 9U, 3U, 4U), WinogradInfo(Size2D(4U, 1U), Size2D(3U, 1U), Size2D(9U, 9U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(27U, 13U, 2U, 4U), WinogradInfo(Size2D(4U, 1U), Size2D(3U, 1U), Size2D(27U, 13U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
+        add_config(TensorShape(9U, 9U, 3U, 5U), WinogradInfo(Size2D(4U, 1U), Size2D(3U, 1U), Size2D(9U, 9U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(14U, 14U, 512U, 2U), WinogradInfo(Size2D(4U, 1U), Size2D(3U, 1U), Size2D(14U, 14U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
+    }
+};
+
+class SmallWinogradInputTransformDataset1x4_1x3 final : public WinogradInputTransformDataset
+{
+public:
+    SmallWinogradInputTransformDataset1x4_1x3()
+    {
+        add_config(TensorShape(9U, 9U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 3U), Size2D(9U, 9U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
+        add_config(TensorShape(27U, 13U, 2U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 3U), Size2D(27U, 13U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(128U, 64U, 1U, 3U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 3U), Size2D(128U, 64U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
+        add_config(TensorShape(9U, 9U, 3U, 4U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 3U), Size2D(9U, 9U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(27U, 13U, 2U, 4U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 3U), Size2D(27U, 13U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
+        add_config(TensorShape(9U, 9U, 3U, 5U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 3U), Size2D(9U, 9U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(14U, 14U, 512U, 2U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 3U), Size2D(14U, 14U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
+    }
+};
+
 class SmallWinogradInputTransformDataset4x4_5x5 final : public WinogradInputTransformDataset
 {
 public:
@@ -154,6 +214,30 @@
     }
 };
 
+class LargeWinogradInputTransformDataset2x1_3x1 final : public WinogradInputTransformDataset
+{
+public:
+    LargeWinogradInputTransformDataset2x1_3x1()
+    {
+        add_config(TensorShape(42U, 37U, 8U, 15U), WinogradInfo(Size2D(2U, 1U), Size2D(3U, 1U), Size2D(42U, 37U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
+        add_config(TensorShape(57U, 60U, 13U, 8U), WinogradInfo(Size2D(2U, 1U), Size2D(3U, 1U), Size2D(57U, 60U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
+        add_config(TensorShape(128U, 64U, 21U, 13U), WinogradInfo(Size2D(2U, 1U), Size2D(3U, 1U), Size2D(128U, 64U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(83U, 72U, 14U, 5U), WinogradInfo(Size2D(2U, 1U), Size2D(3U, 1U), Size2D(83U, 72U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+    }
+};
+
+class LargeWinogradInputTransformDataset1x2_1x3 final : public WinogradInputTransformDataset
+{
+public:
+    LargeWinogradInputTransformDataset1x2_1x3()
+    {
+        add_config(TensorShape(42U, 37U, 8U, 15U), WinogradInfo(Size2D(1U, 2U), Size2D(1U, 3U), Size2D(42U, 37U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
+        add_config(TensorShape(57U, 60U, 13U, 8U), WinogradInfo(Size2D(1U, 2U), Size2D(1U, 3U), Size2D(57U, 60U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
+        add_config(TensorShape(128U, 64U, 21U, 13U), WinogradInfo(Size2D(1U, 2U), Size2D(1U, 3U), Size2D(128U, 64U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(83U, 72U, 14U, 5U), WinogradInfo(Size2D(1U, 2U), Size2D(1U, 3U), Size2D(83U, 72U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+    }
+};
+
 class LargeWinogradInputTransformDataset4x4_3x3 final : public WinogradInputTransformDataset
 {
 public:
@@ -166,6 +250,30 @@
     }
 };
 
+class LargeWinogradInputTransformDataset4x1_3x1 final : public WinogradInputTransformDataset
+{
+public:
+    LargeWinogradInputTransformDataset4x1_3x1()
+    {
+        add_config(TensorShape(42U, 37U, 8U, 15U), WinogradInfo(Size2D(4U, 1U), Size2D(3U, 1U), Size2D(42U, 37U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
+        add_config(TensorShape(57U, 60U, 13U, 8U), WinogradInfo(Size2D(4U, 1U), Size2D(3U, 1U), Size2D(57U, 60U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
+        add_config(TensorShape(128U, 64U, 21U, 13U), WinogradInfo(Size2D(4U, 1U), Size2D(3U, 1U), Size2D(128U, 64U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(83U, 72U, 14U, 5U), WinogradInfo(Size2D(4U, 1U), Size2D(3U, 1U), Size2D(83U, 72U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+    }
+};
+
+class LargeWinogradInputTransformDataset1x4_1x3 final : public WinogradInputTransformDataset
+{
+public:
+    LargeWinogradInputTransformDataset1x4_1x3()
+    {
+        add_config(TensorShape(42U, 37U, 8U, 15U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 3U), Size2D(42U, 37U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
+        add_config(TensorShape(57U, 60U, 13U, 8U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 3U), Size2D(57U, 60U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
+        add_config(TensorShape(128U, 64U, 21U, 13U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 3U), Size2D(128U, 64U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(83U, 72U, 14U, 5U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 3U), Size2D(83U, 72U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+    }
+};
+
 class LargeWinogradInputTransformDataset4x4_5x5 final : public WinogradInputTransformDataset
 {
 public:
diff --git a/tests/datasets/WinogradOutputTransformDataset.h b/tests/datasets/WinogradOutputTransformDataset.h
index c7ba3b2..a4689c6 100644
--- a/tests/datasets/WinogradOutputTransformDataset.h
+++ b/tests/datasets/WinogradOutputTransformDataset.h
@@ -99,12 +99,11 @@
     std::vector<WinogradInfo> _info{};
 };
 
-class SmallWinogradOutputTransformDataset final : public WinogradOutputTransformDataset
+class SmallWinogradOutputTransformDatasetNCHW final : public WinogradOutputTransformDataset
 {
 public:
-    SmallWinogradOutputTransformDataset()
+    SmallWinogradOutputTransformDatasetNCHW()
     {
-        // NCHW
         // (2x2, 3x3)
         add_config(TensorShape(13U, 6U, 16U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(7U, 6U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
         add_config(TensorShape(7U, 20U, 16U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(10U, 11U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
@@ -120,6 +119,34 @@
         add_config(TensorShape(24U, 16U, 36U, 2U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(14U, 14U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
         add_config(TensorShape(7U, 12U, 16U, 5U), WinogradInfo(Size2D(2U, 2U), Size2D(3U, 3U), Size2D(8U, 10U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
 
+        // (2x1, 3x1)
+        add_config(TensorShape(13U, 18U, 4U), WinogradInfo(Size2D(2U, 1U), Size2D(3U, 1U), Size2D(7U, 6U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(7U, 44U, 4U), WinogradInfo(Size2D(2U, 1U), Size2D(3U, 1U), Size2D(10U, 11U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(1U, 891U, 4U), WinogradInfo(Size2D(2U, 1U), Size2D(3U, 1U), Size2D(53U, 33U), PadStrideInfo(1, 1, 1, 0), DataLayout::NCHW));
+        add_config(TensorShape(7U, 30U, 4U, 3U), WinogradInfo(Size2D(2U, 1U), Size2D(3U, 1U), Size2D(8U, 10U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(24U, 98U, 4U, 2U), WinogradInfo(Size2D(2U, 1U), Size2D(3U, 1U), Size2D(14U, 14U), PadStrideInfo(1, 1, 1, 0), DataLayout::NCHW));
+
+        // (1x2, 1x3)
+        add_config(TensorShape(13U, 14U, 4U), WinogradInfo(Size2D(1U, 2U), Size2D(1U, 3U), Size2D(7U, 6U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(7U, 50U, 4U), WinogradInfo(Size2D(1U, 2U), Size2D(1U, 3U), Size2D(10U, 11U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(1U, 901U, 4U), WinogradInfo(Size2D(1U, 2U), Size2D(1U, 3U), Size2D(53U, 33U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW));
+        add_config(TensorShape(7U, 32U, 4U, 3U), WinogradInfo(Size2D(1U, 2U), Size2D(1U, 3U), Size2D(8U, 10U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(24U, 98U, 4U, 2U), WinogradInfo(Size2D(1U, 2U), Size2D(1U, 3U), Size2D(14U, 14U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW));
+
+        // (4x1, 3x1)
+        add_config(TensorShape(13U, 12U, 6U), WinogradInfo(Size2D(4U, 1U), Size2D(3U, 1U), Size2D(7U, 6U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(7U, 22U, 6U), WinogradInfo(Size2D(4U, 1U), Size2D(3U, 1U), Size2D(10U, 11U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(1U, 462U, 6U), WinogradInfo(Size2D(4U, 1U), Size2D(3U, 1U), Size2D(53U, 33U), PadStrideInfo(1, 1, 1, 0), DataLayout::NCHW));
+        add_config(TensorShape(7U, 20U, 6U, 3U), WinogradInfo(Size2D(4U, 1U), Size2D(3U, 1U), Size2D(8U, 10U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(24U, 56U, 6U, 2U), WinogradInfo(Size2D(4U, 1U), Size2D(3U, 1U), Size2D(14U, 14U), PadStrideInfo(1, 1, 1, 0), DataLayout::NCHW));
+
+        // (1x4, 1x3)
+        add_config(TensorShape(13U, 7U, 6U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 3U), Size2D(7U, 6U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(7U, 30U, 6U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 3U), Size2D(10U, 11U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(1U, 477U, 6U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 3U), Size2D(53U, 33U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW));
+        add_config(TensorShape(7U, 16U, 6U, 3U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 3U), Size2D(8U, 10U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(24U, 56U, 6U, 2U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 3U), Size2D(14U, 14U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW));
+
         // (4x4, 5x5)
         add_config(TensorShape(13U, 1U, 64U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(7U, 6U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
         add_config(TensorShape(7U, 4U, 64U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(10U, 11U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
@@ -127,8 +154,14 @@
         add_config(TensorShape(7U, 2U, 64U, 3U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(8U, 10U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
         add_config(TensorShape(24U, 9U, 64U, 2U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(14U, 14U), PadStrideInfo(1, 1, 1, 1), DataLayout::NCHW));
         add_config(TensorShape(7U, 2U, 64U, 5U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(8U, 10U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+    }
+};
 
-        // NHWC
+class SmallWinogradOutputTransformDatasetNHWC final : public WinogradOutputTransformDataset
+{
+public:
+    SmallWinogradOutputTransformDatasetNHWC()
+    {
         // (4x4, 3x3)
         add_config(TensorShape(13U, 4U, 36U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(10U, 9U), PadStrideInfo(1, 1, 0, 0), DataLayout::NHWC));
         add_config(TensorShape(13U, 6U, 36U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(10U, 11U), PadStrideInfo(1, 1, 0, 0), DataLayout::NHWC));
@@ -146,10 +179,10 @@
     }
 };
 
-class LargeWinogradOutputTransformDataset final : public WinogradOutputTransformDataset
+class LargeWinogradOutputTransformDatasetNCHW final : public WinogradOutputTransformDataset
 {
 public:
-    LargeWinogradOutputTransformDataset()
+    LargeWinogradOutputTransformDatasetNCHW()
     {
         // NCHW
         // (2x2, 3x3)
@@ -168,13 +201,51 @@
         add_config(TensorShape(32U, 784U, 36U, 2U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(112U, 112U), PadStrideInfo(1, 1, 1, 0), DataLayout::NCHW));
         add_config(TensorShape(13U, 196U, 36U, 5U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(56U, 56U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW));
 
+        // (2x1, 3x1)
+        add_config(TensorShape(64U, 24976U, 4U), WinogradInfo(Size2D(2U, 1U), Size2D(3U, 1U), Size2D(224U, 223U), PadStrideInfo(1, 1, 1, 0), DataLayout::NCHW));
+        add_config(TensorShape(32U, 6160U, 4U), WinogradInfo(Size2D(2U, 1U), Size2D(3U, 1U), Size2D(112U, 110U), PadStrideInfo(1, 1, 1, 0), DataLayout::NCHW));
+        add_config(TensorShape(13U, 1568U, 4U), WinogradInfo(Size2D(2U, 1U), Size2D(3U, 1U), Size2D(56U, 56U), PadStrideInfo(1, 1, 1, 0), DataLayout::NCHW));
+        add_config(TensorShape(64U, 24753U, 4U, 3U), WinogradInfo(Size2D(2U, 1U), Size2D(3U, 1U), Size2D(224U, 223U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(32U, 6050U, 4U, 2U), WinogradInfo(Size2D(2U, 1U), Size2D(3U, 1U), Size2D(112U, 110U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(13U, 1512U, 4U, 5U), WinogradInfo(Size2D(2U, 1U), Size2D(3U, 1U), Size2D(56U, 56U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+
+        // (1x2, 1x3)
+        add_config(TensorShape(64U, 25088U, 4U), WinogradInfo(Size2D(1U, 2U), Size2D(1U, 3U), Size2D(224U, 223U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW));
+        add_config(TensorShape(32U, 6160U, 4U), WinogradInfo(Size2D(1U, 2U), Size2D(1U, 3U), Size2D(112U, 110U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW));
+        add_config(TensorShape(13U, 1568U, 4U), WinogradInfo(Size2D(1U, 2U), Size2D(1U, 3U), Size2D(56U, 56U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW));
+        add_config(TensorShape(64U, 24864U, 4U, 3U), WinogradInfo(Size2D(1U, 2U), Size2D(1U, 3U), Size2D(224U, 223U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(32U, 6048U, 4U, 2U), WinogradInfo(Size2D(1U, 2U), Size2D(1U, 3U), Size2D(112U, 110U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(13U, 1512U, 4U, 5U), WinogradInfo(Size2D(1U, 2U), Size2D(1U, 3U), Size2D(56U, 56U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+
+        // (4x1, 3x1)
+        add_config(TensorShape(64U, 12488U, 6U), WinogradInfo(Size2D(4U, 1U), Size2D(3U, 1U), Size2D(224U, 223U), PadStrideInfo(1, 1, 1, 0), DataLayout::NCHW));
+        add_config(TensorShape(32U, 3080U, 6U), WinogradInfo(Size2D(4U, 1U), Size2D(3U, 1U), Size2D(112U, 110U), PadStrideInfo(1, 1, 1, 0), DataLayout::NCHW));
+        add_config(TensorShape(13U, 784U, 6U), WinogradInfo(Size2D(4U, 1U), Size2D(3U, 1U), Size2D(56U, 56U), PadStrideInfo(1, 1, 1, 0), DataLayout::NCHW));
+        add_config(TensorShape(64U, 12488U, 6U, 3U), WinogradInfo(Size2D(4U, 1U), Size2D(3U, 1U), Size2D(224U, 223U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(32U, 3080U, 6U, 2U), WinogradInfo(Size2D(4U, 1U), Size2D(3U, 1U), Size2D(112U, 110U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(13U, 784U, 6U, 5U), WinogradInfo(Size2D(4U, 1U), Size2D(3U, 1U), Size2D(56U, 56U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+
+        // (1x4, 1x3)
+        add_config(TensorShape(64U, 12544U, 6U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 3U), Size2D(224U, 223U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW));
+        add_config(TensorShape(32U, 3136U, 6U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 3U), Size2D(112U, 110U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW));
+        add_config(TensorShape(13U, 784U, 6U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 3U), Size2D(56U, 56U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW));
+        add_config(TensorShape(64U, 12544U, 6U, 3U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 3U), Size2D(224U, 223U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(32U, 3024U, 6U, 2U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 3U), Size2D(112U, 110U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+        add_config(TensorShape(13U, 784U, 6U, 5U), WinogradInfo(Size2D(1U, 4U), Size2D(1U, 3U), Size2D(56U, 56U), PadStrideInfo(1, 1, 0, 0), DataLayout::NCHW));
+
         // (4x4, 5x5)
         add_config(TensorShape(32U, 756U, 64U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(112U, 112U), PadStrideInfo(1, 1, 1, 0), DataLayout::NCHW));
         add_config(TensorShape(13U, 182U, 64U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(56U, 56U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW));
         add_config(TensorShape(32U, 756U, 64U, 2U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(112U, 112U), PadStrideInfo(1, 1, 1, 0), DataLayout::NCHW));
         add_config(TensorShape(13U, 182U, 64U, 5U), WinogradInfo(Size2D(4U, 4U), Size2D(5U, 5U), Size2D(56U, 56U), PadStrideInfo(1, 1, 0, 1), DataLayout::NCHW));
+    }
+};
 
-        // NHWC
+class LargeWinogradOutputTransformDatasetNHWC final : public WinogradOutputTransformDataset
+{
+public:
+    LargeWinogradOutputTransformDatasetNHWC()
+    {
         // (4x4, 3x3)
         add_config(TensorShape(64U, 3136U, 36U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(224U, 224U), PadStrideInfo(1, 1, 1, 1), DataLayout::NHWC));
         add_config(TensorShape(32U, 784U, 36U), WinogradInfo(Size2D(4U, 4U), Size2D(3U, 3U), Size2D(112U, 112U), PadStrideInfo(1, 1, 1, 0), DataLayout::NHWC));
diff --git a/tests/validation/CL/Winograd.cpp b/tests/validation/CL/Winograd.cpp
index b869f4c..f68ec8c 100644
--- a/tests/validation/CL/Winograd.cpp
+++ b/tests/validation/CL/Winograd.cpp
@@ -23,6 +23,7 @@
  */
 #include "arm_compute/core/CL/kernels/CLWinogradFilterTransformKernel.h"
 #include "arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h"
+#include "arm_compute/core/Helpers.h"
 #include "arm_compute/core/Types.h"
 #include "arm_compute/core/utils/misc/ShapeCalculator.h"
 #include "arm_compute/runtime/CL/CLTensor.h"
@@ -51,12 +52,66 @@
 {
 namespace
 {
+// *INDENT-OFF*
+// clang-format off
 constexpr AbsoluteTolerance<float> tolerance_f32(0.001f);
 constexpr AbsoluteTolerance<float> tolerance_convolution_layer_f32(0.1f);
-const auto                         SmallWinogradInputTransformDataset = framework::dataset::concat(datasets::SmallWinogradInputTransformDataset2x2_3x3(),
-                                                                                                   framework::dataset::concat(datasets::SmallWinogradInputTransformDataset4x4_3x3(), datasets::SmallWinogradInputTransformDataset4x4_5x5()));
-const auto LargeWinogradInputTransformDataset = framework::dataset::concat(datasets::LargeWinogradInputTransformDataset2x2_3x3(),
-                                                                           framework::dataset::concat(datasets::LargeWinogradInputTransformDataset4x4_3x3(), datasets::LargeWinogradInputTransformDataset4x4_5x5()));
+
+// Input transform
+const auto SmallWinogradInputTransformDatasetNCHW =
+           framework::dataset::concat(datasets::SmallWinogradInputTransformDataset2x2_3x3(),
+           framework::dataset::concat(datasets::SmallWinogradInputTransformDataset2x1_3x1(),
+           framework::dataset::concat(datasets::SmallWinogradInputTransformDataset1x2_1x3(),
+           framework::dataset::concat(datasets::SmallWinogradInputTransformDataset4x4_3x3(),
+           framework::dataset::concat(datasets::SmallWinogradInputTransformDataset4x1_3x1(),
+           framework::dataset::concat(datasets::SmallWinogradInputTransformDataset1x4_1x3(),
+                                      datasets::SmallWinogradInputTransformDataset4x4_5x5()))))));
+
+const auto SmallWinogradInputTransformDatasetNHWC = framework::dataset::concat(datasets::SmallWinogradInputTransformDataset4x4_3x3(),
+                                                                               datasets::SmallWinogradInputTransformDataset4x4_5x5());
+
+const auto LargeWinogradInputTransformDatasetNCHW =
+           framework::dataset::concat(datasets::LargeWinogradInputTransformDataset2x2_3x3(),
+           framework::dataset::concat(datasets::LargeWinogradInputTransformDataset2x1_3x1(),
+           framework::dataset::concat(datasets::LargeWinogradInputTransformDataset1x2_1x3(),
+           framework::dataset::concat(datasets::LargeWinogradInputTransformDataset4x4_3x3(),
+           framework::dataset::concat(datasets::LargeWinogradInputTransformDataset4x1_3x1(),
+           framework::dataset::concat(datasets::LargeWinogradInputTransformDataset1x4_1x3(),
+                                      datasets::LargeWinogradInputTransformDataset4x4_5x5()))))));
+
+const auto LargeWinogradInputTransformDatasetNHWC =
+           framework::dataset::concat(datasets::LargeWinogradInputTransformDataset4x4_3x3(),
+                                      datasets::LargeWinogradInputTransformDataset4x4_5x5());
+
+// Filter transform
+const auto SmallWinogradFilterTransformDatasetNCHW =
+           framework::dataset::concat(combine(datasets::Small3x3Shapes(), framework::dataset::make("OutputTile", { Size2D(2U, 2U), Size2D(4U, 4U) })),
+           framework::dataset::concat(combine(datasets::Small3x1Shapes(), framework::dataset::make("OutputTile", { Size2D(2U, 1U), Size2D(4U, 1U) })),
+           framework::dataset::concat(combine(datasets::Small1x3Shapes(), framework::dataset::make("OutputTile", { Size2D(1U, 2U), Size2D(1U, 4U) })),
+                                      combine(datasets::Small5x5Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 4U) })))));
+
+const auto SmallWinogradFilterTransformDatasetNHWC =
+           framework::dataset::concat(combine(datasets::Small3x3Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 4U) })),
+                                      combine(datasets::Small5x5Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 4U) })));
+
+const auto LargeWinogradFilterTransformDatasetNCHW =
+           framework::dataset::concat(combine(datasets::Large3x3Shapes(), framework::dataset::make("OutputTile", { Size2D(2U, 2U), Size2D(4U, 4U) })),
+           framework::dataset::concat(combine(datasets::Large3x1Shapes(), framework::dataset::make("OutputTile", { Size2D(2U, 1U), Size2D(4U, 1U) })),
+           framework::dataset::concat(combine(datasets::Large1x3Shapes(), framework::dataset::make("OutputTile", { Size2D(1U, 2U), Size2D(1U, 4U) })),
+                                      combine(datasets::Large5x5Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 4U) })))));
+
+const auto LargeWinogradFilterTransformDatasetNHWC =
+           framework::dataset::concat(combine(datasets::Large3x3Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 4U) })),
+                                      combine(datasets::Large5x5Shapes(), framework::dataset::make("OutputTile", { Size2D(4U, 4U) })));
+
+// Output transform
+const auto SmallWinogradOutputTransformDatasetNCHW = datasets::SmallWinogradOutputTransformDatasetNCHW();
+
+const auto SmallWinogradOutputTransformDatasetNHWC = datasets::SmallWinogradOutputTransformDatasetNHWC();
+
+const auto LargeWinogradOutputTransformDatasetNCHW = datasets::LargeWinogradOutputTransformDatasetNCHW();
+
+const auto LargeWinogradOutputTransformDatasetNHWC = datasets::LargeWinogradOutputTransformDatasetNHWC();
 } // namespace
 
 using namespace arm_compute::misc::shape_calculator;
@@ -65,9 +120,6 @@
 TEST_SUITE(Winograd)
 
 TEST_SUITE(InputTransform)
-
-// *INDENT-OFF*
-// clang-format off
 DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
                                                 framework::dataset::make("InputInfo",{
                                                                                         TensorInfo(TensorShape(53U, 21U, 5U, 3U), 1, DataType::F16),     // F16 not supported
@@ -101,17 +153,20 @@
 {
     ARM_COMPUTE_EXPECT(bool(CLWinogradInputTransform::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), winograd_info)) == expected, framework::LogLevel::ERRORS);
 }
-// clang-format on
-// *INDENT-ON*
 
 using CLWinogradInputTransformFixture = WinogradInputTransformValidationFixture<CLTensor, CLAccessor, CLWinogradInputTransform, float>;
 
-DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(framework::dataset::concat(SmallWinogradInputTransformDataset, LargeWinogradInputTransformDataset),
+TEST_SUITE(NCHW)
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(framework::dataset::concat(SmallWinogradInputTransformDatasetNCHW,
+                                                                                                      LargeWinogradInputTransformDatasetNCHW),
                                                                            framework::dataset::make("DataLayout", { DataLayout::NCHW })),
-                                                                   framework::dataset::make("DataType", { DataType::F32 })),
+                                                                           framework::dataset::make("DataType", { DataType::F32 })),
                shape_in, winograd_info, data_layout, data_type)
 {
-    TensorShape shape_out = compute_winograd_input_transform_shape(TensorInfo(shape_in, 1, data_type), winograd_info);
+    TensorInfo  tensor_info_in(shape_in, 1, data_type);
+    tensor_info_in.set_data_layout(data_layout);
+
+    TensorShape shape_out = compute_winograd_input_transform_shape(tensor_info_in, winograd_info);
 
     // Create tensors
     CLTensor in  = create_tensor<CLTensor>(shape_in, data_type, 1, 0, QuantizationInfo(), data_layout);
@@ -127,28 +182,70 @@
     winograd_input_transform.configure(&in, &out, winograd_info);
 }
 
-FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradInputTransformFixture, framework::DatasetMode::PRECOMMIT, combine(framework::dataset::concat(combine(SmallWinogradInputTransformDataset,
-                                                                                                             framework::dataset::make("DataLayout", { DataLayout::NCHW })),
-                                                                                                             combine(framework::dataset::concat(datasets::SmallWinogradInputTransformDataset4x4_3x3(), datasets::SmallWinogradInputTransformDataset4x4_5x5()),
-                                                                                                                     framework::dataset::make("DataLayout", { DataLayout::NHWC }))),
-                                                                                                             framework::dataset::make("DataType", { DataType::F32 })))
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradInputTransformFixture, framework::DatasetMode::PRECOMMIT, combine(combine(SmallWinogradInputTransformDatasetNCHW,
+                                                                                                                     framework::dataset::make("DataLayout", { DataLayout::NCHW })),
+                                                                                                                     framework::dataset::make("DataType", { DataType::F32 })))
 {
     validate(CLAccessor(_target), _reference, tolerance_f32);
 }
 
-FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradInputTransformFixture, framework::DatasetMode::NIGHTLY, combine(framework::dataset::concat(combine(LargeWinogradInputTransformDataset,
-                                                                                                           framework::dataset::make("DataLayout", { DataLayout::NCHW })),
-                                                                                                           combine(framework::dataset::concat(datasets::LargeWinogradInputTransformDataset4x4_3x3(), datasets::LargeWinogradInputTransformDataset4x4_5x5()),
-                                                                                                                   framework::dataset::make("DataLayout", { DataLayout::NHWC }))),
-                                                                                                           framework::dataset::make("DataType", { DataType::F32 })))
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradInputTransformFixture, framework::DatasetMode::NIGHTLY, combine(combine(LargeWinogradInputTransformDatasetNCHW,
+                                                                                                                   framework::dataset::make("DataLayout", { DataLayout::NCHW })),
+                                                                                                                   framework::dataset::make("DataType", { DataType::F32 })))
 {
     validate(CLAccessor(_target), _reference, tolerance_f32);
 }
+TEST_SUITE_END() // NCHW
+
+TEST_SUITE(NHWC)
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(framework::dataset::concat(SmallWinogradInputTransformDatasetNHWC,
+                                                                                                      LargeWinogradInputTransformDatasetNHWC),
+                                                                           framework::dataset::make("DataLayout", { DataLayout::NHWC })),
+                                                                           framework::dataset::make("DataType", { DataType::F32 })),
+               shape_in, winograd_info, data_layout, data_type)
+{
+    TensorShape shape_in_nhwc(shape_in);
+
+    // Convert the shape to NHWC
+    permute(shape_in_nhwc, PermutationVector(2U, 0U, 1U));
+
+    // TensorInfo
+    TensorInfo  tensor_info_in(shape_in_nhwc, 1, data_type);
+    tensor_info_in.set_data_layout(data_layout);
+
+    TensorShape shape_out = compute_winograd_input_transform_shape(tensor_info_in, winograd_info);
+
+    // Create tensors
+    CLTensor in  = create_tensor<CLTensor>(shape_in_nhwc, data_type, 1, 0, QuantizationInfo(), data_layout);
+    CLTensor out = create_tensor<CLTensor>(shape_out, data_type);
+
+    ARM_COMPUTE_EXPECT(in.info()->is_resizable(), framework::LogLevel::ERRORS);
+    ARM_COMPUTE_EXPECT(out.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+    // Create and configure function
+    CLWinogradInputTransform winograd_input_transform;
+
+    // Configure the function
+    winograd_input_transform.configure(&in, &out, winograd_info);
+}
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradInputTransformFixture, framework::DatasetMode::PRECOMMIT, combine(combine(SmallWinogradInputTransformDatasetNHWC,
+                                                                                                                     framework::dataset::make("DataLayout", { DataLayout::NHWC })),
+                                                                                                                     framework::dataset::make("DataType", { DataType::F32 })))
+{
+    validate(CLAccessor(_target), _reference, tolerance_f32);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradInputTransformFixture, framework::DatasetMode::NIGHTLY, combine(combine(LargeWinogradInputTransformDatasetNHWC,
+                                                                                                                   framework::dataset::make("DataLayout", { DataLayout::NHWC })),
+                                                                                                                   framework::dataset::make("DataType", { DataType::F32 })))
+{
+    validate(CLAccessor(_target), _reference, tolerance_f32);
+}
+TEST_SUITE_END() // NHWC
 TEST_SUITE_END() // InputTransform
 
 TEST_SUITE(FilterTransform)
-// *INDENT-OFF*
-// clang-format off
 DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(
                                                 framework::dataset::make("InputInfo",{
                                                                                         TensorInfo(TensorShape(3U, 3U, 5U, 3U), 1, DataType::F16),     // F16 not supported
@@ -182,19 +279,19 @@
 {
     ARM_COMPUTE_EXPECT(bool(CLWinogradFilterTransformKernel::validate(&input_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), winograd_info)) == expected, framework::LogLevel::ERRORS);
 }
-// clang-format on
-// *INDENT-ON*
 
 using CLWinogradFilterTransform        = CLSynthetizeFunctionWithZeroConstantBorder<CLWinogradFilterTransformKernel, 0>;
 using CLWinogradFilterTransformFixture = WinogradFilterTransformValidationFixture<CLTensor, CLAccessor, CLWinogradFilterTransform, float>;
 
-DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(combine(combine(framework::dataset::concat(datasets::Small3x3Shapes(), datasets::Large3x3Shapes()),
-                                                                                   framework::dataset::make("OutputTile", { Size2D(2U, 2U), Size2D(4U, 4U) })),
-                                                                           framework::dataset::make("DataLayout", { DataLayout::NCHW })),
-                                                                   framework::dataset::make("DataType", { DataType::F32 })),
+TEST_SUITE(NCHW)
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL,
+               combine(combine(framework::dataset::concat(SmallWinogradFilterTransformDatasetNCHW,
+                                                          LargeWinogradFilterTransformDatasetNCHW),
+                                                          framework::dataset::make("DataLayout", { DataLayout::NCHW })),
+                                                          framework::dataset::make("DataType", { DataType::F32 })),
                shape_a, output_tile, data_layout, data_type)
 {
-    WinogradInfo winograd_info(output_tile, Size2D(shape_a[0], shape_a[1]), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, DataLayout::NCHW /* Not needed */);
+    WinogradInfo winograd_info(output_tile, Size2D(shape_a[0], shape_a[1]), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, data_layout /* Not needed */);
 
     TensorShape shape_b = compute_winograd_filter_transform_shape(TensorInfo(shape_a, 1, data_type), winograd_info);
 
@@ -210,37 +307,79 @@
     winograd_filter_transform.configure(&a, &b, winograd_info);
 }
 
-FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradFilterTransformFixture, framework::DatasetMode::ALL,
-                       combine(framework::dataset::concat(combine(framework::dataset::concat(framework::dataset::concat(combine(datasets::Small3x3Shapes(), framework::dataset::make("OutputTile", Size2D(2U, 2U))),
-                                                                                                                        combine(datasets::Small3x3Shapes(),
-                                                                                                                                framework::dataset::make("OutputTile", Size2D(4U, 4U)))),
-                                                                                             combine(datasets::Small5x5Shapes(), framework::dataset::make("OutputTile", Size2D(4U, 4U)))),
-                                                                  framework::dataset::make("DataLayout", { DataLayout::NCHW })),
-                                                          combine(combine(framework::dataset::concat(datasets::Small3x3Shapes(), datasets::Small5x5Shapes()), framework::dataset::make("OutputTile", Size2D(4U, 4U))), framework::dataset::make("DataLayout", { DataLayout::NHWC }))),
-                               framework::dataset::make("DataType", { DataType::F32 })))
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradFilterTransformFixture, framework::DatasetMode::PRECOMMIT,
+                       combine(combine(SmallWinogradFilterTransformDatasetNCHW,
+                                       framework::dataset::make("DataLayout", { DataLayout::NCHW })),
+                                       framework::dataset::make("DataType", { DataType::F32 })))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_f32);
 }
 
 FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradFilterTransformFixture, framework::DatasetMode::NIGHTLY,
-                       combine(framework::dataset::concat(combine(framework::dataset::concat(framework::dataset::concat(combine(datasets::Large3x3Shapes(), framework::dataset::make("OutputTile", Size2D(2U, 2U))),
-                                                                                                                        combine(datasets::Large3x3Shapes(),
-                                                                                                                                framework::dataset::make("OutputTile", Size2D(4U, 4U)))),
-                                                                                             combine(datasets::Large5x5Shapes(), framework::dataset::make("OutputTile", Size2D(4U, 4U)))),
-                                                                  framework::dataset::make("DataLayout", { DataLayout::NCHW })),
-                                                          combine(combine(framework::dataset::concat(datasets::Large3x3Shapes(), datasets::Large5x5Shapes()), framework::dataset::make("OutputTile", Size2D(4U, 4U))), framework::dataset::make("DataLayout", { DataLayout::NHWC }))),
-                               framework::dataset::make("DataType", { DataType::F32 })))
+                       combine(combine(LargeWinogradFilterTransformDatasetNCHW,
+                                       framework::dataset::make("DataLayout", { DataLayout::NCHW })),
+                                       framework::dataset::make("DataType", { DataType::F32 })))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_f32);
+}
+TEST_SUITE_END() // NCHW
+
+TEST_SUITE(NHWC)
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL,
+               combine(combine(framework::dataset::concat(SmallWinogradFilterTransformDatasetNHWC,
+                                                          LargeWinogradFilterTransformDatasetNHWC),
+                                                          framework::dataset::make("DataLayout", { DataLayout::NHWC })),
+                                                          framework::dataset::make("DataType", { DataType::F32 })),
+               shape_in, output_tile, data_layout, data_type)
+{
+    TensorShape shape_in_nhwc(shape_in);
+
+    // Convert the shape to NHWC
+    permute(shape_in_nhwc, PermutationVector(2U, 0U, 1U));
+
+    // TensorInfo
+    TensorInfo  tensor_info_in(shape_in_nhwc, 1, data_type);
+    tensor_info_in.set_data_layout(data_layout);
+
+    WinogradInfo winograd_info(output_tile, Size2D(shape_in[0], shape_in[1]), Size2D() /* Not needed */, PadStrideInfo() /* Not needed */, data_layout /* Not needed */);
+
+    TensorShape shape_b = compute_winograd_filter_transform_shape(tensor_info_in, winograd_info);
+
+    // Create tensors
+    CLTensor a = create_tensor<CLTensor>(shape_in_nhwc, data_type, 1, 0, QuantizationInfo(), data_layout);
+    CLTensor b = create_tensor<CLTensor>(shape_b, data_type, 1, 0, QuantizationInfo(), data_layout);
+
+    ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS);
+    ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+    // Create and configure function
+    CLWinogradFilterTransform winograd_filter_transform;
+    winograd_filter_transform.configure(&a, &b, winograd_info);
+}
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradFilterTransformFixture, framework::DatasetMode::PRECOMMIT,
+                       combine(combine(SmallWinogradFilterTransformDatasetNHWC,
+                                       framework::dataset::make("DataLayout", { DataLayout::NHWC })),
+                                       framework::dataset::make("DataType", { DataType::F32 })))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_f32);
 }
 
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradFilterTransformFixture, framework::DatasetMode::NIGHTLY,
+                       combine(combine(LargeWinogradFilterTransformDatasetNHWC,
+                                       framework::dataset::make("DataLayout", { DataLayout::NHWC })),
+                                       framework::dataset::make("DataType", { DataType::F32 })))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_f32);
+}
+TEST_SUITE_END() // NHWC
 TEST_SUITE_END() // FilterTransform
 
 TEST_SUITE(OutputTransform)
-// *INDENT-OFF*
-// clang-format off
 DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(
                                                 framework::dataset::make("InputInfo",{
                                                                                         TensorInfo(TensorShape(512U, 49U, 16U, 5U), 1, DataType::F16),      // F16 not supported
@@ -291,14 +430,14 @@
 {
     ARM_COMPUTE_EXPECT(bool(CLWinogradOutputTransformKernel::validate(&input_info.clone()->set_is_resizable(false), &bias_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), winograd_info)) == expected, framework::LogLevel::ERRORS);
 }
-// clang-format on
-// *INDENT-ON*
 
 using CLWinogradOutputTransform        = CLSynthetizeFunctionWithZeroConstantBorder<CLWinogradOutputTransformKernel, 0>;
 using CLWinogradOutputTransformFixture = WinogradOutputTransformValidationFixture<CLTensor, CLAccessor, CLWinogradOutputTransform, float>;
 
-DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(datasets::SmallWinogradOutputTransformDataset(), datasets::LargeWinogradOutputTransformDataset()),
-                                                                   framework::dataset::make("DataType", { DataType::F32 })),
+TEST_SUITE(NCHW)
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(SmallWinogradOutputTransformDatasetNCHW,
+                                                                                              LargeWinogradOutputTransformDatasetNCHW),
+                                                                                              framework::dataset::make("DataType", { DataType::F32 })),
                shape_a, winograd_info, data_type)
 {
     TensorShape shape_b = compute_winograd_output_transform_shape(TensorInfo(shape_a, 1, data_type), winograd_info);
@@ -315,23 +454,62 @@
     winograd_output_transform.configure(&a, nullptr, &b, winograd_info);
 }
 
-FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradOutputTransformFixture, framework::DatasetMode::ALL, combine(datasets::SmallWinogradOutputTransformDataset(), framework::dataset::make("DataType", { DataType::F32 })))
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradOutputTransformFixture, framework::DatasetMode::ALL,
+                       combine(SmallWinogradOutputTransformDatasetNCHW,
+                               framework::dataset::make("DataType", { DataType::F32 })))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_f32);
 }
 
-FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixture, framework::DatasetMode::NIGHTLY, combine(datasets::LargeWinogradOutputTransformDataset(), framework::dataset::make("DataType", { DataType::F32 })))
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixture, framework::DatasetMode::NIGHTLY,
+                       combine(LargeWinogradOutputTransformDatasetNCHW,
+                               framework::dataset::make("DataType", { DataType::F32 })))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_f32);
+}
+TEST_SUITE_END() // NCHW
+
+TEST_SUITE(NHWC)
+DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::dataset::concat(SmallWinogradOutputTransformDatasetNHWC,
+                                                                                              LargeWinogradOutputTransformDatasetNHWC),
+                                                                                              framework::dataset::make("DataType", { DataType::F32 })),
+               shape_a, winograd_info, data_type)
+{
+    TensorShape shape_b = compute_winograd_output_transform_shape(TensorInfo(shape_a, 1, data_type), winograd_info);
+
+    // Create tensors
+    CLTensor a = create_tensor<CLTensor>(shape_a, data_type);
+    CLTensor b = create_tensor<CLTensor>(shape_b, data_type, 1, 0, QuantizationInfo(), winograd_info.output_data_layout);
+
+    ARM_COMPUTE_EXPECT(a.info()->is_resizable(), framework::LogLevel::ERRORS);
+    ARM_COMPUTE_EXPECT(b.info()->is_resizable(), framework::LogLevel::ERRORS);
+
+    // Create and configure function
+    CLWinogradOutputTransform winograd_output_transform;
+    winograd_output_transform.configure(&a, nullptr, &b, winograd_info);
+}
+
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradOutputTransformFixture, framework::DatasetMode::ALL,
+                       combine(SmallWinogradOutputTransformDatasetNHWC,
+                               framework::dataset::make("DataType", { DataType::F32 })))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_f32);
 }
 
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixture, framework::DatasetMode::NIGHTLY,
+                       combine(LargeWinogradOutputTransformDatasetNHWC,
+                               framework::dataset::make("DataType", { DataType::F32 })))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_f32);
+}
+TEST_SUITE_END() // NHWC
 TEST_SUITE_END() // OutputTransform
 
 TEST_SUITE(ConvolutionLayer)
-// *INDENT-OFF*
-// clang-format off
 DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(
                                                 framework::dataset::make("InputInfo", {
                                                                                         TensorInfo(TensorShape(17U, 31U, 2U), 1, DataType::F16),     // FP16 not supported
@@ -373,16 +551,14 @@
 {
     ARM_COMPUTE_EXPECT(bool(CLWinogradConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &bias_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info)) == expected, framework::LogLevel::ERRORS);
 }
-// clang-format on
-// *INDENT-ON*
 
 using CLWinogradConvolutionLayerFastMathFixture = WinogradConvolutionLayerFastMathValidationFixture<CLTensor, CLAccessor, CLWinogradConvolutionLayer, float>;
 TEST_SUITE(Conv3x3)
 FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT,
                        combine(combine(combine(datasets::SmallWinogradConvolutionLayer3x3Dataset(),
                                                framework::dataset::make("DataType", { DataType::F32 })),
-                                       framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
-                               framework::dataset::make("DataLayout", { DataLayout::NCHW })))
+                                               framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+                                               framework::dataset::make("DataLayout", { DataLayout::NCHW })))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32);
@@ -391,20 +567,64 @@
 FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY,
                        combine(combine(combine(datasets::LargeWinogradConvolutionLayer3x3Dataset(),
                                                framework::dataset::make("DataType", { DataType::F32 })),
-                                       framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
-                               framework::dataset::make("DataLayout", { DataLayout::NCHW })))
+                                               framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+                                               framework::dataset::make("DataLayout", { DataLayout::NCHW })))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32);
 }
 TEST_SUITE_END() // Conv3x3
 
+TEST_SUITE(Conv3x1)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT,
+                       combine(combine(combine(datasets::SmallWinogradConvolutionLayer3x1Dataset(),
+                                       framework::dataset::make("DataType", { DataType::F32 })),
+                                       framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+                                       framework::dataset::make("DataLayout", { DataLayout::NCHW })))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY,
+                       combine(combine(combine(datasets::LargeWinogradConvolutionLayer3x1Dataset(),
+                                       framework::dataset::make("DataType", { DataType::F32 })),
+                                       framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+                                       framework::dataset::make("DataLayout", { DataLayout::NCHW })))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32);
+}
+TEST_SUITE_END() // Conv3x1
+
+TEST_SUITE(Conv1x3)
+FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT,
+                       combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x3Dataset(),
+                                       framework::dataset::make("DataType", { DataType::F32 })),
+                                       framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+                                       framework::dataset::make("DataLayout", { DataLayout::NCHW })))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32);
+}
+
+FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY,
+                       combine(combine(combine(datasets::LargeWinogradConvolutionLayer1x3Dataset(),
+                                       framework::dataset::make("DataType", { DataType::F32 })),
+                                       framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+                                       framework::dataset::make("DataLayout", { DataLayout::NCHW })))
+{
+    // Validate output
+    validate(CLAccessor(_target), _reference, tolerance_convolution_layer_f32);
+}
+TEST_SUITE_END() // Conv1x3
+
 TEST_SUITE(Conv5x5)
 FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT,
                        combine(combine(combine(datasets::SmallWinogradConvolutionLayer5x5Dataset(),
                                                framework::dataset::make("DataType", { DataType::F32 })),
-                                       framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
-                               framework::dataset::make("DataLayout", { DataLayout::NCHW })))
+                                               framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+                                               framework::dataset::make("DataLayout", { DataLayout::NCHW })))
 
 {
     // Validate output
@@ -414,8 +634,8 @@
 FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY,
                        combine(combine(combine(datasets::LargeWinogradConvolutionLayer5x5Dataset(),
                                                framework::dataset::make("DataType", { DataType::F32 })),
-                                       framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
-                               framework::dataset::make("DataLayout", { DataLayout::NCHW })))
+                                               framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+                                               framework::dataset::make("DataLayout", { DataLayout::NCHW })))
 
 {
     // Validate output
@@ -424,7 +644,6 @@
 TEST_SUITE_END() // Conv5x5
 
 TEST_SUITE_END() // ConvolutionLayer
-
 TEST_SUITE_END() // Winograd
 TEST_SUITE_END() // CL
 } // namespace validation
diff --git a/tests/validation/Helpers.cpp b/tests/validation/Helpers.cpp
index e2415a2..ff69b1c 100644
--- a/tests/validation/Helpers.cpp
+++ b/tests/validation/Helpers.cpp
@@ -215,7 +215,7 @@
 template <typename T>
 void get_tile(const SimpleTensor<T> &in, SimpleTensor<T> &tile, const Coordinates &coord)
 {
-    ARM_COMPUTE_ERROR_ON(tile.shape().num_dimensions() != 2);
+    ARM_COMPUTE_ERROR_ON(tile.shape().num_dimensions() > 2);
 
     const int w_tile = tile.shape()[0];
     const int h_tile = tile.shape()[1];
@@ -272,7 +272,36 @@
     }
 }
 
+template <typename T>
+void zeros(SimpleTensor<T> &in, const Coordinates &anchor, const TensorShape &shape)
+{
+    ARM_COMPUTE_ERROR_ON(anchor.num_dimensions() != shape.num_dimensions());
+    ARM_COMPUTE_ERROR_ON(in.shape().num_dimensions() > 2);
+    ARM_COMPUTE_ERROR_ON(shape.num_dimensions() > 2);
+
+    // Check if with the dimensions greater than 2 we could have out-of-bound reads
+    for(size_t d = 0; d < Coordinates::num_max_dimensions; ++d)
+    {
+        if(anchor[d] < 0 || ((anchor[d] + shape[d]) > in.shape()[d]))
+        {
+            ARM_COMPUTE_ERROR("anchor[d] < 0 || (anchor[d] + shape[d]) > in.shape()[d]");
+        }
+    }
+
+    // Get input pointer
+    auto in_ptr = static_cast<T *>(in(anchor[0] + anchor[1] * in.shape()[0]));
+
+    const unsigned int n = in.shape()[0];
+
+    for(unsigned int y = 0; y < shape[1]; ++y)
+    {
+        std::fill(in_ptr, in_ptr + shape[0], 0);
+        in_ptr += n;
+    }
+}
+
 template void get_tile(const SimpleTensor<float> &in, SimpleTensor<float> &roi, const Coordinates &coord);
+template void zeros(SimpleTensor<float> &in, const Coordinates &anchor, const TensorShape &shape);
 } // namespace validation
 } // namespace test
 } // namespace arm_compute
diff --git a/tests/validation/Helpers.h b/tests/validation/Helpers.h
index 49432d6..88262d5 100644
--- a/tests/validation/Helpers.h
+++ b/tests/validation/Helpers.h
@@ -259,6 +259,15 @@
  */
 template <typename T>
 void get_tile(const SimpleTensor<T> &in, SimpleTensor<T> &tile, const Coordinates &coord);
+
+/** Fill with zeros the input tensor in the area defined by anchor and shape
+ *
+ * @param[in]  in     Input tensor to fill with zeros
+ * @param[out] anchor Starting point of the zeros area
+ * @param[in]  shape  Ending point of the zeros area
+ */
+template <typename T>
+void zeros(SimpleTensor<T> &in, const Coordinates &anchor, const TensorShape &shape);
 } // namespace validation
 } // namespace test
 } // namespace arm_compute
diff --git a/tests/validation/fixtures/WinogradConvolutionLayerFixture.h b/tests/validation/fixtures/WinogradConvolutionLayerFixture.h
index aca24f1..ac168eb 100644
--- a/tests/validation/fixtures/WinogradConvolutionLayerFixture.h
+++ b/tests/validation/fixtures/WinogradConvolutionLayerFixture.h
@@ -259,7 +259,18 @@
             fill(bias, 2, 0.f, 0.f);
         }
 
-        WinogradInfo winograd_info(Size2D(4U, 4U),
+        // Set output tile
+        Size2D output_tile(4U, 4U);
+        if(weights_shape[0] == 1)
+        {
+            output_tile.width = 1;
+        }
+        else if(weights_shape[1] == 1)
+        {
+            output_tile.height = 1;
+        }
+
+        WinogradInfo winograd_info(output_tile,
                                    Size2D(weights_shape[0], weights_shape[1]),
                                    Size2D(input_shape[0], input_shape[1]),
                                    info,
diff --git a/tests/validation/reference/Winograd.cpp b/tests/validation/reference/Winograd.cpp
index 197d218..5be4fe2 100644
--- a/tests/validation/reference/Winograd.cpp
+++ b/tests/validation/reference/Winograd.cpp
@@ -29,6 +29,7 @@
 #include "arm_compute/core/Types.h"
 
 #include <algorithm>
+#include <cmath>
 
 namespace arm_compute
 {
@@ -142,12 +143,24 @@
     {
         { WinogradKey(std::pair<int, int>(2, 2), std::pair<int, int>(3, 3), WinogradTransformType::INPUT), imatrix2x2_3x3 },
         { WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(3, 3), WinogradTransformType::INPUT), imatrix4x4_3x3 },
+        { WinogradKey(std::pair<int, int>(2, 1), std::pair<int, int>(3, 1), WinogradTransformType::INPUT), imatrix2x2_3x3 },
+        { WinogradKey(std::pair<int, int>(4, 1), std::pair<int, int>(3, 1), WinogradTransformType::INPUT), imatrix4x4_3x3 },
+        { WinogradKey(std::pair<int, int>(1, 2), std::pair<int, int>(1, 3), WinogradTransformType::INPUT), imatrix2x2_3x3 },
+        { WinogradKey(std::pair<int, int>(1, 4), std::pair<int, int>(1, 3), WinogradTransformType::INPUT), imatrix4x4_3x3 },
         { WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5), WinogradTransformType::INPUT), imatrix4x4_5x5 },
         { WinogradKey(std::pair<int, int>(2, 2), std::pair<int, int>(3, 3), WinogradTransformType::FILTER), fmatrix2x2_3x3 },
         { WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(3, 3), WinogradTransformType::FILTER), fmatrix4x4_3x3 },
+        { WinogradKey(std::pair<int, int>(2, 1), std::pair<int, int>(3, 1), WinogradTransformType::FILTER), fmatrix2x2_3x3 },
+        { WinogradKey(std::pair<int, int>(4, 1), std::pair<int, int>(3, 1), WinogradTransformType::FILTER), fmatrix4x4_3x3 },
+        { WinogradKey(std::pair<int, int>(1, 2), std::pair<int, int>(1, 3), WinogradTransformType::FILTER), fmatrix2x2_3x3 },
+        { WinogradKey(std::pair<int, int>(1, 4), std::pair<int, int>(1, 3), WinogradTransformType::FILTER), fmatrix4x4_3x3 },
         { WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5), WinogradTransformType::FILTER), fmatrix4x4_5x5 },
         { WinogradKey(std::pair<int, int>(2, 2), std::pair<int, int>(3, 3), WinogradTransformType::OUTPUT), omatrix2x2_3x3 },
         { WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(3, 3), WinogradTransformType::OUTPUT), omatrix4x4_3x3 },
+        { WinogradKey(std::pair<int, int>(2, 1), std::pair<int, int>(3, 1), WinogradTransformType::OUTPUT), omatrix2x2_3x3 },
+        { WinogradKey(std::pair<int, int>(4, 1), std::pair<int, int>(3, 1), WinogradTransformType::OUTPUT), omatrix4x4_3x3 },
+        { WinogradKey(std::pair<int, int>(1, 2), std::pair<int, int>(1, 3), WinogradTransformType::OUTPUT), omatrix2x2_3x3 },
+        { WinogradKey(std::pair<int, int>(1, 4), std::pair<int, int>(1, 3), WinogradTransformType::OUTPUT), omatrix4x4_3x3 },
         { WinogradKey(std::pair<int, int>(4, 4), std::pair<int, int>(5, 5), WinogradTransformType::OUTPUT), omatrix4x4_5x5 },
     };
 
@@ -175,6 +188,20 @@
 } // namespace
 
 template <typename T>
+void print_tile(SimpleTensor<T> &in)
+{
+    for(int y = 0; y < in.shape()[1]; y++)
+    {
+        for(int x = 0; x < in.shape()[0]; x++)
+        {
+            std::cout << in[x + y * in.shape()[0]] << " ";
+        }
+
+        std::cout << std::endl;
+    }
+}
+
+template <typename T>
 SimpleTensor<T> winograd_input_transform(const SimpleTensor<T> &in, const TensorShape &output_shape, const WinogradInfo &winograd_info)
 {
     ARM_COMPUTE_ERROR_ON(in.data_layout() != DataLayout::NCHW);
@@ -189,7 +216,10 @@
     const unsigned int tile_w = output_tile_size.width + kernel_size.width - 1;
     const unsigned int tile_h = output_tile_size.height + kernel_size.height - 1;
 
-    TensorShape tile_dims(tile_w, tile_h);
+    // Get the maximum dimension from the tile size
+    const unsigned int tile_max_dim = std::max(tile_w, tile_h);
+
+    TensorShape tile_dims(tile_max_dim, tile_max_dim);
 
     // Simple tensor for the input tile
     SimpleTensor<T> src_tile{ tile_dims, in.data_type() };
@@ -217,11 +247,46 @@
     const int in_d        = in.shape().z();
     const int out_d       = out.shape().z();
     const int num_batches = in.shape().total_size() / (in_w * in_h * in_d);
-    const int num_tiles_x = std::ceil((in_w - (kernel_size.width - 1) + conv_info.pad_left() + conv_info.pad_right()) / static_cast<float>(output_tile_size.width));
-    const int num_tiles_y = std::ceil((in_h - (kernel_size.height - 1) + conv_info.pad_top() + conv_info.pad_bottom()) / static_cast<float>(output_tile_size.height));
     const int step_x      = output_tile_size.width;
     const int step_y      = output_tile_size.height;
 
+    // Compute the number of output tiles along the x and y direction of size "output_tile_size"
+    const Size2D num_tiles = compute_winograd_convolution_tiles(Size2D(in_w, in_h),
+                                                                kernel_size,
+                                                                output_tile_size,
+                                                                conv_info);
+
+    const int num_tiles_x = num_tiles.width;
+    const int num_tiles_y = num_tiles.height;
+
+    // In case of 1D convolution, the input tile has to be partially filled with zeros
+    int start_x_zero = 0;
+    int start_y_zero = 0;
+    int end_x_zero   = 0;
+    int end_y_zero   = 0;
+
+    if(output_tile_size.width == 1)
+    {
+        start_x_zero = 1;
+        start_y_zero = 0;
+        end_x_zero   = tile_max_dim - 1;
+        end_y_zero   = tile_max_dim;
+    }
+    else if(output_tile_size.height == 1)
+    {
+        start_x_zero = 0;
+        start_y_zero = 1;
+        end_x_zero   = tile_max_dim;
+        end_y_zero   = tile_max_dim - 1;
+    }
+
+    // Set the anchor and shape of the zeros area
+    const Coordinates anchor_zeros(start_x_zero, start_y_zero);
+    const TensorShape shape_zeros(end_x_zero, end_y_zero);
+
+    // If we have a vertical filter (i.e. 1x3, 1x5,..), we need to take the elements along the y direction (step = width of the output tile)
+    const int step_y_transf_tile = kernel_size.width == 1 ? tile_max_dim : 1;
+
     ARM_COMPUTE_ERROR_ON((num_tiles_x * num_tiles_y) != static_cast<int>(out.shape().y()));
 
     for(int b = 0; b < num_batches; ++b)
@@ -238,6 +303,9 @@
                     // Get the tile from the input tensor
                     get_tile(in, src_tile, Coordinates(xi, yi, z, b));
 
+                    // Fill partially with zeros in case of 1D convolution
+                    zeros(src_tile, anchor_zeros, shape_zeros);
+
                     // Compute the transformation
                     matrix_multiply(matrix, src_tile, tmp_tile);
                     matrix_multiply(tmp_tile, matrix_transposed, dst_tile);
@@ -247,7 +315,7 @@
                     {
                         int xo = z;
                         int yo = x + y * num_tiles_x;
-                        out[coords2index(out.shape(), Coordinates(xo, yo, i, b))] = dst_tile[i];
+                        out[coords2index(out.shape(), Coordinates(xo, yo, i, b))] = dst_tile[i * step_y_transf_tile];
                     }
                 }
             }
@@ -268,27 +336,31 @@
     const Size2D output_tile_size = winograd_info.output_tile_size;
     const Size2D kernel_size      = winograd_info.kernel_size;
 
-    TensorShape kernel_tile_dims(kernel_size.width, kernel_size.height);
-
     // Calculate dimensions for the tile
     const unsigned int input_tile_w    = output_tile_size.width + kernel_size.width - 1;
     const unsigned int input_tile_h    = output_tile_size.height + kernel_size.height - 1;
     const unsigned int input_tile_area = input_tile_w * input_tile_h;
 
+    // Get the maximum dimension from the filter size
+    const unsigned int kernel_max_dim = std::max(kernel_size.width, kernel_size.height);
+
+    // Get the maximum dimension from the input tile
+    const unsigned int input_tile_max_dim = std::max(input_tile_w, input_tile_h);
+
     // Simple tensor for the input tile
-    SimpleTensor<T> input_tile{ kernel_tile_dims, in.data_type(), 1 };
+    SimpleTensor<T> input_tile{ TensorShape(kernel_max_dim, kernel_max_dim), in.data_type(), 1 };
 
     // Simple tensor for the transformation matrix
-    SimpleTensor<T> trans_matrix{ TensorShape(kernel_tile_dims[0], input_tile_w), in.data_type(), 1 };
+    SimpleTensor<T> trans_matrix{ TensorShape(kernel_max_dim, input_tile_max_dim), in.data_type(), 1 };
 
     // Simple tensor for the transformation matrix transpose
-    SimpleTensor<T> trans_matrix_transposed{ TensorShape(input_tile_w, kernel_tile_dims[0]), in.data_type(), 1 };
+    SimpleTensor<T> trans_matrix_transposed{ TensorShape(input_tile_max_dim, kernel_max_dim), in.data_type(), 1 };
 
     // Simple tensor for the temporary tile
-    SimpleTensor<T> tmp_tile{ TensorShape(kernel_tile_dims[0], input_tile_w), in.data_type(), 1 };
+    SimpleTensor<T> tmp_tile{ TensorShape(kernel_max_dim, input_tile_max_dim), in.data_type(), 1 };
 
     // Simple tensor for the output tile
-    SimpleTensor<T> transf_tile{ TensorShape(input_tile_w, input_tile_w), in.data_type(), 1 };
+    SimpleTensor<T> transf_tile{ TensorShape(input_tile_max_dim, input_tile_max_dim), in.data_type(), 1 };
 
     // Initialize matrix for the filter transform
     initialize_matrix_transform(trans_matrix, output_tile_size, kernel_size, WinogradTransformType::FILTER);
@@ -300,6 +372,9 @@
     const int num_filters  = in.shape()[3];
     const int num_batches  = in.shape().total_size() / (kernel_size.area() * num_channels * num_filters);
 
+    // If we have a vertical filter (i.e. 1x3, 1x5,..), we need to take the elements along the y direction (step_y_transf_tile = width of the output tile)
+    const int step_y_transf_tile = kernel_size.width == 1 ? input_tile_max_dim : 1;
+
     for(int n = 0; n < num_batches; ++n)
     {
         for(int w = 0; w < num_filters; ++w)
@@ -321,7 +396,7 @@
                 // Store the values across the channels
                 for(unsigned int i = 0; i < input_tile_area; ++i)
                 {
-                    out[output_offset + i * num_filters * num_channels] = transf_tile[i];
+                    out[output_offset + i * num_filters * num_channels] = transf_tile[i * step_y_transf_tile];
                 }
             }
         }
@@ -350,15 +425,19 @@
     ARM_COMPUTE_ERROR_ON(in.shape()[2] != (in_tile_w * in_tile_h));
     ARM_COMPUTE_ERROR_ON(in.shape()[0] != out.shape()[get_data_layout_dimension_index(winograd_info.output_data_layout, DataLayoutDimension::CHANNEL)]);
 
+    // Get the maximum dimension from the tile size
+    const unsigned int in_tile_max_dim  = std::max(in_tile_w, in_tile_h);
+    const unsigned int out_tile_max_dim = std::max(output_tile_size.width, output_tile_size.height);
+
     // Compute tile dimensions
     // Input tile dimensions
-    TensorShape in_tile_dims(in_tile_w, in_tile_h);
+    TensorShape in_tile_dims(in_tile_max_dim, in_tile_max_dim);
 
     // Output tile dimensions
-    TensorShape out_tile_dims(output_tile_size.width, output_tile_size.height);
+    TensorShape out_tile_dims(out_tile_max_dim, out_tile_max_dim);
 
     // Transformation matrix dimensions
-    TensorShape tr_tile_dims(in_tile_w, output_tile_size.width);
+    TensorShape tr_tile_dims(in_tile_max_dim, out_tile_max_dim);
 
     // Create tensors
     // Simple tensor for the input tile
@@ -400,15 +479,24 @@
     const int stridez_out = stridey_out * h_out;
     const int stridew_out = stridez_out * c_out;
 
-    // Compute number of elements to process in the X and Y direction
-    const int num_elements_x = input_dimensions.width - (kernel_size.width - 1) + conv_info.pad_left() + conv_info.pad_right();
-    const int num_elements_y = input_dimensions.height - (kernel_size.height - 1) + conv_info.pad_top() + conv_info.pad_bottom();
-    const int num_tiles_x    = std::ceil(num_elements_x / static_cast<float>(output_tile_size.width));
-    const int num_tiles_y    = std::ceil(num_elements_y / static_cast<float>(output_tile_size.height));
+    // Compute the number of output tiles along the x and y direction of size "output_tile_size"
+    const Size2D num_tiles = compute_winograd_convolution_tiles(Size2D(input_dimensions.width, input_dimensions.height),
+                                                                kernel_size,
+                                                                output_tile_size,
+                                                                conv_info);
+
+    const int num_tiles_x = num_tiles.width;
+    const int num_tiles_y = num_tiles.height;
 
     ARM_COMPUTE_UNUSED(num_tiles_y);
     ARM_COMPUTE_ERROR_ON(in.shape()[1] != static_cast<unsigned int>(num_tiles_x * num_tiles_y));
 
+    // If we have a vertical filter (i.e. 1x3, 1x5,..), we still need to take the elements along the x direction (step_y_transf_tile = 1)
+    const int step_y_transf_tile = kernel_size.width == 1 ? 1 : output_tile.shape()[0];
+
+    // Initialize with zeros the input tile
+    zeros(input_tile, Coordinates(0, 0), input_tile.shape());
+
     for(int n = 0; n < num_batches; ++n)
     {
         for(int y = 0; y < h_in; ++y)
@@ -441,7 +529,7 @@
                         // Check out-of-bound writes
                         if((xo + xi < w_out) && (yo + yi < h_out))
                         {
-                            out[output_offset + yi * stridey_out + xi] = output_tile[xi + yi * out_tile_w];
+                            out[output_offset + yi * stridey_out + xi] = output_tile[xi + yi * step_y_transf_tile];
 
                             // Add bias
                             out[output_offset + yi * stridey_out + xi] += b[zo];