COMPMID-811 Add NHWC data format support for CL depthwise convolution QASYMM8

Change-Id: I89de432f3fbcba7abf9e1d4f8396a4334b4fa2c2
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/118324
Tested-by: Jenkins <bsgcomp@arm.com>
Reviewed-by: Gian Marco Iodice <gianmarco.iodice@arm.com>

5 files changed, 762 insertions, 21 deletions

diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index 8405754f52..0509f1e785 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -191,7 +191,9 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map =
     { "deconvolution_upsample", "deconvolution_layer.cl" },
     { "depthwise_convolution_3x3", "depthwise_convolution.cl" },
     { "depthwise_convolution_3x3_f16", "depthwise_convolution.cl" },
-    { "depthwise_convolution_3x3_quantized", "depthwise_convolution_quantized.cl" },
+    { "depthwise_convolution_3x3_quantized_nchw", "depthwise_convolution_quantized.cl" },
+    { "depthwise_convolution_3x3_quantized_nhwc_stride1", "depthwise_convolution_quantized.cl" },
+    { "depthwise_convolution_3x3_quantized_nhwc_stride2", "depthwise_convolution_quantized.cl" },
     { "depthwise_convolution_3x3_stridex1_stridey1_bifrost_f16", "depthwise_convolution.cl" },
     { "depthwise_convolution_3x3_stridex2_stridey2_bifrost_f16", "depthwise_convolution.cl" },
     { "depthwise_convolution_3x3_stridex1_stridey1_bifrost_f32", "depthwise_convolution.cl" },
diff --git a/src/core/CL/cl_kernels/depthwise_convolution_quantized.cl b/src/core/CL/cl_kernels/depthwise_convolution_quantized.cl
index 21daee8230..635bc9d50b 100644
--- a/src/core/CL/cl_kernels/depthwise_convolution_quantized.cl
+++ b/src/core/CL/cl_kernels/depthwise_convolution_quantized.cl
@@ -24,17 +24,21 @@
 
 #include "helpers_asymm.h"
 
-#if defined(CONV_STRIDE_X) && defined(CONV_STRIDE_Y) && defined(WEIGHTS_OFFSET) && defined(INPUT_OFFSET) && defined(K_OFFSET) && defined(OUTPUT_OFFSET) && defined(OUTPUT_MULTIPLIER) && defined(OUTPUT_SHIFT)
+#if defined(WEIGHTS_OFFSET) && defined(INPUT_OFFSET) && defined(K_OFFSET) && defined(OUTPUT_OFFSET) && defined(OUTPUT_MULTIPLIER) && defined(OUTPUT_SHIFT)
 
 #if defined(FUSED_ACTIVATION)
 #define DATA_TYPE uchar
+#ifndef VEC_SIZE
 #define VEC_SIZE 8
+#endif /* VEC_SIZE */
 #include "activation_layer_qa8.cl"
 #define ACTIVATION_FUNC(x) PERFORM_ACTIVATION_QA8(FUSED_ACTIVATION, x)
 #else /* defined(FUSED_ACTIVATION) */
 #define ACTIVATION_FUNC(x) (x)
 #endif /* defined(FUSED_ACTIVATION) */
 
+#if defined(CONV_STRIDE_Y) && defined(CONV_STRIDE_X)
+
 #if CONV_STRIDE_X > 3
 #error "Stride X not supported"
 #endif /* CONV_STRIDE_X > 3 */
@@ -71,7 +75,7 @@
     })
 #endif /* CONV_STRIDE_X */
 
-/** This function computes the horizontal integral of the image and adds offsets.
+/** This function computes the depthwise convolution quantized.
  *
  * @param[in] src_ptr                               Pointer to the source image. Supported data types: QASYMM8
  * @param[in] src_stride_x                          Stride of the source image in X dimension (in bytes)
@@ -103,7 +107,7 @@
  * @param[in] biases_offset_first_element_in_bytes  (Optional) The offset of the first element in the biases vector
  */
 
-__kernel void depthwise_convolution_3x3_quantized(
+__kernel void depthwise_convolution_3x3_quantized_nchw(
     TENSOR3D_DECLARATION(src),
     TENSOR3D_DECLARATION(dst),
     TENSOR3D_DECLARATION(weights)
@@ -244,4 +248,478 @@ __kernel void depthwise_convolution_3x3_quantized(
 #endif /* CONV_STRIDE_Y == 1 */
 }
 
-#endif /* defined(CONV_STRIDE_X) && defined(CONV_STRIDE_Y) && defined(WEIGHTS_OFFSET) && defined(INPUT_OFFSET) && defined(K_OFFSET) && defined(OUTPUT_OFFSET) && defined(OUTPUT_MULTIPLIER) && defined(OUTPUT_SHIFT) */
+#endif /* defined(CONV_STRIDE_Y) && defined(CONV_STRIDE_X) */
+
+#if defined(VEC_SIZE) && defined(SRC_DEPTH) && defined(CONV_PAD_TOP) && defined(ROWS_READ)
+
+#define asymm_mult_by_quant_multiplier_less_than_one(x, y, z) ASYMM_MULT_BY_QUANT_MULTIPLIER_LESS_THAN_ONE(x, y, z, VEC_SIZE)
+
+#define VEC_INT VEC_DATA_TYPE(int, VEC_SIZE)
+#define VEC_UCHAR VEC_DATA_TYPE(uchar, VEC_SIZE)
+
+#define BIFROST_MAD_4(acc, x, y)               \
+    ({                                         \
+        acc.s0 += (ushort)x.s0 * (ushort)y.s0; \
+        acc.s1 += (ushort)x.s1 * (ushort)y.s1; \
+        acc.s2 += (ushort)x.s2 * (ushort)y.s2; \
+        acc.s3 += (ushort)x.s3 * (ushort)y.s3; \
+    })
+
+#if WEIGHTS_OFFSET != 0
+#define BIFROST_MAD_ACC_4(acc, sum, x, y) \
+    ({                                    \
+        sum += CONVERT(x, VEC_INT);       \
+        BIFROST_MAD_4(acc, x, y);         \
+    })
+#else /* WEIGHTS_OFFSET != 0 */
+#define BIFROST_MAD_ACC_4(acc, sum, x, y) BIFROST_MAD_4(acc, x, y)
+#endif /* WEIGHTS_OFFSET != 0 */
+
+/** This function computes the depthwise convolution quantized.
+ *
+ * @param[in] src_ptr                               Pointer to the source image. Supported data types: QASYMM8
+ * @param[in] src_stride_x                          Stride of the source image in X dimension (in bytes)
+ * @param[in] src_step_x                            src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y                          Stride of the source image in Y dimension (in bytes)
+ * @param[in] src_step_y                            src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes     The offset of the first element in the source image
+ * @param[in] src_stride_z                          Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z                            src_stride_z * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_ptr                               Pointer to the destination tensor. Supported data types: QASYMM8
+ * @param[in] dst_stride_x                          Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] dst_step_x                            dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y                          Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in] dst_step_y                            dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_stride_z                          Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in] dst_step_z                            dst_stride_z * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes     The offset of the first element in the destination tensor
+ * @param[in] weights_ptr                           Pointer to the weights tensor. Supported data types: QASYMM8
+ * @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 Y 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                            (Optional) Pointer to the biases vector. Supported data types: QASYMM8
+ * @param[in] biases_stride_x                       (Optional) Stride of the biases vector in X dimension (in bytes)
+ * @param[in] biases_step_x                         (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] biases_offset_first_element_in_bytes  (Optional) The offset of the first element in the biases vector
+ */
+
+__kernel void depthwise_convolution_3x3_quantized_nhwc_stride1(
+    TENSOR3D_DECLARATION(src),
+    TENSOR3D_DECLARATION(dst),
+    TENSOR3D_DECLARATION(weights),
+#if defined(HAS_BIAS)
+    VECTOR_DECLARATION(biases)
+#endif /* defined(HAS_BIAS) */
+)
+{
+    Image  dst     = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(dst);
+    Vector weights = CONVERT_TO_VECTOR_STRUCT(weights);
+#if defined(HAS_BIAS)
+    Vector biases = CONVERT_TO_VECTOR_STRUCT(biases);
+
+    VEC_INT bias_values = VLOAD(VEC_SIZE)(0, (__global int *)biases.ptr);
+#endif /* defined(HAS_BIAS) */
+
+    __global uchar *first_elem = src_ptr + src_offset_first_element_in_bytes;
+
+    const int z         = get_global_id(2);
+    const int pad_offs  = -ROWS_READ * src_stride_y;
+    const int src_offs0 = get_global_id(0) * src_step_x + get_global_id(1) * src_step_y + z * src_step_z - CONV_PAD_TOP * src_stride_z;
+    const int src_offs1 = src_offs0 + src_stride_z;
+    const int src_offs2 = src_offs1 + src_stride_z;
+
+    const int cond_top    = z - CONV_PAD_TOP < 0;
+    const int cond_bottom = z * (src_step_z / src_stride_z) + 2 >= SRC_DEPTH;
+
+    __global uchar *src_addr0 = first_elem + select(src_offs0, pad_offs, cond_top);
+    __global uchar *src_addr1 = first_elem + src_offs1;
+    __global uchar *src_addr2 = first_elem + select(src_offs2, pad_offs, cond_bottom);
+
+    VEC_INT sum_we = 0;
+    VEC_INT acc0 = 0, acc1 = 0, acc2 = 0, acc3 = 0;
+    VEC_INT sum0 = 0, sum1 = 0, sum2 = 0, sum3 = 0;
+
+    // z == 0
+    VEC_UCHAR w0, w1, w2;
+    w0 = VLOAD(VEC_SIZE)(0, weights.ptr + 0 * weights_stride_y);
+    w1 = VLOAD(VEC_SIZE)(0, weights.ptr + 1 * weights_stride_y);
+    w2 = VLOAD(VEC_SIZE)(0, weights.ptr + 2 * weights_stride_y);
+
+#if INPUT_OFFSET != 0
+    sum_we += CONVERT(w0, VEC_INT) + CONVERT(w1, VEC_INT) + CONVERT(w2, VEC_INT);
+#endif /* INPUT_OFFSET != 0 */
+
+    VEC_UCHAR values = VLOAD(VEC_SIZE)(0, src_addr0);
+    BIFROST_MAD_ACC_4(acc0, sum0, values, w0);
+
+    src_addr0 += src_stride_y;
+    values = VLOAD(VEC_SIZE)(0, src_addr0);
+    BIFROST_MAD_ACC_4(acc0, sum0, values, w1);
+    BIFROST_MAD_ACC_4(acc1, sum1, values, w0);
+
+    src_addr0 += src_stride_y;
+    values = VLOAD(VEC_SIZE)(0, src_addr0);
+    BIFROST_MAD_ACC_4(acc0, sum0, values, w2);
+    BIFROST_MAD_ACC_4(acc1, sum1, values, w1);
+    BIFROST_MAD_ACC_4(acc2, sum2, values, w0);
+
+    src_addr0 += src_stride_y;
+    values = VLOAD(VEC_SIZE)(0, src_addr0);
+    BIFROST_MAD_ACC_4(acc1, sum1, values, w2);
+    BIFROST_MAD_ACC_4(acc2, sum2, values, w1);
+    BIFROST_MAD_ACC_4(acc3, sum3, values, w0);
+
+    src_addr0 += src_stride_y;
+    values = VLOAD(VEC_SIZE)(0, src_addr0);
+    BIFROST_MAD_ACC_4(acc2, sum2, values, w2);
+    BIFROST_MAD_ACC_4(acc3, sum3, values, w1);
+
+    src_addr0 += src_stride_y;
+    values = VLOAD(VEC_SIZE)(0, src_addr0);
+    BIFROST_MAD_ACC_4(acc3, sum3, values, w2);
+
+    weights.ptr += weights_stride_z;
+
+    // z == 1
+    w0 = VLOAD(VEC_SIZE)(0, weights.ptr + 0 * weights_stride_y);
+    w1 = VLOAD(VEC_SIZE)(0, weights.ptr + 1 * weights_stride_y);
+    w2 = VLOAD(VEC_SIZE)(0, weights.ptr + 2 * weights_stride_y);
+
+#if INPUT_OFFSET != 0
+    sum_we += CONVERT(w0, VEC_INT) + CONVERT(w1, VEC_INT) + CONVERT(w2, VEC_INT);
+#endif /* INPUT_OFFSET != 0 */
+
+    values = VLOAD(VEC_SIZE)(0, src_addr1);
+    BIFROST_MAD_ACC_4(acc0, sum0, values, w0);
+
+    src_addr1 += src_stride_y;
+    values = VLOAD(VEC_SIZE)(0, src_addr1);
+    BIFROST_MAD_ACC_4(acc0, sum0, values, w1);
+    BIFROST_MAD_ACC_4(acc1, sum1, values, w0);
+
+    src_addr1 += src_stride_y;
+    values = VLOAD(VEC_SIZE)(0, src_addr1);
+    BIFROST_MAD_ACC_4(acc0, sum0, values, w2);
+    BIFROST_MAD_ACC_4(acc1, sum1, values, w1);
+    BIFROST_MAD_ACC_4(acc2, sum2, values, w0);
+
+    src_addr1 += src_stride_y;
+    values = VLOAD(VEC_SIZE)(0, src_addr1);
+    BIFROST_MAD_ACC_4(acc1, sum1, values, w2);
+    BIFROST_MAD_ACC_4(acc2, sum2, values, w1);
+    BIFROST_MAD_ACC_4(acc3, sum3, values, w0);
+
+    src_addr1 += src_stride_y;
+    values = VLOAD(VEC_SIZE)(0, src_addr1);
+    BIFROST_MAD_ACC_4(acc2, sum2, values, w2);
+    BIFROST_MAD_ACC_4(acc3, sum3, values, w1);
+
+    src_addr1 += src_stride_y;
+    values = VLOAD(VEC_SIZE)(0, src_addr1);
+    BIFROST_MAD_ACC_4(acc3, sum3, values, w2);
+
+    weights.ptr += weights_stride_z;
+
+    // z == 2
+    w0 = VLOAD(VEC_SIZE)(0, weights.ptr + 0 * weights_stride_y);
+    w1 = VLOAD(VEC_SIZE)(0, weights.ptr + 1 * weights_stride_y);
+    w2 = VLOAD(VEC_SIZE)(0, weights.ptr + 2 * weights_stride_y);
+
+#if INPUT_OFFSET != 0
+    sum_we += CONVERT(w0, VEC_INT) + CONVERT(w1, VEC_INT) + CONVERT(w2, VEC_INT);
+#endif /* INPUT_OFFSET != 0 */
+
+    values = VLOAD(VEC_SIZE)(0, src_addr2);
+    BIFROST_MAD_ACC_4(acc0, sum0, values, w0);
+
+    src_addr2 += src_stride_y;
+    values = VLOAD(VEC_SIZE)(0, src_addr2);
+    BIFROST_MAD_ACC_4(acc0, sum0, values, w1);
+    BIFROST_MAD_ACC_4(acc1, sum1, values, w0);
+
+    src_addr2 += src_stride_y;
+    values = VLOAD(VEC_SIZE)(0, src_addr2);
+    BIFROST_MAD_ACC_4(acc0, sum0, values, w2);
+    BIFROST_MAD_ACC_4(acc1, sum1, values, w1);
+    BIFROST_MAD_ACC_4(acc2, sum2, values, w0);
+
+    src_addr2 += src_stride_y;
+    values = VLOAD(VEC_SIZE)(0, src_addr2);
+    BIFROST_MAD_ACC_4(acc1, sum1, values, w2);
+    BIFROST_MAD_ACC_4(acc2, sum2, values, w1);
+    BIFROST_MAD_ACC_4(acc3, sum3, values, w0);
+
+    src_addr2 += src_stride_y;
+    values = VLOAD(VEC_SIZE)(0, src_addr2);
+    BIFROST_MAD_ACC_4(acc2, sum2, values, w2);
+    BIFROST_MAD_ACC_4(acc3, sum3, values, w1);
+
+    src_addr2 += src_stride_y;
+    values = VLOAD(VEC_SIZE)(0, src_addr2);
+    BIFROST_MAD_ACC_4(acc3, sum3, values, w2);
+
+#if defined(HAS_BIAS)
+    acc0 += bias_values;
+    acc1 += bias_values;
+    acc2 += bias_values;
+    acc3 += bias_values;
+#endif /* defined(HAS_BIAS) */
+
+#if WEIGHTS_OFFSET != 0
+    acc0 += WEIGHTS_OFFSET * sum0;
+    acc1 += WEIGHTS_OFFSET * sum1;
+    acc2 += WEIGHTS_OFFSET * sum2;
+    acc3 += WEIGHTS_OFFSET * sum3;
+#endif /* WEIGHTS_OFFSET != 0 */
+
+#if INPUT_OFFSET != 0
+    VEC_INT offs = INPUT_OFFSET * sum_we;
+
+    acc0 += offs;
+    acc1 += offs;
+    acc2 += offs;
+    acc3 += offs;
+#endif /* INPUT_OFFSET != 0 */
+
+#if K_OFFSET != 0
+    acc0 += (VEC_INT)K_OFFSET;
+    acc1 += (VEC_INT)K_OFFSET;
+    acc2 += (VEC_INT)K_OFFSET;
+    acc3 += (VEC_INT)K_OFFSET;
+#endif /* K_OFFSET != 0 */
+
+    acc0 = asymm_mult_by_quant_multiplier_less_than_one(acc0, OUTPUT_MULTIPLIER, OUTPUT_SHIFT);
+    acc1 = asymm_mult_by_quant_multiplier_less_than_one(acc1, OUTPUT_MULTIPLIER, OUTPUT_SHIFT);
+    acc2 = asymm_mult_by_quant_multiplier_less_than_one(acc2, OUTPUT_MULTIPLIER, OUTPUT_SHIFT);
+    acc3 = asymm_mult_by_quant_multiplier_less_than_one(acc3, OUTPUT_MULTIPLIER, OUTPUT_SHIFT);
+
+    acc0 += (VEC_INT)OUTPUT_OFFSET;
+    acc1 += (VEC_INT)OUTPUT_OFFSET;
+    acc2 += (VEC_INT)OUTPUT_OFFSET;
+    acc3 += (VEC_INT)OUTPUT_OFFSET;
+
+    VEC_UCHAR res0 = CONVERT_SAT(acc0, VEC_UCHAR);
+    VEC_UCHAR res1 = CONVERT_SAT(acc1, VEC_UCHAR);
+    VEC_UCHAR res2 = CONVERT_SAT(acc2, VEC_UCHAR);
+    VEC_UCHAR res3 = CONVERT_SAT(acc3, VEC_UCHAR);
+
+    res0 = CLAMP(res0, (VEC_UCHAR)0, (VEC_UCHAR)255);
+    res1 = CLAMP(res1, (VEC_UCHAR)0, (VEC_UCHAR)255);
+    res2 = CLAMP(res2, (VEC_UCHAR)0, (VEC_UCHAR)255);
+    res3 = CLAMP(res3, (VEC_UCHAR)0, (VEC_UCHAR)255);
+
+    VSTORE(VEC_SIZE)
+    (res0, 0, dst.ptr + 0 * dst_stride_y);
+    VSTORE(VEC_SIZE)
+    (res1, 0, dst.ptr + 1 * dst_stride_y);
+    VSTORE(VEC_SIZE)
+    (res2, 0, dst.ptr + 2 * dst_stride_y);
+    VSTORE(VEC_SIZE)
+    (res3, 0, dst.ptr + 3 * dst_stride_y);
+}
+
+/** This function computes the depthwise convolution quantized.
+ *
+ * @param[in] src_ptr                               Pointer to the source image. Supported data types: QASYMM8
+ * @param[in] src_stride_x                          Stride of the source image in X dimension (in bytes)
+ * @param[in] src_step_x                            src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] src_stride_y                          Stride of the source image in Y dimension (in bytes)
+ * @param[in] src_step_y                            src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] src_offset_first_element_in_bytes     The offset of the first element in the source image
+ * @param[in] src_stride_z                          Stride of the source tensor in Z dimension (in bytes)
+ * @param[in] src_step_z                            src_stride_z * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_ptr                               Pointer to the destination tensor. Supported data types: QASYMM8
+ * @param[in] dst_stride_x                          Stride of the destination tensor in X dimension (in bytes)
+ * @param[in] dst_step_x                            dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] dst_stride_y                          Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in] dst_step_y                            dst_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_stride_z                          Stride of the destination tensor in Z dimension (in bytes)
+ * @param[in] dst_step_z                            dst_stride_z * number of elements along Y processed per workitem(in bytes)
+ * @param[in] dst_offset_first_element_in_bytes     The offset of the first element in the destination tensor
+ * @param[in] weights_ptr                           Pointer to the weights tensor. Supported data types: QASYMM8
+ * @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 Y 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                            (Optional) Pointer to the biases vector. Supported data types: QASYMM8
+ * @param[in] biases_stride_x                       (Optional) Stride of the biases vector in X dimension (in bytes)
+ * @param[in] biases_step_x                         (Optional) biases_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in] biases_offset_first_element_in_bytes  (Optional) The offset of the first element in the biases vector
+ */
+
+__kernel void depthwise_convolution_3x3_quantized_nhwc_stride2(
+    TENSOR3D_DECLARATION(src),
+    TENSOR3D_DECLARATION(dst),
+    TENSOR3D_DECLARATION(weights),
+#if defined(HAS_BIAS)
+    VECTOR_DECLARATION(biases)
+#endif /* defined(HAS_BIAS) */
+)
+{
+    Image  dst     = CONVERT_TENSOR3D_TO_IMAGE_STRUCT(dst);
+    Vector weights = CONVERT_TO_VECTOR_STRUCT(weights);
+#if defined(HAS_BIAS)
+    Vector biases = CONVERT_TO_VECTOR_STRUCT(biases);
+
+    VEC_INT bias_values = VLOAD(VEC_SIZE)(0, (__global int *)biases.ptr);
+#endif /* defined(HAS_BIAS) */
+
+    __global uchar *first_elem = src_ptr + src_offset_first_element_in_bytes;
+
+    const int z         = get_global_id(2);
+    const int pad_offs  = -ROWS_READ * src_stride_y;
+    const int src_offs0 = get_global_id(0) * src_step_x + get_global_id(1) * src_step_y + z * src_step_z - CONV_PAD_TOP * src_stride_z;
+    const int src_offs1 = src_offs0 + src_stride_z;
+    const int src_offs2 = src_offs1 + src_stride_z;
+
+    const int cond_top    = z - CONV_PAD_TOP < 0;
+    const int cond_bottom = z * (src_step_z / src_stride_z) + 2 >= SRC_DEPTH;
+    ;
+
+    __global uchar *src_addr0 = first_elem + select(src_offs0, pad_offs, cond_top);
+    __global uchar *src_addr1 = first_elem + src_offs1;
+    __global uchar *src_addr2 = first_elem + select(src_offs2, pad_offs, cond_bottom);
+
+    VEC_INT sum_we = 0;
+    VEC_INT acc0 = 0, acc2 = 0;
+    VEC_INT sum0 = 0, sum2 = 0;
+
+    // z == 0
+    VEC_UCHAR w0, w1, w2;
+    w0 = VLOAD(VEC_SIZE)(0, weights.ptr + 0 * weights_stride_y);
+    w1 = VLOAD(VEC_SIZE)(0, weights.ptr + 1 * weights_stride_y);
+    w2 = VLOAD(VEC_SIZE)(0, weights.ptr + 2 * weights_stride_y);
+
+#if INPUT_OFFSET != 0
+    sum_we += CONVERT(w0, VEC_INT) + CONVERT(w1, VEC_INT) + CONVERT(w2, VEC_INT);
+#endif /* INPUT_OFFSET != 0 */
+
+    VEC_UCHAR values = VLOAD(VEC_SIZE)(0, src_addr0);
+    BIFROST_MAD_ACC_4(acc0, sum0, values, w0);
+
+    src_addr0 += src_stride_y;
+    values = VLOAD(VEC_SIZE)(0, src_addr0);
+    BIFROST_MAD_ACC_4(acc0, sum0, values, w1);
+
+    src_addr0 += src_stride_y;
+    values = VLOAD(VEC_SIZE)(0, src_addr0);
+    BIFROST_MAD_ACC_4(acc0, sum0, values, w2);
+    BIFROST_MAD_ACC_4(acc2, sum2, values, w0);
+
+    src_addr0 += src_stride_y;
+    values = VLOAD(VEC_SIZE)(0, src_addr0);
+    BIFROST_MAD_ACC_4(acc2, sum2, values, w1);
+
+    src_addr0 += src_stride_y;
+    values = VLOAD(VEC_SIZE)(0, src_addr0);
+    BIFROST_MAD_ACC_4(acc2, sum2, values, w2);
+
+    weights.ptr += weights_stride_z;
+
+    // z == 1
+    w0 = VLOAD(VEC_SIZE)(0, weights.ptr + 0 * weights_stride_y);
+    w1 = VLOAD(VEC_SIZE)(0, weights.ptr + 1 * weights_stride_y);
+    w2 = VLOAD(VEC_SIZE)(0, weights.ptr + 2 * weights_stride_y);
+
+#if INPUT_OFFSET != 0
+    sum_we += CONVERT(w0, VEC_INT) + CONVERT(w1, VEC_INT) + CONVERT(w2, VEC_INT);
+#endif /* INPUT_OFFSET != 0 */
+
+    values = VLOAD(VEC_SIZE)(0, src_addr1);
+    BIFROST_MAD_ACC_4(acc0, sum0, values, w0);
+
+    src_addr1 += src_stride_y;
+    values = VLOAD(VEC_SIZE)(0, src_addr1);
+    BIFROST_MAD_ACC_4(acc0, sum0, values, w1);
+
+    src_addr1 += src_stride_y;
+    values = VLOAD(VEC_SIZE)(0, src_addr1);
+    BIFROST_MAD_ACC_4(acc0, sum0, values, w2);
+    BIFROST_MAD_ACC_4(acc2, sum2, values, w0);
+
+    src_addr1 += src_stride_y;
+    values = VLOAD(VEC_SIZE)(0, src_addr1);
+    BIFROST_MAD_ACC_4(acc2, sum2, values, w1);
+
+    src_addr1 += src_stride_y;
+    values = VLOAD(VEC_SIZE)(0, src_addr1);
+    BIFROST_MAD_ACC_4(acc2, sum2, values, w2);
+
+    weights.ptr += weights_stride_z;
+
+    // z == 2
+    w0 = VLOAD(VEC_SIZE)(0, weights.ptr + 0 * weights_stride_y);
+    w1 = VLOAD(VEC_SIZE)(0, weights.ptr + 1 * weights_stride_y);
+    w2 = VLOAD(VEC_SIZE)(0, weights.ptr + 2 * weights_stride_y);
+
+#if INPUT_OFFSET != 0
+    sum_we += CONVERT(w0, VEC_INT) + CONVERT(w1, VEC_INT) + CONVERT(w2, VEC_INT);
+#endif /* INPUT_OFFSET != 0 */
+
+    values = VLOAD(VEC_SIZE)(0, src_addr2);
+    BIFROST_MAD_ACC_4(acc0, sum0, values, w0);
+
+    src_addr2 += src_stride_y;
+    values = VLOAD(VEC_SIZE)(0, src_addr2);
+    BIFROST_MAD_ACC_4(acc0, sum0, values, w1);
+
+    src_addr2 += src_stride_y;
+    values = VLOAD(VEC_SIZE)(0, src_addr2);
+    BIFROST_MAD_ACC_4(acc0, sum0, values, w2);
+    BIFROST_MAD_ACC_4(acc2, sum2, values, w0);
+
+    src_addr2 += src_stride_y;
+    values = VLOAD(VEC_SIZE)(0, src_addr2);
+    BIFROST_MAD_ACC_4(acc2, sum2, values, w1);
+
+    src_addr2 += src_stride_y;
+    values = VLOAD(VEC_SIZE)(0, src_addr2);
+    BIFROST_MAD_ACC_4(acc2, sum2, values, w2);
+
+#if defined(HAS_BIAS)
+    acc0 += bias_values;
+    acc2 += bias_values;
+#endif /* defined(HAS_BIAS) */
+
+#if WEIGHTS_OFFSET != 0
+    acc0 += WEIGHTS_OFFSET * sum0;
+    acc2 += WEIGHTS_OFFSET * sum2;
+#endif /* WEIGHTS_OFFSET != 0 */
+
+#if INPUT_OFFSET != 0
+    VEC_INT offs = INPUT_OFFSET * sum_we;
+
+    acc0 += offs;
+    acc2 += offs;
+#endif /* INPUT_OFFSET != 0 */
+
+#if K_OFFSET != 0
+    acc0 += (VEC_INT)K_OFFSET;
+    acc2 += (VEC_INT)K_OFFSET;
+#endif /* K_OFFSET != 0 */
+
+    acc0 = asymm_mult_by_quant_multiplier_less_than_one(acc0, OUTPUT_MULTIPLIER, OUTPUT_SHIFT);
+    acc2 = asymm_mult_by_quant_multiplier_less_than_one(acc2, OUTPUT_MULTIPLIER, OUTPUT_SHIFT);
+    acc0 += (VEC_INT)OUTPUT_OFFSET;
+    acc2 += (VEC_INT)OUTPUT_OFFSET;
+    VEC_UCHAR res0 = CONVERT_SAT(acc0, VEC_UCHAR);
+    VEC_UCHAR res2 = CONVERT_SAT(acc2, VEC_UCHAR);
+    res0           = CLAMP(res0, (VEC_UCHAR)0, (VEC_UCHAR)255);
+    res2           = CLAMP(res2, (VEC_UCHAR)0, (VEC_UCHAR)255);
+
+    VSTORE(VEC_SIZE)
+    (res0, 0, dst.ptr + 0 * dst_stride_y);
+    VSTORE(VEC_SIZE)
+    (res2, 0, dst.ptr + 1 * dst_stride_y);
+}
+
+#endif /* defined(VEC_SIZE) && defined(SRC_DEPTH) && defined(CONV_PAD_TOP) && defined(ROWS_READ) */
+
+#endif /* defined(WEIGHTS_OFFSET) && defined(INPUT_OFFSET) && defined(K_OFFSET) && defined(OUTPUT_OFFSET) && defined(OUTPUT_MULTIPLIER) && defined(OUTPUT_SHIFT) */
diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3Kernel.cpp b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp
index c9fe1cfc0b..de68ceda11 100644
--- a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3Kernel.cpp
+++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.cpp
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2018 ARM Limited.
+ * Copyright (c) 2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -21,7 +21,7 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  * SOFTWARE.
  */
-#include "arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3Kernel.h"
+#include "arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NCHWKernel.h"
 
 #include "arm_compute/core/AccessWindowStatic.h"
 #include "arm_compute/core/CL/CLHelpers.h"
@@ -39,18 +39,18 @@
 using namespace arm_compute;
 using namespace arm_compute::misc::shape_calculator;
 
-CLDepthwiseConvolutionLayer3x3Kernel::CLDepthwiseConvolutionLayer3x3Kernel()
-    : _border_size(0), _input(), _output(), _weights(), _biases(), _conv_stride_x(0), _conv_stride_y(0), _conv_pad_left(0), _conv_pad_top(0)
+CLDepthwiseConvolutionLayer3x3NCHWKernel::CLDepthwiseConvolutionLayer3x3NCHWKernel()
+    : _conv_stride_x(0), _conv_pad_top(0)
 {
 }
 
-BorderSize CLDepthwiseConvolutionLayer3x3Kernel::border_size() const
+BorderSize CLDepthwiseConvolutionLayer3x3NCHWKernel::border_size() const
 {
     return _border_size;
 }
 
-void CLDepthwiseConvolutionLayer3x3Kernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info,
-                                                     ActivationLayerInfo act_info)
+void CLDepthwiseConvolutionLayer3x3NCHWKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info,
+                                                         ActivationLayerInfo act_info)
 {
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
@@ -144,8 +144,6 @@ void CLDepthwiseConvolutionLayer3x3Kernel::configure(const ICLTensor *input, con
         }
     }
 
-    // Configure the local work size for Bifrost with a value obtained
-    // via exhaustive autotuning for the MobileNets tensor shapes.
     const GPUTarget gpu_target = get_target();
     const bool      is_bifrost = gpu_target_is_in(gpu_target, GPUTarget::G71, GPUTarget::G72, GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT, GPUTarget::TNOX);
 
@@ -228,7 +226,7 @@ void CLDepthwiseConvolutionLayer3x3Kernel::configure(const ICLTensor *input, con
     }
     else
     {
-        kernel_name                       = is_qasymm ? "depthwise_convolution_3x3_quantized" : "depthwise_convolution_3x3";
+        kernel_name                       = is_qasymm ? "depthwise_convolution_3x3_quantized_nchw" : "depthwise_convolution_3x3";
         num_elems_written_per_iteration_x = 8 / data_size_from_type(input->info()->data_type());
         num_elems_written_per_iteration_y = (is_qasymm && _conv_stride_y < 3) ? (2 / _conv_stride_y) : 1;
         num_elems_read_per_iteration_x    = 3 + (num_elems_written_per_iteration_x - 1) * _conv_stride_x;
@@ -268,7 +266,7 @@ void CLDepthwiseConvolutionLayer3x3Kernel::configure(const ICLTensor *input, con
     _config_id += support::cpp11::to_string(output->info()->dimension(1));
 }
 
-void CLDepthwiseConvolutionLayer3x3Kernel::run(const Window &window, cl::CommandQueue &queue)
+void CLDepthwiseConvolutionLayer3x3NCHWKernel::run(const Window &window, cl::CommandQueue &queue)
 {
     ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
     ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
diff --git a/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp
new file mode 100644
index 0000000000..d783b9e159
--- /dev/null
+++ b/src/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.cpp
@@ -0,0 +1,254 @@
+/*
+ * Copyright (c) 2018 ARM Limited.
+ *
+ * SPDX-License-Identifier: MIT
+ *
+ * Permission is hereby granted, free of charge, to any person obtaining a copy
+ * of this software and associated documentation files (the "Software"), to
+ * deal in the Software without restriction, including without limitation the
+ * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
+ * sell copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in all
+ * copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+ * SOFTWARE.
+ */
+#include "arm_compute/core/CL/kernels/CLDepthwiseConvolutionLayer3x3NHWCKernel.h"
+
+#include "arm_compute/core/AccessWindowStatic.h"
+#include "arm_compute/core/CL/CLHelpers.h"
+#include "arm_compute/core/CL/CLKernelLibrary.h"
+#include "arm_compute/core/CL/ICLKernel.h"
+#include "arm_compute/core/CL/ICLTensor.h"
+#include "arm_compute/core/Error.h"
+#include "arm_compute/core/Helpers.h"
+#include "arm_compute/core/TensorInfo.h"
+#include "arm_compute/core/Types.h"
+#include "arm_compute/core/Utils.h"
+#include "arm_compute/core/utils/misc/ShapeCalculator.h"
+#include "arm_compute/core/utils/quantization/AsymmHelpers.h"
+
+using namespace arm_compute;
+using namespace arm_compute::misc::shape_calculator;
+
+namespace
+{
+Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
+                          const ActivationLayerInfo &act_info)
+{
+    ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8);
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG((act_info.enabled()) && (act_info.activation() != ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU)
+                                    && (act_info.activation() != ActivationLayerInfo::ActivationFunction::BOUNDED_RELU)
+                                    && (act_info.activation() != ActivationLayerInfo::ActivationFunction::RELU),
+                                    "For QASYMM8 only relu, lower bounded relu and lower-upper bounded relu are supported");
+    ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
+    ARM_COMPUTE_RETURN_ERROR_ON(weights->dimension(1) != 3 || weights->dimension(2) != 3);
+
+    if(biases != nullptr)
+    {
+        ARM_COMPUTE_RETURN_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(biases, 1, DataType::S32);
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->dimension(0) != weights->dimension(0));
+        ARM_COMPUTE_RETURN_ERROR_ON(biases->num_dimensions() > 1);
+    }
+
+    if(output->total_size() != 0)
+    {
+        const TensorShape output_shape = compute_depthwise_convolution_shape(*input, *weights, conv_info);
+        ARM_COMPUTE_RETURN_ERROR_ON_MISMATCHING_DIMENSIONS(output->tensor_shape(), output_shape);
+    }
+
+    return Status{};
+}
+
+std::pair<Status, Window> validate_and_configure_window(ITensorInfo *input, ITensorInfo *weights, ITensorInfo *output, const PadStrideInfo &conv_info)
+{
+    const unsigned int num_rows_processed_per_iteration = 4;
+    const unsigned int num_elems_accessed_per_iteration = 4;
+    const unsigned int num_rows_read_per_iteration      = num_rows_processed_per_iteration + 2;
+    const unsigned int num_rows_written_per_iteration   = num_rows_processed_per_iteration / conv_info.stride().first;
+
+    const BorderSize border_size(conv_info.pad_left() + num_rows_read_per_iteration * std::max(conv_info.pad_top(), conv_info.pad_bottom()), 0, conv_info.pad_right(), 0);
+
+    // Configure kernel window
+    Window win = calculate_max_window(*output, Steps(num_elems_accessed_per_iteration, num_rows_written_per_iteration));
+
+    AccessWindowStatic input_access(input, 0, -border_size.top, ceil_to_multiple(input->dimension(0), num_elems_accessed_per_iteration),
+                                    ceil_to_multiple(input->dimension(1) + border_size.bottom, num_rows_read_per_iteration));
+    AccessWindowRectangle  output_access(output, 0, 0, num_elems_accessed_per_iteration, num_rows_written_per_iteration);
+    AccessWindowHorizontal weights_access(weights, 0, num_elems_accessed_per_iteration);
+
+    bool window_changed = update_window_and_padding(win, input_access, weights_access, output_access);
+
+    output_access.set_valid_region(win, ValidRegion(Coordinates(), output->tensor_shape()));
+
+    Status err = (window_changed) ? ARM_COMPUTE_CREATE_ERROR(ErrorCode::RUNTIME_ERROR, "Insufficient Padding!") : Status{};
+    return std::make_pair(err, win);
+}
+} // namespace
+
+CLDepthwiseConvolutionLayer3x3NHWCKernel::CLDepthwiseConvolutionLayer3x3NHWCKernel()
+    : _num_rows_processed_per_iteration(1)
+{
+}
+
+BorderSize CLDepthwiseConvolutionLayer3x3NHWCKernel::border_size() const
+{
+    return _border_size;
+}
+
+void CLDepthwiseConvolutionLayer3x3NHWCKernel::configure(const ICLTensor *input, const ICLTensor *weights, const ICLTensor *biases, ICLTensor *output, const PadStrideInfo &conv_info,
+                                                         ActivationLayerInfo act_info)
+{
+    ARM_COMPUTE_ERROR_ON_NULLPTR(input, weights, output);
+
+    // Get convolved dimensions
+    const TensorShape output_shape = compute_depthwise_convolution_shape(*input->info(), *weights->info(), conv_info);
+
+    // Output auto inizialitation if not yet initialized
+    auto_init_if_empty(*output->info(),
+                       output_shape,
+                       1,
+                       input->info()->data_type(),
+                       input->info()->fixed_point_position(),
+                       input->info()->quantization_info());
+
+    ARM_COMPUTE_ERROR_THROW_ON(validate_arguments(input->info(), weights->info(), (biases != nullptr) ? biases->info() : nullptr, output->info(), conv_info, act_info));
+
+    const unsigned int conv_stride_x = conv_info.stride().first;
+    ARM_COMPUTE_ERROR_ON(conv_stride_x < 1 || conv_stride_x > 2);
+    ARM_COMPUTE_ERROR_ON(std::max(conv_info.pad_top(), conv_info.pad_bottom()) > 1);
+
+    _input                            = input;
+    _output                           = output;
+    _weights                          = weights;
+    _biases                           = biases;
+    _conv_stride_y                    = conv_info.stride().second;
+    _conv_pad_left                    = conv_info.pad_left();
+    _num_rows_processed_per_iteration = 4;
+
+    const unsigned int num_elems_accessed_per_iteration = 4;
+    const unsigned int num_rows_read_per_iteration      = _num_rows_processed_per_iteration + 2;
+
+    _border_size = BorderSize(_conv_pad_left + num_rows_read_per_iteration * std::max(conv_info.pad_top(), conv_info.pad_bottom()), 0, conv_info.pad_right(), 0);
+
+    float multiplier        = _input->info()->quantization_info().scale * _weights->info()->quantization_info().scale / _output->info()->quantization_info().scale;
+    int   output_multiplier = 0;
+    int   output_shift      = 0;
+    quantization::calculate_quantized_multiplier_less_than_one(multiplier, &output_multiplier, &output_shift);
+
+    CLBuildOptions build_opts;
+    build_opts.add_option_if(_biases != nullptr, "-DHAS_BIAS");
+    build_opts.add_option("-DINPUT_OFFSET=" + support::cpp11::to_string(-_input->info()->quantization_info().offset));
+    build_opts.add_option("-DWEIGHTS_OFFSET=" + support::cpp11::to_string(-_weights->info()->quantization_info().offset));
+    build_opts.add_option("-DOUTPUT_OFFSET=" + support::cpp11::to_string(_output->info()->quantization_info().offset));
+    build_opts.add_option("-DK_OFFSET=" + support::cpp11::to_string(9 * input->info()->quantization_info().offset * weights->info()->quantization_info().offset));
+    build_opts.add_option("-DOUTPUT_MULTIPLIER=" + support::cpp11::to_string(output_multiplier));
+    build_opts.add_option("-DOUTPUT_SHIFT=" + support::cpp11::to_string(output_shift));
+    build_opts.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_accessed_per_iteration));
+    build_opts.add_option("-DSRC_DEPTH=" + support::cpp11::to_string(_input->info()->dimension(2)));
+    build_opts.add_option("-DCONV_PAD_TOP=" + support::cpp11::to_string(conv_info.pad_top()));
+    build_opts.add_option("-DROWS_READ=" + support::cpp11::to_string(num_rows_read_per_iteration));
+
+    if(act_info.enabled())
+    {
+        const int a_val = input->info()->quantization_info().quantize(act_info.a(), RoundingPolicy::TO_NEAREST_UP);
+        const int b_val = input->info()->quantization_info().quantize(act_info.b(), RoundingPolicy::TO_NEAREST_UP);
+        const int o1    = input->info()->quantization_info().offset;
+
+        build_opts.add_option("-DFUSED_ACTIVATION=" + lower_string(string_from_activation_func(act_info.activation())));
+        build_opts.add_option("-DA_VAL=" + support::cpp11::to_string(a_val));
+        build_opts.add_option("-DB_VAL=" + support::cpp11::to_string(b_val));
+        build_opts.add_option("-DCONST_0=" + support::cpp11::to_string(o1));
+
+        if(output != nullptr)
+        {
+            const float s1 = input->info()->quantization_info().scale;
+            const float s2 = output->info()->quantization_info().scale;
+            const int   o2 = output->info()->quantization_info().offset;
+
+            if(o1 != o2 || s1 != s2)
+            {
+                build_opts.add_option("-DS1_VAL=" + float_to_string_with_full_precision(s1));
+                build_opts.add_option("-DS2_VAL=" + float_to_string_with_full_precision(s2));
+                build_opts.add_option("-DO1_VAL=" + support::cpp11::to_string(o1));
+                build_opts.add_option("-DO2_VAL=" + support::cpp11::to_string(o2));
+            }
+        }
+    }
+
+    // Create kernel
+    std::string kernel_name = std::string("depthwise_convolution_3x3_quantized_nhwc_stride") + support::cpp11::to_string(conv_stride_x);
+    _kernel                 = static_cast<cl::Kernel>(CLKernelLibrary::get().create_kernel(kernel_name, build_opts.options()));
+
+    // Configure kernel window
+    auto win_config = validate_and_configure_window(input->info(), weights->info(), output->info(), conv_info);
+    ARM_COMPUTE_ERROR_THROW_ON(win_config.first);
+    ICLKernel::configure(win_config.second);
+
+    // Set config_id for enabling LWS tuning
+    _config_id = kernel_name;
+    _config_id += "_";
+    _config_id += support::cpp11::to_string(input->info()->dimension(0));
+    _config_id += "_";
+    _config_id += support::cpp11::to_string(input->info()->dimension(1));
+    _config_id += "_";
+    _config_id += support::cpp11::to_string(input->info()->dimension(2));
+    _config_id += "_";
+    _config_id += support::cpp11::to_string(output->info()->dimension(0));
+    _config_id += "_";
+    _config_id += support::cpp11::to_string(output->info()->dimension(1));
+}
+
+Status CLDepthwiseConvolutionLayer3x3NHWCKernel::validate(const ITensorInfo *input, const ITensorInfo *weights, const ITensorInfo *biases, const ITensorInfo *output, const PadStrideInfo &conv_info,
+                                                          ActivationLayerInfo act_info)
+{
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_arguments(input, weights, biases, output, conv_info, act_info));
+    ARM_COMPUTE_RETURN_ON_ERROR(validate_and_configure_window(input->clone().get(), weights->clone().get(), output->clone().get(), conv_info).first);
+
+    return Status{};
+}
+
+void CLDepthwiseConvolutionLayer3x3NHWCKernel::run(const Window &window, cl::CommandQueue &queue)
+{
+    ARM_COMPUTE_ERROR_ON_UNCONFIGURED_KERNEL(this);
+    ARM_COMPUTE_ERROR_ON_INVALID_SUBWINDOW(IKernel::window(), window);
+
+    // Create input window and adjust
+    Window win_in = window;
+    win_in.adjust(Window::DimY, -_conv_pad_left, true);
+    win_in.set_dimension_step(Window::DimY, _num_rows_processed_per_iteration);
+    win_in.set_dimension_step(Window::DimZ, _conv_stride_y);
+
+    ARM_COMPUTE_ERROR_ON((win_in.y().step() < window.y().step()) || (win_in.z().step() < window.z().step()));
+
+    Window slice_in  = win_in.first_slice_window_3D();
+    Window slice_out = window.first_slice_window_3D();
+
+    if(_biases != nullptr)
+    {
+        unsigned int idx = 3 * num_arguments_per_3D_tensor();
+        Window       win_biases;
+        win_biases.use_tensor_dimensions(_biases->info()->tensor_shape());
+        win_biases.set_dimension_step(Window::DimX, window.x().step());
+        add_1D_tensor_argument(idx, _biases, win_biases);
+    }
+
+    do
+    {
+        unsigned int idx = 0;
+        add_3D_tensor_argument(idx, _input, slice_in);
+        add_3D_tensor_argument(idx, _output, slice_out);
+        add_3D_tensor_argument(idx, _weights, slice_out);
+
+        enqueue(queue, *this, slice_out, _lws_hint);
+    }
+    while(window.slide_window_slice_3D(slice_out) && win_in.slide_window_slice_3D(slice_in));
+}
diff --git a/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp b/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
index 8d7c92bdf1..0276b37e09 100644
--- a/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
+++ b/src/runtime/CL/functions/CLDepthwiseConvolutionLayer.cpp
@@ -35,7 +35,7 @@ using namespace arm_compute::misc;
 using namespace arm_compute::misc::shape_calculator;
 
 CLDepthwiseConvolutionLayer3x3::CLDepthwiseConvolutionLayer3x3()
-    : _kernel(), _border_handler()
+    : _kernel(nullptr), _border_handler()
 {
 }
 
@@ -44,8 +44,17 @@ void CLDepthwiseConvolutionLayer3x3::configure(ICLTensor *input, const ICLTensor
     ARM_COMPUTE_ERROR_ON_DATA_TYPE_CHANNEL_NOT_IN(input, 1, DataType::QASYMM8, DataType::F16, DataType::F32);
     ARM_COMPUTE_ERROR_ON_MISMATCHING_DATA_TYPES(input, weights);
 
-    _kernel.set_target(CLScheduler::get().target());
-    _kernel.configure(input, weights, biases, output, conv_info, act_info);
+    if(input->info()->data_layout() == DataLayout::NCHW)
+    {
+        _kernel = arm_compute::support::cpp14::make_unique<CLDepthwiseConvolutionLayer3x3NCHWKernel>();
+    }
+    else
+    {
+        _kernel = arm_compute::support::cpp14::make_unique<CLDepthwiseConvolutionLayer3x3NHWCKernel>();
+    }
+
+    _kernel->set_target(CLScheduler::get().target());
+    _kernel->configure(input, weights, biases, output, conv_info, act_info);
 
     // Configure border handler
     PixelValue &&zero_value(0.f);
@@ -53,13 +62,13 @@ void CLDepthwiseConvolutionLayer3x3::configure(ICLTensor *input, const ICLTensor
     {
         zero_value = PixelValue(static_cast<uint8_t>(input->info()->quantization_info().offset));
     }
-    _border_handler.configure(input, _kernel.border_size(), BorderMode::CONSTANT, zero_value);
+    _border_handler.configure(input, _kernel->border_size(), BorderMode::CONSTANT, zero_value);
 }
 
 void CLDepthwiseConvolutionLayer3x3::run()
 {
     CLScheduler::get().enqueue(_border_handler);
-    CLScheduler::get().enqueue(_kernel);
+    CLScheduler::get().enqueue(*_kernel);
 }
 
 CLDepthwiseConvolutionLayer::CLDepthwiseConvolutionLayer()