COMPMID-1298: Fuse ReLu activation in CLWinogradOutputTransform

Change-Id: I9e6e43a5839d04c2e4b4552c05446efb0a5074cf
Reviewed-on: https://review.mlplatform.org/232
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>

9 files changed, 241 insertions, 146 deletions

diff --git a/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h b/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h
index 3bbbb5834c..bdb230d645 100644
--- a/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h
+++ b/arm_compute/core/CL/kernels/CLWinogradOutputTransformKernel.h
@@ -63,8 +63,10 @@ public:
      * @param[in]  bias          Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. It can be a nullptr. Data type supported: as @p input
      * @param[out] output        The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_output_transform_shape. Data types supported: Same as @p input
      * @param[in]  winograd_info Contains Winograd's information described in @ref WinogradInfo
+     * @param[in]  act_info      (Optional) Activation layer information in case of a fused activation.
      */
-    void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const WinogradInfo &winograd_info);
+    void configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info = ActivationLayerInfo());
+
     /** Static function to check if given info will lead to a valid configuration of @ref CLWinogradOutputTransformKernel
      *
      * @note Winograd output transform supports the following configurations for NCWH data layout
@@ -82,10 +84,11 @@ public:
      * @param[in]  bias          Biases tensor. Shared biases supported. Biases are 1D tensor with dimensions [OFM]. It can be a nullptr. Data type supported: as @p input
      * @param[out] output        The output tensor. The shape for this tensor can be calculated using the utility function @p compute_winograd_output_transform_shape. Data types supported: Same as @p input
      * @param[in]  winograd_info Contains Winograd's information described in @ref WinogradInfo
+     * @param[in]  act_info      (Optional) Activation layer information in case of a fused activation @ref ActivationLayerInfo. Only RELU, BOUNDED_RELU, LU_BOUNDED_RELU, LEAKY_RELU and SOFT_RELU supported.
      *
      * @return a status
      */
-    static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info);
+    static Status validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info = ActivationLayerInfo());
 
     // Inherited methods overridden:
     void run(const Window &window, cl::CommandQueue &queue) override;
diff --git a/arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h b/arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h
index 395f59500b..f11eb2a335 100644
--- a/arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h
+++ b/arm_compute/runtime/CL/functions/CLWinogradConvolutionLayer.h
@@ -108,13 +108,11 @@ private:
     CLWinogradInputTransform        _input_transform;
     CLWinogradFilterTransformKernel _filter_transform;
     CLWinogradOutputTransformKernel _output_transform;
-    CLActivationLayer               _activationlayer_function;
     CLTensor                        _input0;
     CLTensor                        _input1;
     CLTensor                        _batched_mm_output;
     const ICLTensor                *_original_weights;
     bool                            _is_prepared;
-    bool                            _is_activationlayer_enabled;
 };
-}
+} // namespace arm_compute
 #endif /* __ARM_COMPUTE_CLWINOGRADCONVOLUTIONLAYER_H__ */
diff --git a/src/core/CL/cl_kernels/activation_helpers.h b/src/core/CL/cl_kernels/activation_helpers.h
index dfab082381..9d4af8497a 100644
--- a/src/core/CL/cl_kernels/activation_helpers.h
+++ b/src/core/CL/cl_kernels/activation_helpers.h
@@ -70,7 +70,7 @@ inline TYPE lrelu_op(TYPE x)
 // Soft RELU Activation
 inline TYPE srelu_op(TYPE x)
 {
-    return LOG_OP(ADD_OP((TYPE)CONST_ONE, EXP_OP(x)));
+    return CONVERT(LOG_OP(ADD_OP((VEC_DATA_TYPE(float, VEC_SIZE))CONST_ONE, EXP_OP(CONVERT(x, VEC_DATA_TYPE(float, VEC_SIZE))))), TYPE);
 }
 // Absolute Activation
 inline TYPE abs_op(TYPE x)
diff --git a/src/core/CL/cl_kernels/helpers.h b/src/core/CL/cl_kernels/helpers.h
index 7ee97d9bbc..180bd50528 100644
--- a/src/core/CL/cl_kernels/helpers.h
+++ b/src/core/CL/cl_kernels/helpers.h
@@ -50,6 +50,9 @@
 #define VSTORE_STR(size) vstore##size
 #define VSTORE(size) VSTORE_STR(size)
 
+#define float1 float
+#define half1 half
+
 #define VEC_DATA_TYPE_STR(type, size) type##size
 #define VEC_DATA_TYPE(type, size) VEC_DATA_TYPE_STR(type, size)
 
diff --git a/src/core/CL/cl_kernels/winograd_output_transform.cl b/src/core/CL/cl_kernels/winograd_output_transform.cl
index f52b027420..e979978fa2 100644
--- a/src/core/CL/cl_kernels/winograd_output_transform.cl
+++ b/src/core/CL/cl_kernels/winograd_output_transform.cl
@@ -23,7 +23,15 @@
  */
 #include "helpers.h"
 
+#if defined(FUSED_ACTIVATION)
+#include "activation_layer.cl"
+#define ACTIVATION_FUNC(x) ACTIVATION_OP(FUSED_ACTIVATION, x)
+#else /* defined(FUSED_ACTIVATION) */
+#define ACTIVATION_FUNC(x) (x)
+#endif /* defined(FUSED_ACTIVATION) */
+
 #if defined(NUM_TILES_X) && defined(OUTPUT_TILE_W) && defined(OUTPUT_TILE_H)
+#if defined(VEC_SIZE) && VEC_SIZE == 2
 /** 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
@@ -32,6 +40,10 @@
  * @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
  * @note The data type must be passed at compile time using -DDATA_TYPE e.g. -DDATA_TYPE=float. Supported data types: float/half.
+ * @note It is possible to select the activation function to apply using -DFUSED_ACTIVATION e.g. -DFUSED_ACTIVATION=relu
+ * @note A, B variables required by some activation functions are set using -DA_VAL= and -DB_VAL= respectively.
+ * @note Vector size should be given as a preprocessor argument using -DVEC_SIZE=size. Accepted values are -DVEC_SIZE=2 (for output_tile_size 2x2, 2x1, 1x2) and -DVEC_SIZE=4 (for output_tile_size 4x4, 4x1, 1x4)
+ * @note Select data type should be given too with -DSELECT_DATA_TYPE e.g -DSELECT_DATA_TYPE=int
  *
  * @param[in]  src_ptr                           Pointer to the source tensor. Supported data types: F32/F16
  * @param[in]  src_stride_x                      Stride of the source tensor in X dimension (in bytes)
@@ -86,6 +98,7 @@ __kernel void winograd_output_transform_2x2_3x3_nchw(
     float out00 = d00 + d01 + d02;
     float out01 = d01 - d02 - d03;
 #else  // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
+
     DATA_TYPE d10 = *((__global DATA_TYPE *)(src_addr + 4 * src_stride_z));
     DATA_TYPE d11 = *((__global DATA_TYPE *)(src_addr + 5 * src_stride_z));
     DATA_TYPE d12 = *((__global DATA_TYPE *)(src_addr + 6 * src_stride_z));
@@ -150,10 +163,12 @@ __kernel void winograd_output_transform_2x2_3x3_nchw(
 
     // Store the output tile
 #if defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
-    *((__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y)) = (DATA_TYPE)out00;
-    *((__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y)) = (DATA_TYPE)out01;
+    const const VEC_DATA_TYPE(DATA_TYPE, 2)
+    out0_dt                                            = ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 2))(out00, out01), VEC_DATA_TYPE(DATA_TYPE, 2)));
+    *((__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y)) = out0_dt.s0;
+    *((__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y)) = out0_dt.s1;
 #else  // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
-    vstore2((VEC_DATA_TYPE(DATA_TYPE, 2))(out00, out01), 0, (__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y));
+    vstore2(ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 2))(out00, out01), VEC_DATA_TYPE(DATA_TYPE, 2))), 0, (__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y));
 #endif // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
 
 #if !defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
@@ -162,11 +177,12 @@ __kernel void winograd_output_transform_2x2_3x3_nchw(
     out10 += (DATA_TYPE)b;
     out11 += (DATA_TYPE)b;
 #endif // defined(HAS_BIAS)
-
-    vstore2((VEC_DATA_TYPE(DATA_TYPE, 2))((DATA_TYPE)out10, (DATA_TYPE)out11), 0, (__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y));
+    vstore2(ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 2))(out10, out11), VEC_DATA_TYPE(DATA_TYPE, 2))), 0, (__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y));
 #endif // !defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
 }
+#endif // defined(VEC_SIZE) && VEC_SIZE == 2
 
+#if defined(VEC_SIZE) && VEC_SIZE == 4
 /** 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
@@ -230,6 +246,7 @@ __kernel void winograd_output_transform_4x4_3x3_nchw(
     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)
+
     DATA_TYPE d10 = *((__global DATA_TYPE *)(src_addr + 6 * src_stride_z));
     DATA_TYPE d11 = *((__global DATA_TYPE *)(src_addr + 7 * src_stride_z));
     DATA_TYPE d12 = *((__global DATA_TYPE *)(src_addr + 8 * src_stride_z));
@@ -351,12 +368,14 @@ __kernel void winograd_output_transform_4x4_3x3_nchw(
 
     // Store the output tile
 #if defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
-    *((__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y)) = (DATA_TYPE)out00;
-    *((__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y)) = (DATA_TYPE)out01;
-    *((__global DATA_TYPE *)(dst_addr + 2 * dst_stride_y)) = (DATA_TYPE)out02;
-    *((__global DATA_TYPE *)(dst_addr + 3 * dst_stride_y)) = (DATA_TYPE)out03;
+    VEC_DATA_TYPE(DATA_TYPE, 4)
+    out0_dt                                                = ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out00, out01, out02, out03), VEC_DATA_TYPE(DATA_TYPE, 4)));
+    *((__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y)) = out0_dt.s0;
+    *((__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y)) = out0_dt.s1;
+    *((__global DATA_TYPE *)(dst_addr + 2 * dst_stride_y)) = out0_dt.s2;
+    *((__global DATA_TYPE *)(dst_addr + 3 * dst_stride_y)) = out0_dt.s3;
 #else  // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
-    vstore4((VEC_DATA_TYPE(DATA_TYPE, 4))((DATA_TYPE)out00, (DATA_TYPE)out01, (DATA_TYPE)out02, (DATA_TYPE)out03), 0, (__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y));
+    vstore4(ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out00, out01, out02, out03), VEC_DATA_TYPE(DATA_TYPE, 4))), 0, (__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y));
 #endif // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
 
 #if !defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
@@ -377,9 +396,9 @@ __kernel void winograd_output_transform_4x4_3x3_nchw(
     out32 += (float)b;
     out33 += (float)b;
 #endif // defined(HAS_BIAS)
-    vstore4((VEC_DATA_TYPE(DATA_TYPE, 4))((DATA_TYPE)out10, (DATA_TYPE)out11, (DATA_TYPE)out12, (DATA_TYPE)out13), 0, (__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y));
-    vstore4((VEC_DATA_TYPE(DATA_TYPE, 4))((DATA_TYPE)out20, (DATA_TYPE)out21, (DATA_TYPE)out22, (DATA_TYPE)out23), 0, (__global DATA_TYPE *)(dst_addr + 2 * dst_stride_y));
-    vstore4((VEC_DATA_TYPE(DATA_TYPE, 4))((DATA_TYPE)out30, (DATA_TYPE)out31, (DATA_TYPE)out32, (DATA_TYPE)out33), 0, (__global DATA_TYPE *)(dst_addr + 3 * dst_stride_y));
+    vstore4(ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out10, out11, out12, out13), VEC_DATA_TYPE(DATA_TYPE, 4))), 0, (__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y));
+    vstore4(ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out20, out21, out22, out23), VEC_DATA_TYPE(DATA_TYPE, 4))), 0, (__global DATA_TYPE *)(dst_addr + 2 * dst_stride_y));
+    vstore4(ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out30, out31, out32, out33), VEC_DATA_TYPE(DATA_TYPE, 4))), 0, (__global DATA_TYPE *)(dst_addr + 3 * dst_stride_y));
 #endif // !defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
 }
 
@@ -579,25 +598,29 @@ __kernel void winograd_output_transform_4x4_3x3_nhwc(
 #if defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
 #if defined(SRC_DEPTH)
     int4 offset = (int4)(dst_offset_first_element_in_bytes + x_out * sizeof(DATA_TYPE) + y_out * dst_stride_y + z_out * dst_stride_z + batch * dst_stride_w);
-#else /* defined(SRC_DEPTH) */
+#else                                                                               /* defined(SRC_DEPTH) */
     int4 offset = (int4)(dst_offset_first_element_in_bytes + x_out * sizeof(DATA_TYPE) + y_out * dst_stride_y + z_out * dst_stride_z);
-#endif /* defined(SRC_DEPTH) */
+#endif                                                                              /* defined(SRC_DEPTH) */
     offset = min(offset + (int4)(0, 1, 2, 3) * (int4)dst_stride_z, (int4)dst_size); // If address is beyond the last plane, clamp it to dst_size (which points to the last padding).
 
     // Store the 1x4 output tile
-    *((__global DATA_TYPE *)(dst_ptr + offset.s0)) = (DATA_TYPE)out00;
-    *((__global DATA_TYPE *)(dst_ptr + offset.s1)) = (DATA_TYPE)out01;
-    *((__global DATA_TYPE *)(dst_ptr + offset.s2)) = (DATA_TYPE)out02;
-    *((__global DATA_TYPE *)(dst_ptr + offset.s3)) = (DATA_TYPE)out03;
+    VEC_DATA_TYPE(DATA_TYPE, 4)
+    out0_dt                                        = ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out00, out01, out02, out03), VEC_DATA_TYPE(DATA_TYPE, 4)));
+    *((__global DATA_TYPE *)(dst_ptr + offset.s0)) = out0_dt.s0;
+    *((__global DATA_TYPE *)(dst_ptr + offset.s1)) = out0_dt.s1;
+    *((__global DATA_TYPE *)(dst_ptr + offset.s2)) = out0_dt.s2;
+    *((__global DATA_TYPE *)(dst_ptr + offset.s3)) = out0_dt.s3;
 #elif defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL)
     // Store the 4x1 output tile
     int offset = dst_offset_first_element_in_bytes + x_out * sizeof(DATA_TYPE) + y_out * dst_stride_y + z_out * dst_stride_z;
     int mult_y = min(dst_size - offset, 1);
 
-    *((__global DATA_TYPE *)(dst_ptr + mult_y * 0 * dst_stride_y + offset)) = (DATA_TYPE)out00;
-    *((__global DATA_TYPE *)(dst_ptr + mult_y * 1 * dst_stride_y + offset)) = (DATA_TYPE)out01;
-    *((__global DATA_TYPE *)(dst_ptr + mult_y * 2 * dst_stride_y + offset)) = (DATA_TYPE)out02;
-    *((__global DATA_TYPE *)(dst_ptr + mult_y * 3 * dst_stride_y + offset)) = (DATA_TYPE)out03;
+    VEC_DATA_TYPE(DATA_TYPE, 4)
+    out0_dt                                                                 = ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out00, out01, out02, out03), VEC_DATA_TYPE(DATA_TYPE, 4)));
+    *((__global DATA_TYPE *)(dst_ptr + mult_y * 0 * dst_stride_y + offset)) = out0_dt.s0;
+    *((__global DATA_TYPE *)(dst_ptr + mult_y * 1 * dst_stride_y + offset)) = out0_dt.s1;
+    *((__global DATA_TYPE *)(dst_ptr + mult_y * 2 * dst_stride_y + offset)) = out0_dt.s2;
+    *((__global DATA_TYPE *)(dst_ptr + mult_y * 3 * dst_stride_y + offset)) = out0_dt.s3;
 #else // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL)
     // Get output address
 #if defined(SRC_DEPTH)
@@ -609,22 +632,30 @@ __kernel void winograd_output_transform_4x4_3x3_nhwc(
     int4 mult_y = min((int4)dst_size - offset, (int4)1);                                 // If out of bound, we don't want to increase dst_stride_y, so we set the multiplier to 0. It will be 1 otherwise.
 
     // Store the 4x4 output tile
-    *((__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 0 * dst_stride_y + offset.s0)) = (DATA_TYPE)out00;
-    *((__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 1 * dst_stride_y + offset.s0)) = (DATA_TYPE)out01;
-    *((__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 2 * dst_stride_y + offset.s0)) = (DATA_TYPE)out02;
-    *((__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 3 * dst_stride_y + offset.s0)) = (DATA_TYPE)out03;
-    *((__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 0 * dst_stride_y + offset.s1)) = (DATA_TYPE)out10;
-    *((__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 1 * dst_stride_y + offset.s1)) = (DATA_TYPE)out11;
-    *((__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 2 * dst_stride_y + offset.s1)) = (DATA_TYPE)out12;
-    *((__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 3 * dst_stride_y + offset.s1)) = (DATA_TYPE)out13;
-    *((__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 0 * dst_stride_y + offset.s2)) = (DATA_TYPE)out20;
-    *((__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 1 * dst_stride_y + offset.s2)) = (DATA_TYPE)out21;
-    *((__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 2 * dst_stride_y + offset.s2)) = (DATA_TYPE)out22;
-    *((__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 3 * dst_stride_y + offset.s2)) = (DATA_TYPE)out23;
-    *((__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 0 * dst_stride_y + offset.s3)) = (DATA_TYPE)out30;
-    *((__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 1 * dst_stride_y + offset.s3)) = (DATA_TYPE)out31;
-    *((__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 2 * dst_stride_y + offset.s3)) = (DATA_TYPE)out32;
-    *((__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 3 * dst_stride_y + offset.s3)) = (DATA_TYPE)out33;
+    VEC_DATA_TYPE(DATA_TYPE, 4)
+    out0_dt = ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out00, out01, out02, out03), VEC_DATA_TYPE(DATA_TYPE, 4)));
+    VEC_DATA_TYPE(DATA_TYPE, 4)
+    out1_dt = ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out10, out11, out12, out13), VEC_DATA_TYPE(DATA_TYPE, 4)));
+    VEC_DATA_TYPE(DATA_TYPE, 4)
+    out2_dt = ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out20, out21, out22, out23), VEC_DATA_TYPE(DATA_TYPE, 4)));
+    VEC_DATA_TYPE(DATA_TYPE, 4)
+    out3_dt                                                                       = ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out30, out31, out32, out33), VEC_DATA_TYPE(DATA_TYPE, 4)));
+    *((__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 0 * dst_stride_y + offset.s0)) = out0_dt.s0;
+    *((__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 1 * dst_stride_y + offset.s0)) = out0_dt.s1;
+    *((__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 2 * dst_stride_y + offset.s0)) = out0_dt.s2;
+    *((__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 3 * dst_stride_y + offset.s0)) = out0_dt.s3;
+    *((__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 0 * dst_stride_y + offset.s1)) = out1_dt.s0;
+    *((__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 1 * dst_stride_y + offset.s1)) = out1_dt.s1;
+    *((__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 2 * dst_stride_y + offset.s1)) = out1_dt.s2;
+    *((__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 3 * dst_stride_y + offset.s1)) = out1_dt.s3;
+    *((__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 0 * dst_stride_y + offset.s2)) = out2_dt.s0;
+    *((__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 1 * dst_stride_y + offset.s2)) = out2_dt.s1;
+    *((__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 2 * dst_stride_y + offset.s2)) = out2_dt.s2;
+    *((__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 3 * dst_stride_y + offset.s2)) = out2_dt.s3;
+    *((__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 0 * dst_stride_y + offset.s3)) = out3_dt.s0;
+    *((__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 1 * dst_stride_y + offset.s3)) = out3_dt.s1;
+    *((__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 2 * dst_stride_y + offset.s3)) = out3_dt.s2;
+    *((__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 3 * dst_stride_y + offset.s3)) = out3_dt.s3;
 
 #endif // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL)
 }
@@ -690,6 +721,7 @@ __kernel void winograd_output_transform_4x4_5x5_nchw(
     Tensor4D       src             = CONVERT_TO_TENSOR4D_STRUCT(src, SRC_DEPTH);
     const __global uchar *src_addr = tensor4D_offset(&src, 0, 0, 0, 0);
 #else  /* defined(SRC_DEPTH) */
+
     Tensor3D       src             = CONVERT_TO_TENSOR3D_STRUCT(src);
     const __global uchar *src_addr = tensor3D_offset(&src, 0, 0, 0);
 #endif /* defined(SRC_DEPTH) */
@@ -706,6 +738,7 @@ __kernel void winograd_output_transform_4x4_5x5_nchw(
 #if defined(SRC_DEPTH)
     __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_out * sizeof(DATA_TYPE) + y_out * dst_stride_y + z_out * dst_stride_z + batch * dst_stride_w;
 #else  /* defined(SRC_DEPTH) */
+
     __global uchar *dst_addr = dst_ptr + dst_offset_first_element_in_bytes + x_out * sizeof(DATA_TYPE) + y_out * dst_stride_y + z_out * dst_stride_z;
 #endif /* defined(SRC_DEPTH) */
 
@@ -740,15 +773,18 @@ __kernel void winograd_output_transform_4x4_5x5_nchw(
 
     // Store the output tile
 #if defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
-    *((__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y)) = out00;
-    *((__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y)) = out01;
-    *((__global DATA_TYPE *)(dst_addr + 2 * dst_stride_y)) = out02;
-    *((__global DATA_TYPE *)(dst_addr + 3 * dst_stride_y)) = out03;
+    VEC_DATA_TYPE(DATA_TYPE, 4)
+    out0_dt                                                = ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out00, out01, out02, out03), VEC_DATA_TYPE(DATA_TYPE, 4)));
+    *((__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y)) = out0_dt.s0;
+    *((__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y)) = out0_dt.s1;
+    *((__global DATA_TYPE *)(dst_addr + 2 * dst_stride_y)) = out0_dt.s2;
+    *((__global DATA_TYPE *)(dst_addr + 3 * dst_stride_y)) = out0_dt.s3;
 #else  // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
-    vstore4((VEC_DATA_TYPE(DATA_TYPE, 4))(out00, out01, out02, out03), 0, (__global DATA_TYPE *)(dst_addr));
+    vstore4(ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out00, out01, out02, out03), VEC_DATA_TYPE(DATA_TYPE, 4))), 0, (__global DATA_TYPE *)(dst_addr));
 #endif // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
 
 #else // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
+
     DATA_TYPE d10 = *((__global DATA_TYPE *)(src_addr + 8 * src_stride_z));
     DATA_TYPE d11 = *((__global DATA_TYPE *)(src_addr + 9 * src_stride_z));
     DATA_TYPE d12 = *((__global DATA_TYPE *)(src_addr + 10 * src_stride_z));
@@ -859,10 +895,10 @@ __kernel void winograd_output_transform_4x4_5x5_nchw(
 #endif // defined(HAS_BIAS)
 
     // Store the output tile
-    vstore4((VEC_DATA_TYPE(DATA_TYPE, 4))((DATA_TYPE)out_col0.s0, (DATA_TYPE)out_col1.s0, (DATA_TYPE)out_col2.s0, (DATA_TYPE)out_col3.s0), 0, (__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y));
-    vstore4((VEC_DATA_TYPE(DATA_TYPE, 4))((DATA_TYPE)out_col0.s1, (DATA_TYPE)out_col1.s1, (DATA_TYPE)out_col2.s1, (DATA_TYPE)out_col3.s1), 0, (__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y));
-    vstore4((VEC_DATA_TYPE(DATA_TYPE, 4))((DATA_TYPE)out_col0.s2, (DATA_TYPE)out_col1.s2, (DATA_TYPE)out_col2.s2, (DATA_TYPE)out_col3.s2), 0, (__global DATA_TYPE *)(dst_addr + 2 * dst_stride_y));
-    vstore4((VEC_DATA_TYPE(DATA_TYPE, 4))((DATA_TYPE)out_col0.s3, (DATA_TYPE)out_col1.s3, (DATA_TYPE)out_col2.s3, (DATA_TYPE)out_col3.s3), 0, (__global DATA_TYPE *)(dst_addr + 3 * dst_stride_y));
+    vstore4(ACTIVATION_FUNC((VEC_DATA_TYPE(DATA_TYPE, 4))(out_col0.s0, out_col1.s0, out_col2.s0, out_col3.s0)), 0, (__global DATA_TYPE *)(dst_addr + 0 * dst_stride_y));
+    vstore4(ACTIVATION_FUNC((VEC_DATA_TYPE(DATA_TYPE, 4))(out_col0.s1, out_col1.s1, out_col2.s1, out_col3.s1)), 0, (__global DATA_TYPE *)(dst_addr + 1 * dst_stride_y));
+    vstore4(ACTIVATION_FUNC((VEC_DATA_TYPE(DATA_TYPE, 4))(out_col0.s2, out_col1.s2, out_col2.s2, out_col3.s2)), 0, (__global DATA_TYPE *)(dst_addr + 2 * dst_stride_y));
+    vstore4(ACTIVATION_FUNC((VEC_DATA_TYPE(DATA_TYPE, 4))(out_col0.s3, out_col1.s3, out_col2.s3, out_col3.s3)), 0, (__global DATA_TYPE *)(dst_addr + 3 * dst_stride_y));
 #endif // !defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) && !defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
 }
 
@@ -960,18 +996,21 @@ __kernel void winograd_output_transform_4x4_5x5_nhwc(
 #endif                                                                              /* defined(SRC_DEPTH) */
     offset = min(offset + (int4)(0, 1, 2, 3) * (int4)dst_stride_z, (int4)dst_size); // If address is beyond the last plane, clamp it to dst_size (which points to the last padding).
 
-    *(__global DATA_TYPE *)(dst_ptr + offset.s0) = (DATA_TYPE)out00;
-    *(__global DATA_TYPE *)(dst_ptr + offset.s1) = (DATA_TYPE)out01;
-    *(__global DATA_TYPE *)(dst_ptr + offset.s2) = (DATA_TYPE)out02;
-    *(__global DATA_TYPE *)(dst_ptr + offset.s3) = (DATA_TYPE)out03;
+    VEC_DATA_TYPE(DATA_TYPE, 4)
+    out0_dt                                      = ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out00, out01, out02, out03), VEC_DATA_TYPE(DATA_TYPE, 4)));
+    *(__global DATA_TYPE *)(dst_ptr + offset.s0) = out0_dt.s0;
+    *(__global DATA_TYPE *)(dst_ptr + offset.s1) = out0_dt.s1;
+    *(__global DATA_TYPE *)(dst_ptr + offset.s2) = out0_dt.s2;
+    *(__global DATA_TYPE *)(dst_ptr + offset.s3) = out0_dt.s3;
 #else  // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
     // Get output address
     int offset = dst_offset_first_element_in_bytes + x_out * sizeof(DATA_TYPE) + y_out * dst_stride_y + z_out * dst_stride_z;
-
-    *(__global DATA_TYPE *)(dst_ptr + 0 * dst_stride_y + offset) = (DATA_TYPE)out00;
-    *(__global DATA_TYPE *)(dst_ptr + 1 * dst_stride_y + offset) = (DATA_TYPE)out01;
-    *(__global DATA_TYPE *)(dst_ptr + 2 * dst_stride_y + offset) = (DATA_TYPE)out02;
-    *(__global DATA_TYPE *)(dst_ptr + 3 * dst_stride_y + offset) = (DATA_TYPE)out03;
+    VEC_DATA_TYPE(DATA_TYPE, 4)
+    out0_dt                                                      = ACTIVATION_FUNC(CONVERT((VEC_DATA_TYPE(float, 4))(out00, out01, out02, out03), VEC_DATA_TYPE(DATA_TYPE, 4)));
+    *(__global DATA_TYPE *)(dst_ptr + 0 * dst_stride_y + offset) = out0_dt.s0;
+    *(__global DATA_TYPE *)(dst_ptr + 1 * dst_stride_y + offset) = out0_dt.s1;
+    *(__global DATA_TYPE *)(dst_ptr + 2 * dst_stride_y + offset) = out0_dt.s2;
+    *(__global DATA_TYPE *)(dst_ptr + 3 * dst_stride_y + offset) = out0_dt.s3;
 #endif // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
 
 #else // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
@@ -1094,26 +1133,37 @@ __kernel void winograd_output_transform_4x4_5x5_nhwc(
     int4 mult_y = min((int4)dst_size - offset, (int4)1);                                 // If out of bound, we don't want to increase dst_stride_y, so we set the multiplier to 0. It will be 1 otherwise.
 
     // Store the output tile
-    *(__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 0 * (int)dst_stride_y + offset.s0) = (DATA_TYPE)out_col0.s0;
-    *(__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 1 * (int)dst_stride_y + offset.s0) = (DATA_TYPE)out_col1.s0;
-    *(__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 2 * (int)dst_stride_y + offset.s0) = (DATA_TYPE)out_col2.s0;
-    *(__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 3 * (int)dst_stride_y + offset.s0) = (DATA_TYPE)out_col3.s0;
-    *(__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 0 * (int)dst_stride_y + offset.s1) = (DATA_TYPE)out_col0.s1;
-    *(__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 1 * (int)dst_stride_y + offset.s1) = (DATA_TYPE)out_col1.s1;
-    *(__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 2 * (int)dst_stride_y + offset.s1) = (DATA_TYPE)out_col2.s1;
-    *(__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 3 * (int)dst_stride_y + offset.s1) = (DATA_TYPE)out_col3.s1;
-    *(__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 0 * (int)dst_stride_y + offset.s2) = (DATA_TYPE)out_col0.s2;
-    *(__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 1 * (int)dst_stride_y + offset.s2) = (DATA_TYPE)out_col1.s2;
-    *(__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 2 * (int)dst_stride_y + offset.s2) = (DATA_TYPE)out_col2.s2;
-    *(__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 3 * (int)dst_stride_y + offset.s2) = (DATA_TYPE)out_col3.s2;
-    *(__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 0 * (int)dst_stride_y + offset.s3) = (DATA_TYPE)out_col0.s3;
-    *(__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 1 * (int)dst_stride_y + offset.s3) = (DATA_TYPE)out_col1.s3;
-    *(__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 2 * (int)dst_stride_y + offset.s3) = (DATA_TYPE)out_col2.s3;
-    *(__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 3 * (int)dst_stride_y + offset.s3) = (DATA_TYPE)out_col3.s3;
+    VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+    out_col0_dt = ACTIVATION_FUNC(CONVERT(out_col0, VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)));
+    VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+    out_col1_dt = ACTIVATION_FUNC(CONVERT(out_col1, VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)));
+    VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+    out_col2_dt = ACTIVATION_FUNC(CONVERT(out_col2, VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)));
+    VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+    out_col3_dt = ACTIVATION_FUNC(CONVERT(out_col3, VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)));
+
+    *(__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 0 * (int)dst_stride_y + offset.s0) = out_col0_dt.s0;
+    *(__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 1 * (int)dst_stride_y + offset.s0) = out_col1_dt.s0;
+    *(__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 2 * (int)dst_stride_y + offset.s0) = out_col2_dt.s0;
+    *(__global DATA_TYPE *)(dst_ptr + mult_y.s0 * 3 * (int)dst_stride_y + offset.s0) = out_col3_dt.s0;
+    *(__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 0 * (int)dst_stride_y + offset.s1) = out_col0_dt.s1;
+    *(__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 1 * (int)dst_stride_y + offset.s1) = out_col1_dt.s1;
+    *(__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 2 * (int)dst_stride_y + offset.s1) = out_col2_dt.s1;
+    *(__global DATA_TYPE *)(dst_ptr + mult_y.s1 * 3 * (int)dst_stride_y + offset.s1) = out_col3_dt.s1;
+    *(__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 0 * (int)dst_stride_y + offset.s2) = out_col0_dt.s2;
+    *(__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 1 * (int)dst_stride_y + offset.s2) = out_col1_dt.s2;
+    *(__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 2 * (int)dst_stride_y + offset.s2) = out_col2_dt.s2;
+    *(__global DATA_TYPE *)(dst_ptr + mult_y.s2 * 3 * (int)dst_stride_y + offset.s2) = out_col3_dt.s2;
+    *(__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 0 * (int)dst_stride_y + offset.s3) = out_col0_dt.s3;
+    *(__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 1 * (int)dst_stride_y + offset.s3) = out_col1_dt.s3;
+    *(__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 2 * (int)dst_stride_y + offset.s3) = out_col2_dt.s3;
+    *(__global DATA_TYPE *)(dst_ptr + mult_y.s3 * 3 * (int)dst_stride_y + offset.s3) = out_col3_dt.s3;
 #endif // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL) || defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
 }
+#endif // defined(VEC_SIZE) && VEC_SIZE == 4
 
 #if defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL)
+#if defined(VEC_SIZE) && VEC_SIZE == 2
 /** 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
@@ -1181,7 +1231,9 @@ __kernel void winograd_output_transform_2x1_3x1_nchw(
 #endif // defined(HAS_BIAS)
                                           );
 }
+#endif // defined(VEC_SIZE) && VEC_SIZE == 2
 
+#if defined(VEC_SIZE) && VEC_SIZE == 4
 /** 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
@@ -1449,9 +1501,11 @@ __kernel void winograd_output_transform_4x1_5x1_nhwc(
 #endif // defined(HAS_BIAS)
                                            dst_size);
 }
+#endif // defined(VEC_SIZE) && VEC_SIZE == 4
 #endif // defined(WINOGRAD_OUTPUT_TRANSFORM_HORIZONTAL)
 
 #if defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
+#if defined(VEC_SIZE) && VEC_SIZE == 2
 /** 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
@@ -1519,7 +1573,9 @@ __kernel void winograd_output_transform_1x2_1x3_nchw(
 #endif // defined(HAS_BIAS)
                                           );
 }
+#endif // defined(VEC_SIZE) && VEC_SIZE == 2
 
+#if defined(VEC_SIZE) && VEC_SIZE == 4
 /** 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
@@ -1787,5 +1843,6 @@ __kernel void winograd_output_transform_1x4_1x5_nhwc(
 #endif // defined(HAS_BIAS)
                                            dst_size);
 }
+#endif // defined(VEC_SIZE) && VEC_SIZE == 4
 #endif // defined(WINOGRAD_OUTPUT_TRANSFORM_VERTICAL)
 #endif // defined(NUM_TILES_X) && defined(OUTPUT_TILE_W) && defined(OUTPUT_TILE_H)
diff --git a/src/core/CL/kernels/CLWinogradOutputTransformKernel.cpp b/src/core/CL/kernels/CLWinogradOutputTransformKernel.cpp
index 7f1afe0058..84b5ea23f1 100644
--- a/src/core/CL/kernels/CLWinogradOutputTransformKernel.cpp
+++ b/src/core/CL/kernels/CLWinogradOutputTransformKernel.cpp
@@ -46,8 +46,18 @@ using namespace arm_compute::misc::shape_calculator;
 
 namespace
 {
-Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info)
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
 {
+    if(act_info.enabled())
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
+        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::U8, DataType::QASYMM8, DataType::F16, DataType::F32);
+        ARM_COMPUTE_RETURN_ERROR_ON_MSG((input->data_type() == DataType::QASYMM8) && (act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU)
+                                        && (act_info.activation() != ActivationLayerInfo::ActivationFunction::BOUNDED_RELU)
+                                        && (act_info.activation() != ActivationLayerInfo::ActivationFunction::RELU)
+                                        && (act_info.activation() != ActivationLayerInfo::ActivationFunction::LOGISTIC),
+                                        "For QASYMM8 only logistic, relu, lower bounded relu and lower-upper bounded relu are supported");
+    }
     ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::F32, DataType::F16);
     ARM_COMPUTE_RETURN_ERROR_ON_F16_UNSUPPORTED(input);
 
@@ -133,14 +143,14 @@ CLWinogradOutputTransformKernel::CLWinogradOutputTransformKernel()
 {
 }
 
-void CLWinogradOutputTransformKernel::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const WinogradInfo &winograd_info)
+void CLWinogradOutputTransformKernel::configure(const ICLTensor *input, const ICLTensor *bias, ICLTensor *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
 {
     ARM_COMPUTE_ERROR_ON_NULLPTR(input, output);
 
     // Output tensor auto initialization if not yet initialized
     auto_init_if_empty(*output->info(), input->info()->clone()->set_tensor_shape(compute_winograd_output_transform_shape(*input->info(), winograd_info)));
 
-    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias != nullptr ? bias->info() : nullptr), output->info(), winograd_info));
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), (bias != nullptr ? bias->info() : nullptr), output->info(), winograd_info, act_info));
 
     _input  = input;
     _bias   = bias;
@@ -161,6 +171,21 @@ void CLWinogradOutputTransformKernel::configure(const ICLTensor *input, const IC
 
     // Set build options
     CLBuildOptions build_opts;
+    build_opts.add_option_if(act_info.enabled(), "-DFUSED_ACTIVATION=" + lower_string(string_from_activation_func(act_info.activation())));
+    build_opts.add_option_if(act_info.enabled(), "-DA_VAL=" + float_to_string_with_full_precision(act_info.a()));
+    build_opts.add_option_if(act_info.enabled(), "-DB_VAL=" + float_to_string_with_full_precision(act_info.b()));
+
+    if((output_tile_size.x() == 2) || (output_tile_size.x() == 1 && output_tile_size.y() == 2))
+    {
+        build_opts.add_option("-DVEC_SIZE=2");
+    }
+    else if((output_tile_size.x() == 4) || (output_tile_size.x() == 1 && output_tile_size.y() == 4))
+    {
+        build_opts.add_option("-DVEC_SIZE=4");
+    }
+
+    build_opts.add_option_if(act_info.enabled(), "-DSELECT_DATA_TYPE=" + get_cl_select_type_from_data_type(input->info()->data_type()));
+
     build_opts.add_option_if(_bias != nullptr, std::string("-DHAS_BIAS"));
     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));
@@ -195,9 +220,9 @@ void CLWinogradOutputTransformKernel::configure(const ICLTensor *input, const IC
     _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)
+Status CLWinogradOutputTransformKernel::validate(const ITensorInfo *input, const ITensorInfo *bias, const ITensorInfo *output, const WinogradInfo &winograd_info, const ActivationLayerInfo &act_info)
 {
-    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, (bias != nullptr ? bias->clone().get() : nullptr), output, winograd_info));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, (bias != nullptr ? bias->clone().get() : nullptr), output, winograd_info, act_info));
     ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), (bias != nullptr ? bias->clone().get() : nullptr), output->clone().get(), winograd_info.output_tile_size).first);
 
     return Status{};
diff --git a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp
index 1abcb67132..069196e8c1 100644
--- a/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLWinogradConvolutionLayer.cpp
@@ -84,8 +84,8 @@ bool check_support_fast_math(const Size2D &output_tile, const Size2D &kernel_siz
 } // namespace
 
 CLWinogradConvolutionLayer::CLWinogradConvolutionLayer(std::shared_ptr<IMemoryManager> memory_manager)
-    : _memory_group(memory_manager), _batched_mm(memory_manager), _input_transform(), _filter_transform(), _output_transform(), _activationlayer_function(), _input0(), _input1(), _batched_mm_output(),
-      _original_weights(nullptr), _is_prepared(false), _is_activationlayer_enabled(false)
+    : _memory_group(memory_manager), _batched_mm(memory_manager), _input_transform(), _filter_transform(), _output_transform(), _input0(), _input1(), _batched_mm_output(), _original_weights(nullptr),
+      _is_prepared(false)
 {
 }
 
@@ -133,14 +133,7 @@ void CLWinogradConvolutionLayer::configure(ICLTensor *input, const ICLTensor *we
                                                                                                  (input->info()->data_type() == DataType::F16)));
 
     // Configure output transform
-    _output_transform.configure(&_batched_mm_output, biases, output, winograd_info);
-
-    // Configure activation layer
-    _is_activationlayer_enabled = act_info.enabled();
-    if(_is_activationlayer_enabled)
-    {
-        _activationlayer_function.configure(output, nullptr, act_info);
-    }
+    _output_transform.configure(&_batched_mm_output, biases, output, winograd_info, act_info);
 
     // Allocate temporary tensors
     _input0.allocator()->allocate();
@@ -216,11 +209,6 @@ void CLWinogradConvolutionLayer::run()
     // Run output transform
     CLScheduler::get().enqueue(_output_transform);
 
-    if(_is_activationlayer_enabled)
-    {
-        _activationlayer_function.run();
-    }
-
     _memory_group.release();
 }
 
diff --git a/tests/validation/CL/Winograd.cpp b/tests/validation/CL/Winograd.cpp
index f7f06b7f79..efa049f5ab 100644
--- a/tests/validation/CL/Winograd.cpp
+++ b/tests/validation/CL/Winograd.cpp
@@ -139,6 +139,17 @@ const auto SmallWinogradOutputTransformDatasetNHWC = datasets::SmallWinogradOutp
 const auto LargeWinogradOutputTransformDatasetNCHW = datasets::LargeWinogradOutputTransformDatasetNCHW();
 
 const auto LargeWinogradOutputTransformDatasetNHWC = datasets::LargeWinogradOutputTransformDatasetNHWC();
+
+//Activation Functions
+const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
+{
+    ActivationLayerInfo(),
+    ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
+    ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU),
+    ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU),
+    ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU),
+    ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::SOFT_RELU)
+});
 } // namespace
 
 using namespace arm_compute::misc::shape_calculator;
@@ -562,16 +573,18 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::da
 }
 TEST_SUITE(FP16)
 FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradOutputTransformFixtureFP16, framework::DatasetMode::ALL,
-                       combine(SmallWinogradOutputTransformDatasetNCHW,
-                               framework::dataset::make("DataType", { DataType::F16 })))
+                       combine(combine(SmallWinogradOutputTransformDatasetNCHW,
+                               framework::dataset::make("DataType", { DataType::F16 })),
+                               framework::dataset::make("ActivationInfo",{ ActivationLayerInfo() }) ))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_f16);
 }
 
 FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixtureFP16, framework::DatasetMode::NIGHTLY,
-                       combine(LargeWinogradOutputTransformDatasetNCHW,
-                               framework::dataset::make("DataType", { DataType::F16 })))
+                       combine(combine(LargeWinogradOutputTransformDatasetNCHW,
+                               framework::dataset::make("DataType", { DataType::F16 })),
+                               framework::dataset::make("ActivationInfo",{ ActivationLayerInfo() }) ))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_f16);
@@ -579,16 +592,18 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixtureFP16, framework
 TEST_SUITE_END() // FP16
 TEST_SUITE(FP32)
 FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradOutputTransformFixtureFP32, framework::DatasetMode::ALL,
-                       combine(SmallWinogradOutputTransformDatasetNCHW,
-                               framework::dataset::make("DataType", { DataType::F32 })))
+                       combine(combine(SmallWinogradOutputTransformDatasetNCHW,
+                               framework::dataset::make("DataType", { DataType::F32 })),
+                               framework::dataset::make("ActivationInfo",{ ActivationLayerInfo() }) ))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_f32);
 }
 
 FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixtureFP32, framework::DatasetMode::NIGHTLY,
-                       combine(LargeWinogradOutputTransformDatasetNCHW,
-                               framework::dataset::make("DataType", { DataType::F32 })))
+                       combine(combine(LargeWinogradOutputTransformDatasetNCHW,
+                               framework::dataset::make("DataType", { DataType::F32 })),
+                               framework::dataset::make("ActivationInfo",{ ActivationLayerInfo() }) ))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_f32);
@@ -618,16 +633,18 @@ DATA_TEST_CASE(Configuration, framework::DatasetMode::ALL, combine(framework::da
 
 TEST_SUITE(FP16)
 FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradOutputTransformFixtureFP16, framework::DatasetMode::ALL,
-                       combine(SmallWinogradOutputTransformDatasetNHWC,
-                               framework::dataset::make("DataType", { DataType::F16 })))
+                       combine(combine(SmallWinogradOutputTransformDatasetNHWC,
+                               framework::dataset::make("DataType", { DataType::F16 })),
+                               framework::dataset::make("ActivationInfo",{ ActivationLayerInfo() }) ))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_f16);
 }
 
 FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixtureFP16, framework::DatasetMode::NIGHTLY,
-                       combine(LargeWinogradOutputTransformDatasetNHWC,
-                               framework::dataset::make("DataType", { DataType::F16 })))
+                       combine(combine(LargeWinogradOutputTransformDatasetNHWC,
+                               framework::dataset::make("DataType", { DataType::F16 })),
+                               framework::dataset::make("ActivationInfo",{ ActivationLayerInfo() }) ))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_f16);
@@ -635,16 +652,18 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixtureFP16, framework
 TEST_SUITE_END() // FP16
 TEST_SUITE(FP32)
 FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradOutputTransformFixtureFP32, framework::DatasetMode::ALL,
-                       combine(SmallWinogradOutputTransformDatasetNHWC,
-                               framework::dataset::make("DataType", { DataType::F32 })))
+                       combine(combine(SmallWinogradOutputTransformDatasetNHWC,
+                               framework::dataset::make("DataType", { DataType::F32 })),
+                               framework::dataset::make("ActivationInfo",{ ActivationLayerInfo() }) ))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_f32);
 }
 
 FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradOutputTransformFixtureFP32, framework::DatasetMode::NIGHTLY,
-                       combine(LargeWinogradOutputTransformDatasetNHWC,
-                               framework::dataset::make("DataType", { DataType::F32 })))
+                       combine(combine(LargeWinogradOutputTransformDatasetNHWC,
+                               framework::dataset::make("DataType", { DataType::F32 })),
+                               framework::dataset::make("ActivationInfo",{ ActivationLayerInfo() }) ))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_f32);
@@ -702,7 +721,7 @@ 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() })),
+                                               ActivationFunctionsDataset),
                                                framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 {
     // Validate output
@@ -712,7 +731,7 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, fram
 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() })),
+                                               ActivationFunctionsDataset),
                                                framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 {
     // Validate output
@@ -724,7 +743,7 @@ 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() })),
+                                       ActivationFunctionsDataset),
                                        framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 {
     // Validate output
@@ -734,7 +753,7 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, fram
 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() })),
+                                       ActivationFunctionsDataset),
                                        framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 {
     // Validate output
@@ -746,7 +765,7 @@ 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() })),
+                                       ActivationFunctionsDataset),
                                        framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 {
     // Validate output
@@ -756,7 +775,7 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, fram
 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() })),
+                                       ActivationFunctionsDataset),
                                        framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 {
     // Validate output
@@ -768,7 +787,7 @@ 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() })),
+                                               ActivationFunctionsDataset ),
                                                framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 
 {
@@ -779,7 +798,7 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, fram
 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() })),
+                                               ActivationFunctionsDataset ),
                                                framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 
 {
@@ -792,7 +811,7 @@ TEST_SUITE(Conv5x1)
 FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT,
                        combine(combine(combine(datasets::SmallWinogradConvolutionLayer5x1Dataset(),
                                                framework::dataset::make("DataType", { DataType::F32 })),
-                                               framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+                                               ActivationFunctionsDataset),
                                                framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 
 {
@@ -803,7 +822,7 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, fram
 FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY,
                        combine(combine(combine(datasets::LargeWinogradConvolutionLayer5x1Dataset(),
                                                framework::dataset::make("DataType", { DataType::F32 })),
-                                               framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+                                               ActivationFunctionsDataset),
                                                framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 
 {
@@ -816,7 +835,7 @@ TEST_SUITE(Conv1x5)
 FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::PRECOMMIT,
                        combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x5Dataset(),
                                                framework::dataset::make("DataType", { DataType::F32 })),
-                                               framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+                                               ActivationFunctionsDataset),
                                                framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 
 {
@@ -827,7 +846,7 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture, fram
 FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture, framework::DatasetMode::NIGHTLY,
                        combine(combine(combine(datasets::LargeWinogradConvolutionLayer1x5Dataset(),
                                                framework::dataset::make("DataType", { DataType::F32 })),
-                                               framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+                                               ActivationFunctionsDataset),
                                                framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 
 {
@@ -845,7 +864,7 @@ TEST_SUITE(Conv3x3)
 FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT,
                        combine(combine(combine(datasets::SmallWinogradConvolutionLayer3x3Dataset(),
                                                framework::dataset::make("DataType", { DataType::F16 })),
-                                               framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+                                               ActivationFunctionsDataset),
                                                framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 {
     // Validate output
@@ -855,7 +874,7 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, fr
 FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY,
                        combine(combine(combine(datasets::LargeWinogradConvolutionLayer3x3Dataset(),
                                                framework::dataset::make("DataType", { DataType::F16 })),
-                                               framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+                                               ActivationFunctionsDataset),
                                                framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 {
     // Validate output
@@ -867,7 +886,7 @@ TEST_SUITE(Conv3x1)
 FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT,
                        combine(combine(combine(datasets::SmallWinogradConvolutionLayer3x1Dataset(),
                                        framework::dataset::make("DataType", { DataType::F16 })),
-                                       framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+                                       ActivationFunctionsDataset),
                                        framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 {
     // Validate output
@@ -877,7 +896,7 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, fr
 FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY,
                        combine(combine(combine(datasets::LargeWinogradConvolutionLayer3x1Dataset(),
                                        framework::dataset::make("DataType", { DataType::F16 })),
-                                       framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+                                       ActivationFunctionsDataset),
                                        framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 {
     // Validate output
@@ -889,7 +908,7 @@ TEST_SUITE(Conv1x3)
 FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT,
                        combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x3Dataset(),
                                        framework::dataset::make("DataType", { DataType::F16 })),
-                                       framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+                                       ActivationFunctionsDataset),
                                        framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 {
     // Validate output
@@ -899,7 +918,7 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, fr
 FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY,
                        combine(combine(combine(datasets::LargeWinogradConvolutionLayer1x3Dataset(),
                                        framework::dataset::make("DataType", { DataType::F16 })),
-                                       framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+                                       ActivationFunctionsDataset),
                                        framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 {
     // Validate output
@@ -911,7 +930,7 @@ TEST_SUITE(Conv5x5)
 FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT,
                        combine(combine(combine(datasets::SmallWinogradConvolutionLayer5x5Dataset(),
                                                framework::dataset::make("DataType", { DataType::F16 })),
-                                               framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+                                               ActivationFunctionsDataset),
                                                framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 
 {
@@ -922,7 +941,7 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, fr
 FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY,
                        combine(combine(combine(datasets::LargeWinogradConvolutionLayer5x5Dataset(),
                                                framework::dataset::make("DataType", { DataType::F16 })),
-                                               framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+                                               ActivationFunctionsDataset),
                                                framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 
 {
@@ -935,7 +954,7 @@ TEST_SUITE(Conv5x1)
 FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT,
                        combine(combine(combine(datasets::SmallWinogradConvolutionLayer5x1Dataset(),
                                                framework::dataset::make("DataType", { DataType::F16 })),
-                                               framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+                                               ActivationFunctionsDataset),
                                                framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 
 {
@@ -946,7 +965,7 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, fr
 FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY,
                        combine(combine(combine(datasets::LargeWinogradConvolutionLayer5x1Dataset(),
                                                framework::dataset::make("DataType", { DataType::F16 })),
-                                               framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+                                               ActivationFunctionsDataset),
                                                framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 
 {
@@ -959,7 +978,7 @@ TEST_SUITE(Conv1x5)
 FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT,
                        combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x5Dataset(),
                                                framework::dataset::make("DataType", { DataType::F16 })),
-                                               framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+                                               ActivationFunctionsDataset),
                                                framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 
 {
@@ -970,7 +989,7 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, fr
 FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY,
                        combine(combine(combine(datasets::LargeWinogradConvolutionLayer1x5Dataset(),
                                                framework::dataset::make("DataType", { DataType::F16 })),
-                                               framework::dataset::make("ActivationLayerInfo", { ActivationLayerInfo() })),
+                                               ActivationFunctionsDataset),
                                                framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 
 {
diff --git a/tests/validation/fixtures/WinogradConvolutionLayerFixture.h b/tests/validation/fixtures/WinogradConvolutionLayerFixture.h
index 9c9e634205..8f34654c3a 100644
--- a/tests/validation/fixtures/WinogradConvolutionLayerFixture.h
+++ b/tests/validation/fixtures/WinogradConvolutionLayerFixture.h
@@ -494,10 +494,10 @@ class WinogradOutputTransformValidationFixture : public framework::Fixture
 {
 public:
     template <typename...>
-    void setup(TensorShape input_shape, WinogradInfo winograd_info, DataType data_type)
+    void setup(TensorShape input_shape, WinogradInfo winograd_info, DataType data_type, ActivationLayerInfo act_info = ActivationLayerInfo())
     {
-        _target    = compute_target(input_shape, winograd_info, data_type);
-        _reference = compute_reference(input_shape, winograd_info, data_type);
+        _target    = compute_target(input_shape, winograd_info, data_type, act_info);
+        _reference = compute_reference(input_shape, winograd_info, data_type, act_info);
     }
 
 protected:
@@ -522,7 +522,7 @@ protected:
         }
     }
 
-    TensorType compute_target(const TensorShape &input_shape, const WinogradInfo &winograd_info, DataType data_type)
+    TensorType compute_target(const TensorShape &input_shape, const WinogradInfo &winograd_info, DataType data_type, ActivationLayerInfo act_info)
     {
         TensorShape output_shape = compute_winograd_output_transform_shape(TensorInfo(input_shape, 1, data_type), winograd_info);
 
@@ -533,7 +533,7 @@ protected:
 
         // Create and configure function
         FunctionType output_transform;
-        output_transform.configure(&src, &bias, &dst, winograd_info);
+        output_transform.configure(&src, &bias, &dst, winograd_info, act_info);
 
         ARM_COMPUTE_EXPECT(src.info()->is_resizable(), framework::LogLevel::ERRORS);
         ARM_COMPUTE_EXPECT(bias.info()->is_resizable(), framework::LogLevel::ERRORS);
@@ -557,7 +557,7 @@ protected:
         return dst;
     }
 
-    SimpleTensor<T> compute_reference(const TensorShape &input_shape, WinogradInfo winograd_info, DataType data_type)
+    SimpleTensor<T> compute_reference(const TensorShape &input_shape, WinogradInfo winograd_info, DataType data_type, ActivationLayerInfo act_info)
     {
         winograd_info.output_data_layout = DataLayout::NCHW;
         TensorShape output_shape         = compute_winograd_output_transform_shape(TensorInfo(input_shape, 1, data_type), winograd_info);
@@ -570,7 +570,9 @@ protected:
         fill(src, 0, -1.f, 1.f);
         fill(bias, 1, -1.f, 1.f);
 
-        return reference::winograd_output_transform<T>(src, bias, output_shape, winograd_info);
+        const SimpleTensor<T> winograd_output = reference::winograd_output_transform<T>(src, bias, output_shape, winograd_info);
+
+        return (act_info.enabled()) ? reference::activation_layer<T>(winograd_output, act_info) : winograd_output;
     }
 
     TensorType      _target{};