COMPMID-1611 CLDirectConvolution NHWC QASYMM not implemented

Change-Id: I358c729cb81b83d35f1bc7f70ea593d5bff5f1ed
Signed-off-by: Giorgio Arena <giorgio.arena@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/2738
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>

5 files changed, 512 insertions, 89 deletions

diff --git a/src/core/CL/cl_kernels/direct_convolution_quantized.cl b/src/core/CL/cl_kernels/direct_convolution_quantized.cl
index 0a8c5faecf..3324e9caeb 100644
--- a/src/core/CL/cl_kernels/direct_convolution_quantized.cl
+++ b/src/core/CL/cl_kernels/direct_convolution_quantized.cl
@@ -31,6 +31,435 @@
 #define CONVERT_SAT_STR(x, type) (convert_##type##8_sat((x)))
 #define CONVERT_SAT(x, type) CONVERT_SAT_STR(x, type)
 
+#if defined(DATA_LAYOUT_NHWC)
+
+#if KERNEL_SIZE == 5
+
+#if STRIDE_X == 1
+#define CONVOLUTION1x5(acc, src_ptr, weights_ptr) CONVOLUTION1x5_STRIDE1(acc, src_ptr, weights_ptr)
+#elif STRIDE_X == 2
+#define CONVOLUTION1x5(acc, src_ptr, weights_ptr) CONVOLUTION1x5_STRIDE2(acc, src_ptr, weights_ptr)
+#else /* STRIDE_X not equals 1 or 2 */
+#error "STRIDE_X larger than 2 is not supported"
+#endif /* STRIDE_X */
+
+#define CONVOLUTION1x5_STRIDE1(acc, src_ptr, weights_ptr)                                                            \
+    ({                                                                                                               \
+        int4 weights_values0 = 0;                                                                                    \
+        int  weights_value1  = 0;                                                                                    \
+        weights_values0.s0   = convert_int(*(weights_ptr + 0 * weights_stride_y));                                   \
+        weights_values0.s1   = convert_int(*(weights_ptr + 1 * weights_stride_y));                                   \
+        weights_values0.s2   = convert_int(*(weights_ptr + 2 * weights_stride_y));                                   \
+        weights_values0.s3   = convert_int(*(weights_ptr + 3 * weights_stride_y));                                   \
+        weights_value1       = convert_int(*(weights_ptr + 4 * weights_stride_y));                                   \
+        \
+        int8 src0 = 0;                                                                                               \
+        int4 src1 = 0;                                                                                               \
+        src0.s0   = convert_int(*(src_ptr + 0 * weights_stride_y));                                                  \
+        src0.s1   = convert_int(*(src_ptr + 1 * weights_stride_y));                                                  \
+        src0.s2   = convert_int(*(src_ptr + 2 * weights_stride_y));                                                  \
+        src0.s3   = convert_int(*(src_ptr + 3 * weights_stride_y));                                                  \
+        src0.s4   = convert_int(*(src_ptr + 4 * weights_stride_y));                                                  \
+        src0.s5   = convert_int(*(src_ptr + 5 * weights_stride_y));                                                  \
+        src0.s6   = convert_int(*(src_ptr + 6 * weights_stride_y));                                                  \
+        src0.s7   = convert_int(*(src_ptr + 7 * weights_stride_y));                                                  \
+        src1.s0   = convert_int(*(src_ptr + 8 * weights_stride_y));                                                  \
+        src1.s1   = convert_int(*(src_ptr + 9 * weights_stride_y));                                                  \
+        src1.s2   = convert_int(*(src_ptr + 10 * weights_stride_y));                                                 \
+        src1.s3   = convert_int(*(src_ptr + 11 * weights_stride_y));                                                 \
+        \
+        acc += (src0 + input_offset) * ((int8)weights_values0.s0 + weight_offset);                                   \
+        acc += ((int8)(src0.s1234, src0.s567, src1.s0) + input_offset) * ((int8)weights_values0.s1 + weight_offset); \
+        acc += ((int8)(src0.s234, src0.s567, src1.s01) + input_offset) * ((int8)weights_values0.s2 + weight_offset); \
+        acc += ((int8)(src0.s345, src0.s67, src1.s012) + input_offset) * ((int8)weights_values0.s3 + weight_offset); \
+        acc += ((int8)(src0.s45, src0.s67, src1.s0123) + input_offset) * ((int8)weights_value1 + weight_offset);     \
+    })
+
+#define CONVOLUTION1x5_STRIDE2(acc, src_ptr, weights_ptr)                                                            \
+    ({                                                                                                               \
+        int4 weights_values0 = 0;                                                                                    \
+        int  weights_value1  = 0;                                                                                    \
+        weights_values0.s0   = convert_int(*(weights_ptr + 0 * weights_stride_y));                                   \
+        weights_values0.s1   = convert_int(*(weights_ptr + 1 * weights_stride_y));                                   \
+        weights_values0.s2   = convert_int(*(weights_ptr + 2 * weights_stride_y));                                   \
+        weights_values0.s3   = convert_int(*(weights_ptr + 3 * weights_stride_y));                                   \
+        weights_value1       = convert_int(*(weights_ptr + 4 * weights_stride_y));                                   \
+        \
+        int16 src0 = 0;                                                                                              \
+        int4  src1 = 0;                                                                                              \
+        src0.s0    = convert_int(*(src_ptr + 0 * weights_stride_y));                                                 \
+        src0.s1    = convert_int(*(src_ptr + 1 * weights_stride_y));                                                 \
+        src0.s2    = convert_int(*(src_ptr + 2 * weights_stride_y));                                                 \
+        src0.s3    = convert_int(*(src_ptr + 3 * weights_stride_y));                                                 \
+        src0.s4    = convert_int(*(src_ptr + 4 * weights_stride_y));                                                 \
+        src0.s5    = convert_int(*(src_ptr + 5 * weights_stride_y));                                                 \
+        src0.s6    = convert_int(*(src_ptr + 6 * weights_stride_y));                                                 \
+        src0.s7    = convert_int(*(src_ptr + 7 * weights_stride_y));                                                 \
+        src0.s8    = convert_int(*(src_ptr + 8 * weights_stride_y));                                                 \
+        src0.s9    = convert_int(*(src_ptr + 9 * weights_stride_y));                                                 \
+        src0.sa    = convert_int(*(src_ptr + 10 * weights_stride_y));                                                \
+        src0.sb    = convert_int(*(src_ptr + 11 * weights_stride_y));                                                \
+        src0.sc    = convert_int(*(src_ptr + 12 * weights_stride_y));                                                \
+        src0.sd    = convert_int(*(src_ptr + 13 * weights_stride_y));                                                \
+        src0.se    = convert_int(*(src_ptr + 14 * weights_stride_y));                                                \
+        src0.sf    = convert_int(*(src_ptr + 15 * weights_stride_y));                                                \
+        src1.s0    = convert_int(*(src_ptr + 16 * weights_stride_y));                                                \
+        src1.s1    = convert_int(*(src_ptr + 17 * weights_stride_y));                                                \
+        src1.s2    = convert_int(*(src_ptr + 18 * weights_stride_y));                                                \
+        src1.s3    = convert_int(*(src_ptr + 19 * weights_stride_y));                                                \
+        \
+        acc += (src0.even + input_offset) * ((int8)weights_values0.s0 + weight_offset);                              \
+        acc += ((int8)(src0.s1357, src0.s9BDF) + input_offset) * ((int8)weights_values0.s1 + weight_offset);         \
+        acc += ((int8)(src0.s2468, src0.sACE, src1.s0) + input_offset) * ((int8)weights_values0.s2 + weight_offset); \
+        acc += ((int8)(src0.s3579, src0.sBDF, src1.s1) + input_offset) * ((int8)weights_values0.s3 + weight_offset); \
+        acc += ((int8)(src0.s468a, src0.sCE, src1.s02) + input_offset) * ((int8)weights_value1 + weight_offset);     \
+    })
+
+#elif KERNEL_SIZE == 3
+
+#if STRIDE_X == 1
+#define CONVOLUTION1x3(acc, src_ptr, weights_ptr) CONVOLUTION1x3_STRIDE1(acc, src_ptr, weights_ptr)
+#elif STRIDE_X == 2
+#define CONVOLUTION1x3(acc, src_ptr, weights_ptr) CONVOLUTION1x3_STRIDE2(acc, src_ptr, weights_ptr)
+#else /* STRIDE_X not equals 1 or 2 */
+#error "STRIDE_X larger than 2 is not supported"
+#endif /* STRIDE_X */
+
+#define CONVOLUTION1x3_STRIDE1(acc, src_ptr, weights_ptr)                                                            \
+    ({                                                                                                               \
+        int3 weights_values0 = 0;                                                                                    \
+        weights_values0.s0   = convert_int(*(weights_ptr + 0 * weights_stride_y));                                   \
+        weights_values0.s1   = convert_int(*(weights_ptr + 1 * weights_stride_y));                                   \
+        weights_values0.s2   = convert_int(*(weights_ptr + 2 * weights_stride_y));                                   \
+        \
+        int8 src0 = 0;                                                                                               \
+        int2 src1 = 0;                                                                                               \
+        src0.s0   = convert_int(*(src_ptr + 0 * weights_stride_y));                                                  \
+        src0.s1   = convert_int(*(src_ptr + 1 * weights_stride_y));                                                  \
+        src0.s2   = convert_int(*(src_ptr + 2 * weights_stride_y));                                                  \
+        src0.s3   = convert_int(*(src_ptr + 3 * weights_stride_y));                                                  \
+        src0.s4   = convert_int(*(src_ptr + 4 * weights_stride_y));                                                  \
+        src0.s5   = convert_int(*(src_ptr + 5 * weights_stride_y));                                                  \
+        src0.s6   = convert_int(*(src_ptr + 6 * weights_stride_y));                                                  \
+        src0.s7   = convert_int(*(src_ptr + 7 * weights_stride_y));                                                  \
+        src1.s0   = convert_int(*(src_ptr + 8 * weights_stride_y));                                                  \
+        src1.s1   = convert_int(*(src_ptr + 9 * weights_stride_y));                                                  \
+        \
+        acc += (src0 + input_offset) * ((int8)weights_values0.s0 + weight_offset);                                   \
+        acc += ((int8)(src0.s1234, src0.s567, src1.s0) + input_offset) * ((int8)weights_values0.s1 + weight_offset); \
+        acc += ((int8)(src0.s234, src0.s567, src1.s01) + input_offset) * ((int8)weights_values0.s2 + weight_offset); \
+    })
+
+#define CONVOLUTION1x3_STRIDE2(acc, src_ptr, weights_ptr)                                                         \
+    ({                                                                                                            \
+        int3 weights_values0 = 0;                                                                                 \
+        weights_values0.s0   = convert_int(*(weights_ptr + 0 * weights_stride_y));                                \
+        weights_values0.s1   = convert_int(*(weights_ptr + 1 * weights_stride_y));                                \
+        weights_values0.s2   = convert_int(*(weights_ptr + 2 * weights_stride_y));                                \
+        \
+        int16 src0 = 0;                                                                                           \
+        int   src1 = 0;                                                                                           \
+        src0.s0    = convert_int(*(src_ptr + 0 * src_stride_y));                                                  \
+        src0.s1    = convert_int(*(src_ptr + 1 * src_stride_y));                                                  \
+        src0.s2    = convert_int(*(src_ptr + 2 * src_stride_y));                                                  \
+        src0.s3    = convert_int(*(src_ptr + 3 * src_stride_y));                                                  \
+        src0.s4    = convert_int(*(src_ptr + 4 * src_stride_y));                                                  \
+        src0.s5    = convert_int(*(src_ptr + 5 * src_stride_y));                                                  \
+        src0.s6    = convert_int(*(src_ptr + 6 * src_stride_y));                                                  \
+        src0.s7    = convert_int(*(src_ptr + 7 * src_stride_y));                                                  \
+        src0.s8    = convert_int(*(src_ptr + 8 * src_stride_y));                                                  \
+        src0.s9    = convert_int(*(src_ptr + 9 * src_stride_y));                                                  \
+        src0.sa    = convert_int(*(src_ptr + 10 * src_stride_y));                                                 \
+        src0.sb    = convert_int(*(src_ptr + 11 * src_stride_y));                                                 \
+        src0.sc    = convert_int(*(src_ptr + 12 * src_stride_y));                                                 \
+        src0.sd    = convert_int(*(src_ptr + 13 * src_stride_y));                                                 \
+        src0.se    = convert_int(*(src_ptr + 14 * src_stride_y));                                                 \
+        src0.sf    = convert_int(*(src_ptr + 15 * src_stride_y));                                                 \
+        src1       = convert_int(*(src_ptr + 16 * src_stride_y));                                                 \
+        acc += (src0.even + input_offset) * ((int8)weights_values0.s0 + weight_offset);                           \
+        acc += ((int8)(src0.s1357, src0.s9BDF) + input_offset) * ((int8)weights_values0.s1 + weight_offset);      \
+        acc += ((int8)(src0.s2468, src0.sACE, src1) + input_offset) * ((int8)weights_values0.s2 + weight_offset); \
+    })
+
+#elif KERNEL_SIZE == 1
+
+#if STRIDE_X == 3
+#define INPUT_VALUE extract_input_stride3
+#elif STRIDE_X == 2
+#define INPUT_VALUE extract_input_stride2
+#elif STRIDE_X == 1
+#define INPUT_VALUE extract_input_stride1
+
+#else /* STRIDE_X not equals 1, 2 or 3 */
+#error "Only support strides 1, 2 and 3"
+#endif /* STRIDE_X */
+
+#endif // KERNEL_SIZE == 1
+
+/** Extracts a 1D horizontal vector from the input tensor with stride as 1.
+ *
+ * @param[in] input_value Pointer to the first value.
+ *
+ * @return extracted input values.
+ */
+inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride1(__global const DATA_TYPE *input_value, const uchar stride_y)
+{
+    VEC_DATA_TYPE(DATA_TYPE, 8)
+    vals;
+    vals.s0 = *(input_value + 0 * stride_y);
+    vals.s1 = *(input_value + 1 * stride_y);
+    vals.s2 = *(input_value + 2 * stride_y);
+    vals.s3 = *(input_value + 3 * stride_y);
+    vals.s4 = *(input_value + 4 * stride_y);
+    vals.s5 = *(input_value + 5 * stride_y);
+    vals.s6 = *(input_value + 6 * stride_y);
+    vals.s7 = *(input_value + 7 * stride_y);
+
+    return vals;
+}
+
+/** Extracts a 1D horizontal vector from the input tensor with stride as 2.
+ *
+ * @param[in] input_value Pointer to the first value.
+ *
+ * @return extracted input values.
+ */
+inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride2(__global const DATA_TYPE *input_value, const uchar stride_y)
+{
+    VEC_DATA_TYPE(DATA_TYPE, 8)
+    vals;
+    vals.s0 = *(input_value + 0 * stride_y);
+    vals.s1 = *(input_value + 2 * stride_y);
+    vals.s2 = *(input_value + 4 * stride_y);
+    vals.s3 = *(input_value + 6 * stride_y);
+    vals.s4 = *(input_value + 8 * stride_y);
+    vals.s5 = *(input_value + 10 * stride_y);
+    vals.s6 = *(input_value + 12 * stride_y);
+    vals.s7 = *(input_value + 14 * stride_y);
+
+    return vals;
+}
+
+/** Extracts a 1D horizontal vector from the input tensor with stride as 3 and 8-bit data size.
+ *
+ * @param[in] input_value Pointer to the first value.
+ *
+ * @return extracted input values.
+ */
+inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride3(__global const DATA_TYPE *input_value, const uchar stride_y)
+{
+    VEC_DATA_TYPE(DATA_TYPE, 8)
+    vals;
+    vals.s0 = *(input_value + 0 * stride_y);
+    vals.s1 = *(input_value + 3 * stride_y);
+    vals.s2 = *(input_value + 6 * stride_y);
+    vals.s3 = *(input_value + 9 * stride_y);
+    vals.s4 = *(input_value + 12 * stride_y);
+    vals.s5 = *(input_value + 15 * stride_y);
+    vals.s6 = *(input_value + 18 * stride_y);
+    vals.s7 = *(input_value + 21 * stride_y);
+
+    return vals;
+}
+
+/** This kernel performs a direct convolution to convolve the low three dimensions.
+ *
+ * @note The convolution stride x must be passed at compile time using -DSTRIDE_X e.g. -DSTRIDE_X=1
+ * @note The third dimensions of the weights tensors must be passed at compile time using -DWEIGHTS_DEPTH
+ * @note If biases are used then -DHAS_BIAS has to be passed at compile time
+ * @note The output quantization multiplier must be passed at compile time using -DOUTPUT_MULTIPLIER e.g. -DOUTPUT_MULTIPLIER=1234
+ * @note The output quantization shift must be passed at compile time using -DOUTPUT_SHIFT e.g. -DOUTPUT_SHIFT=4
+ *
+ * @param[in]  src_ptr                               Pointer to the source tensor. Supported data types: QASYMM8/QASYMM8_SIGNED
+ * @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 Z 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 Z processed per workitem(in bytes)
+ * @param[in]  dst_offset_first_element_in_bytes     The offset of the first element in the destination tensor
+ * @param[in]  weights_ptr                           Pointer to the weights tensor. Supported data types: same as @p src_ptr
+ * @param[in]  weights_stride_x                      Stride of the weights tensor in X dimension (in bytes)
+ * @param[in]  weights_step_x                        weights_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  weights_stride_y                      Stride of the weights tensor in Y dimension (in bytes)
+ * @param[in]  weights_step_y                        weights_stride_y * number of elements along y processed per workitem(in bytes)
+ * @param[in]  weights_stride_z                      Stride of the weights tensor in Z dimension (in bytes)
+ * @param[in]  weights_step_z                        weights_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  weights_offset_first_element_in_bytes The offset of the first element in the weights tensor
+ * @param[in]  biases_ptr                            Pointer to the biases tensor. Supported data types: S32
+ * @param[in]  biases_stride_x                       Stride of the biases tensor in X dimension (in bytes)
+ * @param[in]  biases_step_x                         biases_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  biases_offset_first_element_in_bytes  The offset of the first element in the biases tensor
+ * @param[in]  weights_stride_w                      Stride of the weights tensor in the 4th dimension
+ * @param[in]  input_offset                          Input offset quantization parameter
+ * @param[in]  weight_offset                         Weights offset quantization parameter
+ * @param[in]  output_offset                         Output offset quantization parameter
+ */
+__kernel void direct_convolution_quantized(
+    TENSOR3D_DECLARATION(src),
+    TENSOR3D_DECLARATION(dst),
+    TENSOR3D_DECLARATION(weights),
+#ifdef HAS_BIAS
+    VECTOR_DECLARATION(biases),
+#endif /* defined(HAS_BIAS) */
+    unsigned int weights_stride_w,
+    int          input_offset,
+    int          weight_offset,
+    int          output_offset)
+{
+    Image    src     = CONVERT_TO_IMAGE_STRUCT(src);
+    Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights);
+    Tensor3D dst     = CONVERT_TO_TENSOR3D_STRUCT(dst);
+
+    int8 values0 = 0;
+
+    const int y_coord = (get_global_id(2) * STRIDE_Y) - PAD_TOP;
+
+    __global DATA_TYPE *weights_addr = (__global DATA_TYPE *)tensor3D_offset(&weights, 0, 0, 0);
+    __global DATA_TYPE *src_addr     = (__global DATA_TYPE *)offset(&src, 0, 0) - src_stride_x * get_global_id(0) + y_coord * (int)src_stride_z;
+
+    const int kernel_index = get_global_id(2);
+    weights_addr += kernel_index * weights_stride_w;
+
+    for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d)
+    {
+#if KERNEL_SIZE == 5
+#if(PAD_TOP == 1)
+        if(y_coord < 0) // special case Z = -1 doesn't exists
+        {
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_z));
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_z));
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 3 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 3 * weights_stride_z));
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 4 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 4 * weights_stride_z));
+        }
+        else if(get_global_id(2) == (DST_HEIGHT - 1))
+        {
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 0 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 0 * weights_stride_z));
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_z));
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_z));
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 3 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 3 * weights_stride_z));
+        }
+        else
+        {
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 0 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 0 * weights_stride_z));
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_z));
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_z));
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 3 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 3 * weights_stride_z));
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 4 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 4 * weights_stride_z));
+        }
+#elif(PAD_TOP == 2)
+        if(y_coord < -1)
+        {
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_z));
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 3 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 3 * weights_stride_z));
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 4 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 4 * weights_stride_z));
+        }
+        else if(y_coord == -1)
+        {
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_z));
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_z));
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 3 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 3 * weights_stride_z));
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 4 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 4 * weights_stride_z));
+        }
+        else if(y_coord == (SRC_HEIGHT - 3))
+        {
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 0 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 0 * weights_stride_z));
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_z));
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_z));
+        }
+        else if(y_coord >= (SRC_HEIGHT - 4))
+        {
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 0 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 0 * weights_stride_z));
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_z));
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_z));
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 3 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 3 * weights_stride_z));
+        }
+        else
+        {
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 0 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 0 * weights_stride_z));
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_z));
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_z));
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 3 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 3 * weights_stride_z));
+            CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 4 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 4 * weights_stride_z));
+        }
+#else  /*  PAD_TOP == 2 */
+        CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 0 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 0 * weights_stride_z));
+        CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_z));
+        CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_z));
+        CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 3 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 3 * weights_stride_z));
+        CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 4 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 4 * weights_stride_z));
+#endif /*  PAD_TOP == 1 */
+#elif KERNEL_SIZE == 3
+#if PAD_TOP > 0
+        if(y_coord < 0) // special case Z = -1 doesn't exists
+        {
+            //skip first row and load the two next ones
+            CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_z));
+            CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_z));
+        }
+        else if(y_coord == (SRC_HEIGHT - PAD_TOP - 1))
+        {
+            // special case when computing the last row of the output we must read the last three rows from the input buffer (including padding) but the
+            // Z axis has no padding at all.
+            CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 0 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 0 * weights_stride_z));
+            CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_z));
+        }
+        else
+        {
+            CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 0 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 0 * weights_stride_z));
+            CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_z));
+            CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_z));
+        }
+#else  // PAD_TOP > 0
+        CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 0 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 0 * weights_stride_z));
+        CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_z));
+        CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_z), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_z));
+#endif // PAD_TOP > 0
+#elif KERNEL_SIZE == 1
+        int weight       = convert_int(*(__global DATA_TYPE *)weights_addr);
+        int8 input_value = convert_int8(INPUT_VALUE((__global DATA_TYPE *)src_addr, src_stride_y));
+        values0 += (input_value + input_offset) * ((int8)weight + weight_offset);
+#endif /* (KERNEL_SIZE == 1) || (KERNEL_SIZE == 3) || (KERNEL_SIZE == 5) */
+
+        src_addr += src_stride_x;
+        weights_addr += weights_stride_x;
+    }
+
+#ifdef HAS_BIAS
+    Vector        biases    = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);
+    __global int *bias_addr = ((__global int *)(vector_offset(&biases, get_global_id(0))));
+    values0 += (int8)(*bias_addr);
+#endif /* defined(HAS_BIAS) */
+
+#if OUTPUT_SHIFT < 0
+    values0 = ASYMM_MULT_BY_QUANT_MULTIPLIER_GREATER_THAN_ONE(values0, OUTPUT_MULTIPLIER, OUTPUT_SHIFT, 8);
+#else  // OUTPUT_SHIFT < 0
+    values0 = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(values0, OUTPUT_MULTIPLIER, OUTPUT_SHIFT, 8);
+#endif // OUTPUT_SHIFT < 0
+    values0 = values0 + output_offset;
+
+    VEC_DATA_TYPE(DATA_TYPE, 8)
+    values                        = CONVERT_SAT(values0, DATA_TYPE);
+    *(dst.ptr + 0 * dst_stride_y) = values.s0;
+    *(dst.ptr + 1 * dst_stride_y) = values.s1;
+    *(dst.ptr + 2 * dst_stride_y) = values.s2;
+    *(dst.ptr + 3 * dst_stride_y) = values.s3;
+    *(dst.ptr + 4 * dst_stride_y) = values.s4;
+    *(dst.ptr + 5 * dst_stride_y) = values.s5;
+    *(dst.ptr + 6 * dst_stride_y) = values.s6;
+    *(dst.ptr + 7 * dst_stride_y) = values.s7;
+}
+
+#else // defined(DATA_LAYOUT_NHWC)
+
 #if KERNEL_SIZE == 9
 
 #if STRIDE_X == 1
@@ -143,11 +572,11 @@
 #elif KERNEL_SIZE == 1
 
 #if STRIDE_X == 3
-#define INPUT_PIXEL extract_input_stride3
+#define INPUT_VALUE extract_input_stride3
 #elif STRIDE_X == 2
-#define INPUT_PIXEL extract_input_stride2
+#define INPUT_VALUE extract_input_stride2
 #elif STRIDE_X == 1
-#define INPUT_PIXEL extract_input_stride1
+#define INPUT_VALUE extract_input_stride1
 
 #else /* STRIDE_X not equals 1, 2 or 3 */
 #error "Only support strides 1, 2 and 3"
@@ -155,40 +584,40 @@
 
 /** Extracts a 1D horizontal vector from the input tensor with stride as 1.
  *
- * @param[in] input_pixel Pointer to the first pixel.
+ * @param[in] input_value Pointer to the first value.
  *
- * @return extracted input pixels.
+ * @return extracted input values.
  */
-inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride1(__global const DATA_TYPE *input_pixel)
+inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride1(__global const DATA_TYPE *input_value)
 {
-    return vload8(0, input_pixel);
+    return vload8(0, input_value);
 }
 
 /** Extracts a 1D horizontal vector from the input tensor with stride as 2.
  *
- * @param[in] input_pixel Pointer to the first pixel.
+ * @param[in] input_value Pointer to the first value.
  *
- * @return extracted input pixels.
+ * @return extracted input values.
  */
-inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride2(__global const DATA_TYPE *input_pixel)
+inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride2(__global const DATA_TYPE *input_value)
 {
     VEC_DATA_TYPE(DATA_TYPE, 16)
-    temp = vload16(0, input_pixel);
+    temp = vload16(0, input_value);
     return temp.s02468ace;
 }
 
 /** Extracts a 1D horizontal vector from the input tensor with stride as 3 and 8-bit data size.
  *
- * @param[in] input_pixel Pointer to the first pixel.
+ * @param[in] input_value Pointer to the first value.
  *
- * @return extracted input pixels.
+ * @return extracted input values.
  */
-inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride3(__global const DATA_TYPE *input_pixel)
+inline VEC_DATA_TYPE(DATA_TYPE, 8) extract_input_stride3(__global const DATA_TYPE *input_value)
 {
     VEC_DATA_TYPE(DATA_TYPE, 16)
-    temp1 = vload16(0, input_pixel);
+    temp1 = vload16(0, input_value);
     VEC_DATA_TYPE(DATA_TYPE, 16)
-    temp2 = vload16(0, input_pixel + 12);
+    temp2 = vload16(0, input_value + 12);
     return (VEC_DATA_TYPE(DATA_TYPE, 8))(temp1.s0369, temp2.s0369);
 }
 
@@ -253,7 +682,7 @@ __kernel void direct_convolution_quantized(
     Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights);
     Tensor3D dst     = CONVERT_TO_TENSOR3D_STRUCT(dst);
 
-    int8 pixels0 = 0;
+    int8 values0 = 0;
 
     __global DATA_TYPE *weights_addr = (__global DATA_TYPE *)tensor3D_offset(&weights, 0, 0, 0);
     __global DATA_TYPE *src_addr     = (__global DATA_TYPE *)offset(&src, 0, 0);
@@ -264,29 +693,29 @@ __kernel void direct_convolution_quantized(
     for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d)
     {
 #if KERNEL_SIZE == 9
-        CONVOLUTION1x9(pixels0, (__global DATA_TYPE *)(src_addr + 0 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 0 * weights_stride_y));
-        CONVOLUTION1x9(pixels0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y));
-        CONVOLUTION1x9(pixels0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y));
-        CONVOLUTION1x9(pixels0, (__global DATA_TYPE *)(src_addr + 3 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 3 * weights_stride_y));
-        CONVOLUTION1x9(pixels0, (__global DATA_TYPE *)(src_addr + 4 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 4 * weights_stride_y));
-        CONVOLUTION1x9(pixels0, (__global DATA_TYPE *)(src_addr + 5 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 5 * weights_stride_y));
-        CONVOLUTION1x9(pixels0, (__global DATA_TYPE *)(src_addr + 6 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 6 * weights_stride_y));
-        CONVOLUTION1x9(pixels0, (__global DATA_TYPE *)(src_addr + 7 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 7 * weights_stride_y));
-        CONVOLUTION1x9(pixels0, (__global DATA_TYPE *)(src_addr + 8 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 8 * weights_stride_y));
+        CONVOLUTION1x9(values0, (__global DATA_TYPE *)(src_addr + 0 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 0 * weights_stride_y));
+        CONVOLUTION1x9(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y));
+        CONVOLUTION1x9(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y));
+        CONVOLUTION1x9(values0, (__global DATA_TYPE *)(src_addr + 3 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 3 * weights_stride_y));
+        CONVOLUTION1x9(values0, (__global DATA_TYPE *)(src_addr + 4 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 4 * weights_stride_y));
+        CONVOLUTION1x9(values0, (__global DATA_TYPE *)(src_addr + 5 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 5 * weights_stride_y));
+        CONVOLUTION1x9(values0, (__global DATA_TYPE *)(src_addr + 6 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 6 * weights_stride_y));
+        CONVOLUTION1x9(values0, (__global DATA_TYPE *)(src_addr + 7 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 7 * weights_stride_y));
+        CONVOLUTION1x9(values0, (__global DATA_TYPE *)(src_addr + 8 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 8 * weights_stride_y));
 #elif KERNEL_SIZE == 5
-        CONVOLUTION1x5(pixels0, (__global DATA_TYPE *)src_addr, (__global DATA_TYPE *)weights_addr);
-        CONVOLUTION1x5(pixels0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y));
-        CONVOLUTION1x5(pixels0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y));
-        CONVOLUTION1x5(pixels0, (__global DATA_TYPE *)(src_addr + 3 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 3 * weights_stride_y));
-        CONVOLUTION1x5(pixels0, (__global DATA_TYPE *)(src_addr + 4 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 4 * weights_stride_y));
+        CONVOLUTION1x5(values0, (__global DATA_TYPE *)src_addr, (__global DATA_TYPE *)weights_addr);
+        CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y));
+        CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y));
+        CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 3 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 3 * weights_stride_y));
+        CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 4 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 4 * weights_stride_y));
 #elif KERNEL_SIZE == 3
-        CONVOLUTION1x3(pixels0, (__global DATA_TYPE *)(src_addr + 0 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 0 * weights_stride_y));
-        CONVOLUTION1x3(pixels0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y));
-        CONVOLUTION1x3(pixels0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y));
+        CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 0 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 0 * weights_stride_y));
+        CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y));
+        CONVOLUTION1x3(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y));
 #elif KERNEL_SIZE == 1
         int weight       = convert_int(*(__global DATA_TYPE *)weights_addr);
-        int8 input_pixel = convert_int8(INPUT_PIXEL((__global DATA_TYPE *)src_addr));
-        pixels0 += (input_pixel + input_offset) * ((int8)weight + weight_offset);
+        int8 input_value = convert_int8(INPUT_VALUE((__global DATA_TYPE *)src_addr));
+        values0 += (input_value + input_offset) * ((int8)weight + weight_offset);
 #endif /* (KERNEL_SIZE == 1) || (KERNEL_SIZE == 3) || (KERNEL_SIZE == 5) */
 
         src_addr += src_stride_z;
@@ -296,16 +725,18 @@ __kernel void direct_convolution_quantized(
 #ifdef HAS_BIAS
     Vector        biases    = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);
     __global int *bias_addr = ((__global int *)(vector_offset(&biases, kernel_index)));
-    pixels0 += (int8)(*bias_addr);
+    values0 += (int8)(*bias_addr);
 #endif /* defined(HAS_BIAS) */
 
 #if OUTPUT_SHIFT < 0
-    pixels0 = ASYMM_MULT_BY_QUANT_MULTIPLIER_GREATER_THAN_ONE(pixels0, OUTPUT_MULTIPLIER, OUTPUT_SHIFT, 8);
+    values0 = ASYMM_MULT_BY_QUANT_MULTIPLIER_GREATER_THAN_ONE(values0, OUTPUT_MULTIPLIER, OUTPUT_SHIFT, 8);
 #else  // OUTPUT_SHIFT < 0
-    pixels0 = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(pixels0, OUTPUT_MULTIPLIER, OUTPUT_SHIFT, 8);
+    values0 = ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(values0, OUTPUT_MULTIPLIER, OUTPUT_SHIFT, 8);
 #endif // OUTPUT_SHIFT < 0
-    pixels0 = pixels0 + output_offset;
+    values0 = values0 + output_offset;
 
-    vstore8(CONVERT_SAT(pixels0, DATA_TYPE), 0, (__global DATA_TYPE *)dst.ptr);
+    vstore8(CONVERT_SAT(values0, DATA_TYPE), 0, (__global DATA_TYPE *)dst.ptr);
 }
+
+#endif // defined(DATA_LAYOUT_NHWC)
 #endif // defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH) && defined(OUTPUT_MULTIPLIER) && defined(OUTPUT_SHIFT)
diff --git a/tests/validation/CL/DirectConvolutionLayer.cpp b/tests/validation/CL/DirectConvolutionLayer.cpp
index f5cb843f42..3c39151a29 100644
--- a/tests/validation/CL/DirectConvolutionLayer.cpp
+++ b/tests/validation/CL/DirectConvolutionLayer.cpp
@@ -249,35 +249,41 @@ const auto QuantizedActivationFunctionsDataset = framework::dataset::make("Activ
 });
 TEST_SUITE(Quantized)
 TEST_SUITE(QASYMM8)
-FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit, framework::dataset::make("DataType",
-                       DataType::QASYMM8)),
+FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(data_precommit,
+                       framework::dataset::make("DataType",
+                                                DataType::QASYMM8)),
                        framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 10), QuantizationInfo(1.1f, 10) })),
-                       QuantizedActivationFunctionsDataset))
+                       QuantizedActivationFunctionsDataset),
+                       framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_qasymm8);
 }
-FIXTURE_DATA_TEST_CASE(RunSmall9x9, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit_9x9,
+FIXTURE_DATA_TEST_CASE(RunSmall9x9, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(combine(data_precommit_9x9,
                        framework::dataset::make("DataType",
                                                 DataType::QASYMM8)),
                        framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 10), QuantizationInfo(1.1f, 10) })),
-                       QuantizedActivationFunctionsDataset))
+                       QuantizedActivationFunctionsDataset),
+                       framework::dataset::make("DataLayout", { DataLayout::NCHW })))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_qasymm8);
 }
-FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data_nightly, framework::dataset::make("DataType",
+FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(data_nightly, framework::dataset::make("DataType",
                        DataType::QASYMM8)),
                        framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 10), QuantizationInfo(1.1f, 10) })),
-                       QuantizedActivationFunctionsDataset))
+                       QuantizedActivationFunctionsDataset),
+                       framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_qasymm8);
 }
-FIXTURE_DATA_TEST_CASE(RunLarge9x9, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data_nightly_9x9, framework::dataset::make("DataType",
-                       DataType::QASYMM8)),
+FIXTURE_DATA_TEST_CASE(RunLarge9x9, CLDirectConvolutionLayerQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(combine(data_nightly_9x9,
+                       framework::dataset::make("DataType",
+                                                DataType::QASYMM8)),
                        framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 10), QuantizationInfo(1.1f, 10) })),
-                       QuantizedActivationFunctionsDataset))
+                       QuantizedActivationFunctionsDataset),
+                       framework::dataset::make("DataLayout", { DataLayout::NCHW })))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_qasymm8);
@@ -286,10 +292,12 @@ FIXTURE_DATA_TEST_CASE(RunLarge9x9, CLDirectConvolutionLayerQuantizedFixture<uin
 TEST_SUITE_END() // QASYMM8
 
 TEST_SUITE(QASYMM8_CustomDataset)
-FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionValidationWithTensorShapesQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::DirectConvolutionLayerDataset(),
-                       framework::dataset::make("DataType", DataType::QASYMM8)),
-                       framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 127), QuantizationInfo(1.1f, 10) })),
-                       QuantizedActivationFunctionsDataset))
+FIXTURE_DATA_TEST_CASE(Run, CLDirectConvolutionValidationWithTensorShapesQuantizedFixture<uint8_t>, framework::DatasetMode::NIGHTLY,
+                       combine(combine(combine(combine(datasets::DirectConvolutionLayerDataset(),
+                                                       framework::dataset::make("DataType", DataType::QASYMM8)),
+                                               framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 127), QuantizationInfo(1.1f, 10) })),
+                                       QuantizedActivationFunctionsDataset),
+                               framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_qasymm8);
@@ -298,30 +306,33 @@ TEST_SUITE_END() // QASYMM8_CustomDataset
 
 TEST_SUITE(QASYMM8_SIGNED)
 
-FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerQuantizedFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(data_precommit, framework::dataset::make("DataType",
+FIXTURE_DATA_TEST_CASE(RunSmall, CLDirectConvolutionLayerQuantizedFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(data_precommit, framework::dataset::make("DataType",
                                                                                                                         DataType::QASYMM8_SIGNED)),
                                                                                                                         framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 10), QuantizationInfo(1.1f, -10) })),
-                                                                                                                        QuantizedActivationFunctionsDataset))
+                                                                                                                        QuantizedActivationFunctionsDataset),
+                                                                                                                        framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_qasymm8);
 }
 
-FIXTURE_DATA_TEST_CASE(RunSmall9x9, CLDirectConvolutionLayerQuantizedFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(data_precommit_9x9,
+FIXTURE_DATA_TEST_CASE(RunSmall9x9, CLDirectConvolutionLayerQuantizedFixture<int8_t>, framework::DatasetMode::ALL, combine(combine(combine(combine(data_precommit_9x9,
                        framework::dataset::make("DataType",
                                                 DataType::QASYMM8_SIGNED)),
                        framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 10), QuantizationInfo(1.1f, 10) })),
-                       QuantizedActivationFunctionsDataset))
+                       QuantizedActivationFunctionsDataset),
+                       framework::dataset::make("DataLayout", { DataLayout::NCHW })))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_qasymm8);
 }
 
 FIXTURE_DATA_TEST_CASE(RunCustomDataset, CLDirectConvolutionValidationWithTensorShapesQuantizedFixture<int8_t>, framework::DatasetMode::NIGHTLY,
-                       combine(combine(combine(datasets::DirectConvolutionLayerDataset(),
-                                               framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)),
-                                       framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 127), QuantizationInfo(1.1f, 10) })),
-                               QuantizedActivationFunctionsDataset))
+                       combine(combine(combine(combine(datasets::DirectConvolutionLayerDataset(),
+                                                       framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)),
+                                               framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255, 127), QuantizationInfo(1.1f, 10) })),
+                                       QuantizedActivationFunctionsDataset),
+                               framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })))
 {
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_qasymm8);
diff --git a/tests/validation/NEON/DirectConvolutionLayer.cpp b/tests/validation/NEON/DirectConvolutionLayer.cpp
index 439f81af73..05bfbc171a 100644
--- a/tests/validation/NEON/DirectConvolutionLayer.cpp
+++ b/tests/validation/NEON/DirectConvolutionLayer.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2019 ARM Limited.
+ * Copyright (c) 2017-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -229,14 +229,6 @@ FIXTURE_DATA_TEST_CASE(RunLarge, NEDirectConvolutionLayerFixture<float>, framewo
 }
 TEST_SUITE_END() // FP32
 TEST_SUITE_END() // Float
-
-const auto QuantizedActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
-{
-    ActivationLayerInfo(),
-    ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU),
-    ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 6.f)
-});
-
 TEST_SUITE_END() // DirectConvolutionLayer
 TEST_SUITE_END() // NEON
 } // namespace validation
diff --git a/tests/validation/fixtures/DirectConvolutionLayerFixture.h b/tests/validation/fixtures/DirectConvolutionLayerFixture.h
index fc36547c53..1d67a2a653 100644
--- a/tests/validation/fixtures/DirectConvolutionLayerFixture.h
+++ b/tests/validation/fixtures/DirectConvolutionLayerFixture.h
@@ -216,10 +216,10 @@ class DirectConvolutionValidationQuantizedFixture : public DirectConvolutionVali
 public:
     template <typename...>
     void setup(TensorShape input_shape, int stride_x, int stride_y, int pad_x, int pad_y, unsigned int kernel_size, unsigned int num_kernels, DataType data_type, QuantizationInfo quantization_info,
-               ActivationLayerInfo act_info)
+               ActivationLayerInfo act_info, DataLayout data_layout)
     {
         DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, stride_x, stride_y, pad_x, pad_y, kernel_size, num_kernels, data_type, quantization_info,
-                                                                                                    act_info, DataLayout::NCHW);
+                                                                                                    act_info, data_layout);
     }
 };
 
@@ -229,10 +229,10 @@ class DirectConvolutionValidationWithTensorShapesQuantizedFixture : public Direc
 public:
     template <typename...>
     void setup(TensorShape input_shape, TensorShape weights_shape, TensorShape bias_shape, TensorShape output_shape, PadStrideInfo info, Size2D dilation,
-               DataType data_type, QuantizationInfo quantization_info, ActivationLayerInfo act_info)
+               DataType data_type, QuantizationInfo quantization_info, ActivationLayerInfo act_info, DataLayout data_layout)
     {
         DirectConvolutionValidationGenericFixture<TensorType, AccessorType, FunctionType, T>::setup(input_shape, weights_shape, bias_shape, output_shape, info, dilation, data_type, quantization_info,
-                                                                                                    act_info, DataLayout::NCHW);
+                                                                                                    act_info, data_layout);
     }
 };
 
diff --git a/tests/validation/reference/ConvolutionLayer.cpp b/tests/validation/reference/ConvolutionLayer.cpp
index c9ad8d38b9..84fb3491bd 100644
--- a/tests/validation/reference/ConvolutionLayer.cpp
+++ b/tests/validation/reference/ConvolutionLayer.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2019 ARM Limited.
+ * Copyright (c) 2017-2020 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -115,18 +115,7 @@ SimpleTensor<T> convolution_layer(const SimpleTensor<T> &src, const SimpleTensor
     // Create reference
     SimpleTensor<T> dst{ output_shape, src.data_type(), 1, out_quant_info };
 
-    if(src.data_layout() == DataLayout::NHWC)
-    {
-        SimpleTensor<T>  src_nchw     = reference::permute<T>(src, PermutationVector(1U, 2U, 0U));
-        SimpleTensor<TW> weights_nchw = reference::permute<TW>(weights, PermutationVector(1U, 2U, 0U));
-        SimpleTensor<T>  dst_nchw     = reference::permute<T>(dst, PermutationVector(1U, 2U, 0U));
-
-        return reference::permute<T>(convolution_layer_nchw(src_nchw, weights_nchw, bias, dst_nchw, info, dilation, num_groups), PermutationVector(2U, 0U, 1U));
-    }
-    else
-    {
-        return convolution_layer_nchw(src, weights, bias, dst, info, dilation, num_groups);
-    }
+    return convolution_layer_nchw(src, weights, bias, dst, info, dilation, num_groups);
 }
 
 template SimpleTensor<float> convolution_layer(const SimpleTensor<float> &src, const SimpleTensor<float> &weights, const SimpleTensor<float> &bias, const TensorShape &output_shape,