COMPMID-2184: Implement direct convolution 9x9 (NHWC) on OpenCL

Change-Id: I8aa929e7e72d2d1ccee07ee2ed9618c15084ae9d
Signed-off-by: giuros01 <giuseppe.rossini@arm.com>
Reviewed-on: https://review.mlplatform.org/c/1274
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Michele Di Giorgio <michele.digiorgio@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Georgios Pinitas <georgios.pinitas@arm.com>

5 files changed, 546 insertions, 19 deletions

diff --git a/arm_compute/core/CL/kernels/CLDirectConvolutionLayerKernel.h b/arm_compute/core/CL/kernels/CLDirectConvolutionLayerKernel.h
index bd37e35334..081b01aad3 100644
--- a/arm_compute/core/CL/kernels/CLDirectConvolutionLayerKernel.h
+++ b/arm_compute/core/CL/kernels/CLDirectConvolutionLayerKernel.h
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2017-2018 ARM Limited.
+ * Copyright (c) 2017-2019 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -54,6 +54,7 @@ public:
      *        1x1 convolution with stride_x = 1/2/3, stride_y = 1/2/3
      *        3x3 convolution with stride_x = 1/2, stride_y = 1/2
      *        5x5 convolution with stride_x = 1/2, stride_y = 1/2
+     *        9x9 convolution with stride_x = 1/2, stride_y = 1/2, data_layout=NHWC
      *
      * @param[in]  input     The input tensor to convolve. 3 lower dimensions represent a single input [width, height, IFM],
      *                       while every optional dimension from 4 and above represent a batch of inputs. Data types supported: QASYMM8/F16/F32.
diff --git a/src/core/CL/CLKernelLibrary.cpp b/src/core/CL/CLKernelLibrary.cpp
index 253da40077..c0875bebcd 100644
--- a/src/core/CL/CLKernelLibrary.cpp
+++ b/src/core/CL/CLKernelLibrary.cpp
@@ -248,6 +248,7 @@ const std::map<std::string, std::string> CLKernelLibrary::_kernel_program_map =
     { "direct_convolution5x5_nhwc", "direct_convolution5x5.cl" },
     { "direct_convolution5x5_f32_bifrost", "direct_convolution5x5.cl" },
     { "direct_convolution_1x1_3x3_5x5_quantized", "direct_convolution_1x1_3x3_5x5_quantized.cl" },
+    { "direct_convolution9x9_nhwc", "direct_convolution9x9.cl" },
     { "elementwise_operation_ADD", "elementwise_operation.cl" },
     { "elementwise_operation_SUB", "elementwise_operation.cl" },
     { "elementwise_operation_MAX", "elementwise_operation.cl" },
@@ -711,6 +712,10 @@ const std::map<std::string, std::string> CLKernelLibrary::_program_source_map =
 #include "./cl_kernels/direct_convolution_1x1_3x3_5x5_quantized.clembed"
     },
     {
+        "direct_convolution9x9.cl",
+#include "./cl_kernels/direct_convolution9x9.clembed"
+    },
+    {
         "elementwise_operation.cl",
 #include "./cl_kernels/elementwise_operation.clembed"
     },
diff --git a/src/core/CL/cl_kernels/direct_convolution9x9.cl b/src/core/CL/cl_kernels/direct_convolution9x9.cl
new file mode 100644
index 0000000000..22f437f78e
--- /dev/null
+++ b/src/core/CL/cl_kernels/direct_convolution9x9.cl
@@ -0,0 +1,444 @@
+/*
+ * Copyright (c) 2019 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 "helpers.h"
+
+#undef CONVERT_SAT
+
+#if defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH) && defined(DATA_LAYOUT_NHWC)
+
+#define PTR_TO_VALUE(PTR, DATA_TYPE) *((__global DATA_TYPE *)(PTR))
+
+#define CONVOLUTION1x9_STRIDE1_NHWC(acc, row_ptr, weights_ptr)                                                                         \
+    ({                                                                                                                                 \
+        VEC_DATA_TYPE(DATA_TYPE, 8)                                                                                                    \
+        src0 = (VEC_DATA_TYPE(DATA_TYPE, 8))(                                                                                          \
+                PTR_TO_VALUE(row_ptr + 0 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 1 * src_stride_y, DATA_TYPE),                  \
+                PTR_TO_VALUE(row_ptr + 2 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 3 * src_stride_y, DATA_TYPE),                  \
+                PTR_TO_VALUE(row_ptr + 4 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 5 * src_stride_y, DATA_TYPE),                  \
+                PTR_TO_VALUE(row_ptr + 6 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 7 * src_stride_y, DATA_TYPE));                 \
+        VEC_DATA_TYPE(DATA_TYPE, 8)                                                                                                    \
+        src1 = (VEC_DATA_TYPE(DATA_TYPE, 8))(                                                                                          \
+                PTR_TO_VALUE(row_ptr + 8 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 9 * src_stride_y, DATA_TYPE),                  \
+                PTR_TO_VALUE(row_ptr + 10 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 11 * src_stride_y, DATA_TYPE),                \
+                PTR_TO_VALUE(row_ptr + 12 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 13 * src_stride_y, DATA_TYPE),                \
+                PTR_TO_VALUE(row_ptr + 14 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 15 * src_stride_y, DATA_TYPE));               \
+        VEC_DATA_TYPE(DATA_TYPE, 8)                                                                                                    \
+        weights_values0 = (VEC_DATA_TYPE(DATA_TYPE, 8))(                                                                               \
+                          PTR_TO_VALUE(weights_ptr + 0 * weights_stride_y, DATA_TYPE), PTR_TO_VALUE(weights_ptr + 1 * weights_stride_y, DATA_TYPE),  \
+                          PTR_TO_VALUE(weights_ptr + 2 * weights_stride_y, DATA_TYPE), PTR_TO_VALUE(weights_ptr + 3 * weights_stride_y, DATA_TYPE),  \
+                          PTR_TO_VALUE(weights_ptr + 4 * weights_stride_y, DATA_TYPE), PTR_TO_VALUE(weights_ptr + 5 * weights_stride_y, DATA_TYPE),  \
+                          PTR_TO_VALUE(weights_ptr + 6 * weights_stride_y, DATA_TYPE), PTR_TO_VALUE(weights_ptr + 7 * weights_stride_y, DATA_TYPE)); \
+        DATA_TYPE weights_value1 = PTR_TO_VALUE(weights_ptr + 8 * weights_stride_y, DATA_TYPE);                                        \
+        acc += src0 * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s0;                                                                 \
+        acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1234, src0.s567, src1.s0) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s1;        \
+        acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s234, src0.s567, src1.s01) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s2;        \
+        acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s345, src0.s67, src1.s012) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s3;        \
+        acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s4567, src1.s0123) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s4;                \
+        acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s567, src1.s0123, src1.s4) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s5;        \
+        acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s67, src1.s012, src1.s345) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s6;        \
+        acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s7, src1.s0123, src1.s456) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s7;        \
+        acc += src1 * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_value1;                                                                     \
+    })
+
+#define CONVOLUTION1x9_STRIDE2_NHWC(acc, row_ptr, weights_ptr)                                                                         \
+    ({                                                                                                                                 \
+        VEC_DATA_TYPE(DATA_TYPE, 16)                                                                                                   \
+        src0 = (VEC_DATA_TYPE(DATA_TYPE, 16))(                                                                                         \
+                PTR_TO_VALUE(row_ptr + 0 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 1 * src_stride_y, DATA_TYPE),                  \
+                PTR_TO_VALUE(row_ptr + 2 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 3 * src_stride_y, DATA_TYPE),                  \
+                PTR_TO_VALUE(row_ptr + 4 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 5 * src_stride_y, DATA_TYPE),                  \
+                PTR_TO_VALUE(row_ptr + 6 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 7 * src_stride_y, DATA_TYPE),                  \
+                PTR_TO_VALUE(row_ptr + 8 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 9 * src_stride_y, DATA_TYPE),                  \
+                PTR_TO_VALUE(row_ptr + 10 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 11 * src_stride_y, DATA_TYPE),                \
+                PTR_TO_VALUE(row_ptr + 12 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 13 * src_stride_y, DATA_TYPE),                \
+                PTR_TO_VALUE(row_ptr + 14 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 15 * src_stride_y, DATA_TYPE));               \
+        VEC_DATA_TYPE(DATA_TYPE, 8)                                                                                                    \
+        src1 = (VEC_DATA_TYPE(DATA_TYPE, 8))(                                                                                          \
+                PTR_TO_VALUE(row_ptr + 16 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 17 * src_stride_y, DATA_TYPE),                \
+                PTR_TO_VALUE(row_ptr + 18 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 19 * src_stride_y, DATA_TYPE),                \
+                PTR_TO_VALUE(row_ptr + 20 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 21 * src_stride_y, DATA_TYPE),                \
+                PTR_TO_VALUE(row_ptr + 22 * src_stride_y, DATA_TYPE), PTR_TO_VALUE(row_ptr + 23 * src_stride_y, DATA_TYPE));               \
+        VEC_DATA_TYPE(DATA_TYPE, 8)                                                                                                    \
+        weights_values0 = (VEC_DATA_TYPE(DATA_TYPE, 8))(                                                                               \
+                          PTR_TO_VALUE(weights_ptr + 0 * weights_stride_y, DATA_TYPE), PTR_TO_VALUE(weights_ptr + 1 * weights_stride_y, DATA_TYPE),  \
+                          PTR_TO_VALUE(weights_ptr + 2 * weights_stride_y, DATA_TYPE), PTR_TO_VALUE(weights_ptr + 3 * weights_stride_y, DATA_TYPE),  \
+                          PTR_TO_VALUE(weights_ptr + 4 * weights_stride_y, DATA_TYPE), PTR_TO_VALUE(weights_ptr + 5 * weights_stride_y, DATA_TYPE),  \
+                          PTR_TO_VALUE(weights_ptr + 6 * weights_stride_y, DATA_TYPE), PTR_TO_VALUE(weights_ptr + 7 * weights_stride_y, DATA_TYPE)); \
+        DATA_TYPE weights_value1 = PTR_TO_VALUE(weights_ptr + 8 * weights_stride_y, DATA_TYPE);                                        \
+        acc += src0.s02468ACE * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s0;                                                       \
+        acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s1357, src0.s9BDF) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s1;                \
+        acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s2468, src0.sACE, src1.s0) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s2;        \
+        acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s3579, src0.sBDF, src1.s1) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s3;        \
+        acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s468A, src0.sCE, src1.s02) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s4;        \
+        acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s579, src0.sBDF, src1.s13) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s5;        \
+        acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s68A, src0.sCE, src1.s024) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s6;        \
+        acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s79B, src0.sDF, src1.s135) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_values0.s7;        \
+        acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s8AC, src0.sE, src1.s0246) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_value1;            \
+    })
+
+#if defined(VEC_SIZE)
+#define VFMA(acc, w, src0, src1, src2, src3, src4, src5, src6, src7) \
+    ({                                                               \
+        acc##0 = fma(src0, w, acc##0);                               \
+        acc##1 = fma(src1, w, acc##1);                               \
+        acc##2 = fma(src2, w, acc##2);                               \
+        acc##3 = fma(src3, w, acc##3);                               \
+        acc##4 = fma(src4, w, acc##4);                               \
+        acc##5 = fma(src5, w, acc##5);                               \
+        acc##6 = fma(src6, w, acc##6);                               \
+        acc##7 = fma(src7, w, acc##7);                               \
+    })
+
+#define CONVOLUTION1x9_STRIDE1_NHWC_BIFROST(acc, row_ptr, weights_ptr)                       \
+    ({                                                                                       \
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)                                                   \
+        src0 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)row_ptr);                            \
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)                                                   \
+        src1 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(row_ptr + src_stride_y));           \
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)                                                   \
+        src2 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(row_ptr + 2 * src_stride_y));       \
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)                                                   \
+        src3 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(row_ptr + 3 * src_stride_y));       \
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)                                                   \
+        src4 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(row_ptr + 4 * src_stride_y));       \
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)                                                   \
+        src5 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(row_ptr + 5 * src_stride_y));       \
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)                                                   \
+        src6 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(row_ptr + 6 * src_stride_y));       \
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)                                                   \
+        src7 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(row_ptr + 7 * src_stride_y));       \
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)                                                   \
+        src8 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(row_ptr + 8 * src_stride_y));       \
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)                                                   \
+        src9 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(row_ptr + 9 * src_stride_y));       \
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)                                                   \
+        src10 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(row_ptr + 10 * src_stride_y));     \
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)                                                   \
+        src11 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(row_ptr + 11 * src_stride_y));     \
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)                                                   \
+        src12 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(row_ptr + 12 * src_stride_y));     \
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)                                                   \
+        src13 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(row_ptr + 13 * src_stride_y));     \
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)                                                   \
+        src14 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(row_ptr + 14 * src_stride_y));     \
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)                                                   \
+        src15 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(row_ptr + 15 * src_stride_y));     \
+        \
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)                                                   \
+        w0 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights_ptr + 0 * weights_stride_y)); \
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)                                                   \
+        w1 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights_ptr + 1 * weights_stride_y)); \
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)                                                   \
+        w2 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights_ptr + 2 * weights_stride_y)); \
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)                                                   \
+        w3 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights_ptr + 3 * weights_stride_y)); \
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)                                                   \
+        w4 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights_ptr + 4 * weights_stride_y)); \
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)                                                   \
+        w5 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights_ptr + 5 * weights_stride_y)); \
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)                                                   \
+        w6 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights_ptr + 6 * weights_stride_y)); \
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)                                                   \
+        w7 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights_ptr + 7 * weights_stride_y)); \
+        VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)                                                   \
+        w8 = VLOAD(VEC_SIZE)(0, (__global DATA_TYPE *)(weights_ptr + 8 * weights_stride_y)); \
+        \
+        VFMA(acc, w0, src0, src1, src2, src3, src4, src5, src6, src7);                       \
+        VFMA(acc, w1, src1, src2, src3, src4, src5, src6, src7, src8);                       \
+        VFMA(acc, w2, src2, src3, src4, src5, src6, src7, src8, src9);                       \
+        VFMA(acc, w3, src3, src4, src5, src6, src7, src8, src9, src10);                      \
+        VFMA(acc, w4, src4, src5, src6, src7, src8, src9, src10, src11);                     \
+        VFMA(acc, w5, src5, src6, src7, src8, src9, src10, src11, src12);                    \
+        VFMA(acc, w6, src6, src7, src8, src9, src10, src11, src12, src13);                   \
+        VFMA(acc, w7, src7, src8, src9, src10, src11, src12, src13, src14);                  \
+        VFMA(acc, w8, src8, src9, src10, src11, src12, src13, src14, src15);                 \
+    })
+
+#if VEC_SIZE == 4
+#define REDUCE(out, vec)            \
+    ({                              \
+        VEC_DATA_TYPE(DATA_TYPE, 2) \
+        tmp1 = vec.s01 + vec.s23;   \
+        out  = tmp1.s0 + tmp1.s1;   \
+    })
+#else // VEC_SIZE == 4
+#error("Not supported")
+#endif // VEC_SIZE == 4
+
+#if STRIDE_X == 1
+#define CONVOLUTION1x9_NHWC(acc, row_ptr, weights_ptr) CONVOLUTION1x9_STRIDE1_NHWC_BIFROST(acc, row_ptr, weights_ptr)
+#else // STRIDE_X == 1
+#error "Not supported"
+#endif // STRIDE_X == 1
+
+#else // defined(VEC_SIZE)
+
+#if STRIDE_X == 1
+#define CONVOLUTION1x9_NHWC(acc, row_ptr, weights_ptr) CONVOLUTION1x9_STRIDE1_NHWC(acc, row_ptr, weights_ptr)
+#elif STRIDE_X == 2 // STRIDE_X == 1
+#define CONVOLUTION1x9_NHWC(acc, row_ptr, weights_ptr) CONVOLUTION1x9_STRIDE2_NHWC(acc, row_ptr, weights_ptr)
+#else // STRIDE_X == 1
+#error "STRIDE_X larger than 2 is not supported"
+#endif // STRIDE_X == 1
+
+#endif // defined(VEC_SIZE)
+
+//#if defined(VEC_SIZE)
+/** This kernel performs a direct convolution to convolve the low three dimensions in a tensor with the NHWC data layout
+ *
+ * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
+ * @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
+ *
+ * @param[in]  src_ptr                               Pointer to the source tensor. Supported data types: F16/F32
+ * @param[in]  src_stride_x                          Stride of the source tensor in X dimension (in bytes)
+ * @param[in]  src_step_x                            src_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  src_stride_y                          Stride of the source tensor in Y dimension (in bytes)
+ * @param[in]  src_step_y                            src_stride_y * number of elements along Y processed per workitem(in bytes)
+ * @param[in]  src_stride_z                          Stride of the source tensor in Z dimension (in bytes)
+ * @param[in]  src_step_z                            src_stride_z * number of elements along Z processed per workitem(in bytes)
+ * @param[in]  src_offset_first_element_in_bytes     The offset of the first element in the source tensor
+ * @param[out] dst_ptr                               Pointer to the destination tensor. Supported data types: same as @p src_ptr
+ * @param[in]  dst_stride_x                          Stride of the destination tensor in X dimension (in bytes)
+ * @param[in]  dst_step_x                            dst_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  dst_stride_y                          Stride of the destination tensor in Y dimension (in bytes)
+ * @param[in]  dst_step_y                            dst_stride_y * number of elements along 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                            (Optional) Pointer to the biases tensor. Same as @p src_ptr
+ * @param[in]  biases_stride_x                       (Optional) Stride of the biases tensor 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 tensor
+ * @param[in]  weights_stride_w                      (Optional) Stride of the weights tensor in the 4th dimension
+ */
+__kernel void direct_convolution9x9_nhwc(
+    TENSOR3D_DECLARATION(src),
+    TENSOR3D_DECLARATION(dst),
+    TENSOR3D_DECLARATION(weights),
+#ifdef HAS_BIAS
+    VECTOR_DECLARATION(biases),
+#endif /* defined(HAS_BIAS) */
+    unsigned int weights_stride_w)
+{
+    Image    src     = CONVERT_TO_IMAGE_STRUCT(src);
+    Tensor3D weights = CONVERT_TO_TENSOR3D_STRUCT_NO_STEP(weights);
+    Tensor3D dst     = CONVERT_TO_TENSOR3D_STRUCT(dst);
+
+    VEC_DATA_TYPE(DATA_TYPE, 8)
+    values = 0;
+
+#if defined(VEC_SIZE)
+    VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+    values0 = 0;
+    VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+    values1 = 0;
+    VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+    values2 = 0;
+    VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+    values3 = 0;
+    VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+    values4 = 0;
+    VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+    values5 = 0;
+    VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+    values6 = 0;
+    VEC_DATA_TYPE(DATA_TYPE, VEC_SIZE)
+    values7 = 0;
+#define STEP_X (VEC_SIZE)
+#else // defined(VEC_SIZE)
+#define STEP_X (1)
+#endif // defined(VEC_SIZE)
+
+    const int id0 = get_global_id(0);
+    const int id1 = get_global_id(1);
+    const int id2 = get_global_id(2);
+
+    __global uchar *weights_addr = (__global uchar *)tensor3D_offset(&weights, 0, 0, 0);
+    __global uchar *src_addr     = (__global uchar *)offset(&src, 0, 0) + ((id2 * STRIDE_Y) - PAD_TOP) * (int)src_stride_z;
+
+#if(PAD_TOP == 1)
+    const int coordy = id2 - PAD_TOP;
+    for(volatile int d = 0; d < WEIGHTS_DEPTH; d += STEP_X)
+    {
+        if(coordy < 0) // special case Z = -1 doesn't exists
+        {
+            //skip first row and load the two next ones
+            CONVOLUTION1x9_NHWC(values, (src_addr + 1 * (int)src_stride_z), (weights_addr + 1 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 4 * (int)src_stride_z), (weights_addr + 4 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 5 * (int)src_stride_z), (weights_addr + 5 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 6 * (int)src_stride_z), (weights_addr + 6 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 7 * (int)src_stride_z), (weights_addr + 7 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 8 * (int)src_stride_z), (weights_addr + 8 * (int)weights_stride_z));
+        }
+        else if(coordy == (DST_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.
+            CONVOLUTION1x9_NHWC(values, src_addr, weights_addr);
+            CONVOLUTION1x9_NHWC(values, (src_addr + 1 * (int)src_stride_z), (weights_addr + 1 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 4 * (int)src_stride_z), (weights_addr + 4 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 5 * (int)src_stride_z), (weights_addr + 5 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 6 * (int)src_stride_z), (weights_addr + 6 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 7 * (int)src_stride_z), (weights_addr + 7 * (int)weights_stride_z));
+        }
+        else
+        {
+            CONVOLUTION1x9_NHWC(values, src_addr, weights_addr);
+            CONVOLUTION1x9_NHWC(values, (src_addr + 1 * (int)src_stride_z), (weights_addr + 1 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 4 * (int)src_stride_z), (weights_addr + 4 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 5 * (int)src_stride_z), (weights_addr + 5 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 6 * (int)src_stride_z), (weights_addr + 6 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 7 * (int)src_stride_z), (weights_addr + 7 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 8 * (int)src_stride_z), (weights_addr + 8 * (int)weights_stride_z));
+        }
+        src_addr += STEP_X * sizeof(DATA_TYPE);
+        weights_addr += STEP_X * sizeof(DATA_TYPE);
+    }
+#elif(PAD_TOP == 2) // PAD_TOP == 1
+    const int coordy = id2 * STRIDE_Y;
+    for(volatile int d = 0; d < WEIGHTS_DEPTH; d += STEP_X)
+    {
+        if(coordy == 0) // special case Z = -2 doesn't exists
+        {
+            //skip first row and load the two next ones
+            CONVOLUTION1x9_NHWC(values, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 4 * (int)src_stride_z), (weights_addr + 4 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 5 * (int)src_stride_z), (weights_addr + 5 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 6 * (int)src_stride_z), (weights_addr + 6 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 7 * (int)src_stride_z), (weights_addr + 7 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 8 * (int)src_stride_z), (weights_addr + 8 * (int)weights_stride_z));
+        }
+        else if(coordy == 1) // special case Z = -1 doesn't exists
+        {
+            //skip first row and load the two next ones
+            CONVOLUTION1x9_NHWC(values, (src_addr + 1 * (int)src_stride_z), (weights_addr + 1 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 4 * (int)src_stride_z), (weights_addr + 4 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 5 * (int)src_stride_z), (weights_addr + 5 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 6 * (int)src_stride_z), (weights_addr + 6 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 7 * (int)src_stride_z), (weights_addr + 7 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 8 * (int)src_stride_z), (weights_addr + 8 * (int)weights_stride_z));
+        }
+        else if(coordy == (SRC_HEIGHT - 5))
+        {
+            // 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.
+            CONVOLUTION1x9_NHWC(values, src_addr, weights_addr);
+            CONVOLUTION1x9_NHWC(values, (src_addr + 1 * (int)src_stride_z), (weights_addr + 1 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 4 * (int)src_stride_z), (weights_addr + 4 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 5 * (int)src_stride_z), (weights_addr + 5 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 6 * (int)src_stride_z), (weights_addr + 6 * (int)weights_stride_z));
+        }
+        else if(coordy == (SRC_HEIGHT - 6))
+        {
+            // 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.
+            CONVOLUTION1x9_NHWC(values, src_addr, weights_addr);
+            CONVOLUTION1x9_NHWC(values, (src_addr + 1 * (int)src_stride_z), (weights_addr + 1 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 4 * (int)src_stride_z), (weights_addr + 4 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 5 * (int)src_stride_z), (weights_addr + 5 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 6 * (int)src_stride_z), (weights_addr + 6 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 7 * (int)src_stride_z), (weights_addr + 7 * (int)weights_stride_z));
+        }
+        else
+        {
+            CONVOLUTION1x9_NHWC(values, src_addr, weights_addr);
+            CONVOLUTION1x9_NHWC(values, (src_addr + 1 * (int)src_stride_z), (weights_addr + 1 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 4 * (int)src_stride_z), (weights_addr + 4 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 5 * (int)src_stride_z), (weights_addr + 5 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 6 * (int)src_stride_z), (weights_addr + 6 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 7 * (int)src_stride_z), (weights_addr + 7 * (int)weights_stride_z));
+            CONVOLUTION1x9_NHWC(values, (src_addr + 8 * (int)src_stride_z), (weights_addr + 8 * (int)weights_stride_z));
+        }
+        src_addr += STEP_X * sizeof(DATA_TYPE);
+        weights_addr += STEP_X * sizeof(DATA_TYPE);
+    }
+
+#else  // PAD_TOP == 1
+    for(volatile int d = 0; d < WEIGHTS_DEPTH; d += STEP_X)
+    {
+        CONVOLUTION1x9_NHWC(values, src_addr, weights_addr);
+        CONVOLUTION1x9_NHWC(values, (src_addr + 1 * (int)src_stride_z), (weights_addr + 1 * (int)weights_stride_z));
+        CONVOLUTION1x9_NHWC(values, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z));
+        CONVOLUTION1x9_NHWC(values, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z));
+        CONVOLUTION1x9_NHWC(values, (src_addr + 4 * (int)src_stride_z), (weights_addr + 4 * (int)weights_stride_z));
+        CONVOLUTION1x9_NHWC(values, (src_addr + 5 * (int)src_stride_z), (weights_addr + 5 * (int)weights_stride_z));
+        CONVOLUTION1x9_NHWC(values, (src_addr + 6 * (int)src_stride_z), (weights_addr + 6 * (int)weights_stride_z));
+        CONVOLUTION1x9_NHWC(values, (src_addr + 7 * (int)src_stride_z), (weights_addr + 7 * (int)weights_stride_z));
+        CONVOLUTION1x9_NHWC(values, (src_addr + 8 * (int)src_stride_z), (weights_addr + 8 * (int)weights_stride_z));
+        src_addr += STEP_X * sizeof(DATA_TYPE);
+        weights_addr += STEP_X * sizeof(DATA_TYPE);
+    }
+#endif // PAD_TOP == 1
+
+#if defined(VEC_SIZE)
+    REDUCE(values.s0, values0);
+    REDUCE(values.s1, values1);
+    REDUCE(values.s2, values2);
+    REDUCE(values.s3, values3);
+    REDUCE(values.s4, values4);
+    REDUCE(values.s5, values5);
+    REDUCE(values.s6, values6);
+    REDUCE(values.s7, values7);
+#endif // defined(VEC_SIZE)
+
+#if defined(HAS_BIAS)
+    Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);
+    values += (VEC_DATA_TYPE(DATA_TYPE, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, id0)));
+#endif // defined(HAS_BIAS)
+
+    *((__global DATA_TYPE *)(dst.ptr + 0 * dst_stride_y)) = values.s0;
+    *((__global DATA_TYPE *)(dst.ptr + 1 * dst_stride_y)) = values.s1;
+    *((__global DATA_TYPE *)(dst.ptr + 2 * dst_stride_y)) = values.s2;
+    *((__global DATA_TYPE *)(dst.ptr + 3 * dst_stride_y)) = values.s3;
+    *((__global DATA_TYPE *)(dst.ptr + 4 * dst_stride_y)) = values.s4;
+    *((__global DATA_TYPE *)(dst.ptr + 5 * dst_stride_y)) = values.s5;
+    *((__global DATA_TYPE *)(dst.ptr + 6 * dst_stride_y)) = values.s6;
+    *((__global DATA_TYPE *)(dst.ptr + 7 * dst_stride_y)) = values.s7;
+#undef STEP_X
+}
+#endif // defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH) && defined(DATA_LAYOUT_NHWC)
diff --git a/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp b/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp
index 3e158a52ff..b878a2121f 100644
--- a/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp
+++ b/src/core/CL/kernels/CLDirectConvolutionLayerKernel.cpp
@@ -54,14 +54,15 @@ Status validate_arguments(const ITensorInfo *input, const ITensorInfo *weights,
     const int        channel_idx = get_data_layout_dimension_index(data_layout, DataLayoutDimension::CHANNEL);
 
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(width_idx) != weights->dimension(height_idx), "Weights should have same width and height");
-    ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(width_idx) != 1 && weights->dimension(width_idx) != 3 && weights->dimension(width_idx) != 5,
-                                    "Kernel sizes other than 1x1, 3x3 or 5x5 are not supported");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(width_idx) != 1 && weights->dimension(width_idx) != 3 && weights->dimension(width_idx) != 5 && weights->dimension(width_idx) != 9,
+                                    "Kernel sizes other than 1x1, 3x3, 5x5 or 9x9 are not supported");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->dimension(channel_idx) != input->dimension(channel_idx),
                                     "Weights feature map dimension should match the respective input's one");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG(weights->num_dimensions() > 4, "Weights can be at most 4 dimensional");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG((weights->dimension(width_idx) == 1) && std::get<0>(conv_info.stride()) > 3, "Strides larger than 3 not supported for 1x1 convolution.");
     ARM_COMPUTE_RETURN_ERROR_ON_MSG((weights->dimension(width_idx) == 3 || weights->dimension(width_idx) == 5) && std::get<0>(conv_info.stride()) > 2,
                                     "Strides larger than 2 not supported for 3x3 convolution.");
+    ARM_COMPUTE_RETURN_ERROR_ON_MSG((weights->dimension(width_idx) == 9) && data_layout == DataLayout::NCHW, "Only NHWC layout is supported for 9x9 convolution.");
 
     if(biases != nullptr)
     {
@@ -103,6 +104,19 @@ inline bool can_run_optimized_kernel_for_bifrost(GPUTarget gpu_target, unsigned
            && (data_layout == DataLayout::NCHW);
 }
 
+inline bool can_run_optimized_kernel_for_bifrost_nhwc(GPUTarget gpu_target, unsigned int conv_stride_x, unsigned int conv_stride_y, unsigned int kernel_size,
+                                                      DataType data_type, DataLayout data_layout)
+{
+    return gpu_target_is_in(gpu_target,
+                            GPUTarget::G71, GPUTarget::G72, GPUTarget::G76,
+                            GPUTarget::G51, GPUTarget::G51BIG, GPUTarget::G51LIT,
+                            GPUTarget::G52, GPUTarget::G52LIT)
+           && (kernel_size == 9)
+           && (conv_stride_x == 1) && (conv_stride_y == 1)
+           && (data_type == DataType::F32)
+           && (data_layout == DataLayout::NHWC);
+}
+
 inline void setup_num_elems(unsigned int &num_elems_read_per_iteration_x, unsigned int &num_elems_read_per_iteration_y,
                             unsigned int &num_elems_written_per_iteration_x, unsigned int &num_elems_written_per_iteration_y,
                             unsigned int kernel_size, const PadStrideInfo &conv_info, const GPUTarget target, ITensorInfo *input)
@@ -149,7 +163,7 @@ inline void setup_num_elems(unsigned int &num_elems_read_per_iteration_x, unsign
             }
         }
     }
-    else
+    else if(data_layout == DataLayout::NCHW)
     {
         num_elems_read_per_iteration_y    = kernel_size;
         num_elems_written_per_iteration_x = 8;
@@ -215,11 +229,17 @@ inline void setup_num_elems(unsigned int &num_elems_read_per_iteration_x, unsign
                 ARM_COMPUTE_ERROR("Invalid direct convolution size");
         }
     }
-
-    if(data_layout == DataLayout::NHWC)
+    else // data_layout == NHWC
     {
+        const bool run_optimized_bifrost_nhwc = can_run_optimized_kernel_for_bifrost_nhwc(target, conv_stride_x, conv_stride_y, kernel_size, data_type, data_layout);
+
         num_elems_written_per_iteration_x = 1;
-        num_elems_read_per_iteration_x    = 1;
+
+        if(run_optimized_bifrost_nhwc)
+        {
+            num_elems_read_per_iteration_x = 4;
+        }
+
         switch(kernel_size)
         {
             case 1:
@@ -267,6 +287,21 @@ inline void setup_num_elems(unsigned int &num_elems_read_per_iteration_x, unsign
                         ARM_COMPUTE_ERROR("Invalid convolution stride X");
                 }
                 break;
+            case 9:
+                switch(conv_stride_x)
+                {
+                    case 1:
+                        num_elems_read_per_iteration_y    = 16;
+                        num_elems_written_per_iteration_y = 8;
+                        break;
+                    case 2:
+                        num_elems_read_per_iteration_y    = 24;
+                        num_elems_written_per_iteration_y = 8;
+                        break;
+                    default:
+                        ARM_COMPUTE_ERROR("Invalid convolution stride X");
+                }
+                break;
             default:
                 ARM_COMPUTE_ERROR("Not implemented.");
                 break;
@@ -429,6 +464,7 @@ void CLDirectConvolutionLayerKernel::configure(const ICLTensor *input, const ICL
         build_options.add_option(std::string("-DSTRIDE_X=" + support::cpp11::to_string(_conv_stride_x)));
         if(data_layout == DataLayout::NHWC)
         {
+            const bool run_optimized_for_bifrost_nhwc = can_run_optimized_kernel_for_bifrost_nhwc(gpu_target, _conv_stride_x, _conv_stride_y, kernel_size, data_type, data_layout);
             build_options.add_option(std::string("-DDATA_LAYOUT_NHWC=1"));
             build_options.add_option(std::string("-DDST_HEIGHT=" + support::cpp11::to_string(_output->info()->dimension(height_idx))));
             build_options.add_option(std::string("-DDST_WIDTH=" + support::cpp11::to_string(_output->info()->dimension(width_idx))));
@@ -437,6 +473,12 @@ void CLDirectConvolutionLayerKernel::configure(const ICLTensor *input, const ICL
             build_options.add_option(std::string("-DPAD_LEFT=" + support::cpp11::to_string(conv_info.pad_left())));
             build_options.add_option(std::string("-DPAD_TOP=" + support::cpp11::to_string(conv_info.pad_top())));
             build_options.add_option(std::string("-DSTRIDE_Y=" + support::cpp11::to_string(_conv_stride_y)));
+            if(run_optimized_for_bifrost_nhwc)
+            {
+                const unsigned int num_elems_read_per_iteration_x = 4;
+                _border_size.right                                = num_elems_read_per_iteration_x;
+                build_options.add_option("-DVEC_SIZE=" + support::cpp11::to_string(num_elems_read_per_iteration_x));
+            }
         }
         build_options.add_option(std::string("-DDATA_TYPE_PROMOTED=" + get_cl_type_from_data_type(data_type)));
         // Create kernel
diff --git a/tests/validation/CL/DirectConvolutionLayer.cpp b/tests/validation/CL/DirectConvolutionLayer.cpp
index 437d5bac8c..6c46374b54 100644
--- a/tests/validation/CL/DirectConvolutionLayer.cpp
+++ b/tests/validation/CL/DirectConvolutionLayer.cpp
@@ -49,21 +49,28 @@ RelativeTolerance<float>             tolerance_fp32(0.02f);     /**< Tolerance f
 constexpr float                      tolerance_num = 0.07f;     /**< Tolerance number */
 constexpr AbsoluteTolerance<uint8_t> tolerance_qasymm8(1);      /**< Tolerance for quantized tests */
 
-const auto data_strides         = combine(framework::dataset::make("StrideX", 1, 3), framework::dataset::make("StrideY", 1, 3));
-const auto data_strides_small   = combine(framework::dataset::make("StrideX", 1), framework::dataset::make("StrideY", 1));
-const auto data_ksize_one       = combine(framework::dataset::make("PadX", 0, 1), combine(framework::dataset::make("PadY", 0, 1), framework::dataset::make("KernelSize", 1)));
-const auto data_ksize_one_small = combine(framework::dataset::make("PadX", 0), combine(framework::dataset::make("PadY", 0), framework::dataset::make("KernelSize", 1)));
-const auto data_ksize_three     = combine(framework::dataset::make("PadX", 0, 2), combine(framework::dataset::make("PadY", 0, 2), framework::dataset::make("KernelSize", 3)));
-const auto data_ksize_five      = combine(framework::dataset::make("PadX", 0, 3), combine(framework::dataset::make("PadY", 0, 3), framework::dataset::make("KernelSize", 5)));
-const auto data_all_kernels     = concat(concat(data_ksize_one, data_ksize_three), data_ksize_five);
+const auto data_strides          = combine(framework::dataset::make("StrideX", 1, 3), framework::dataset::make("StrideY", 1, 3));
+const auto data_strides_small    = combine(framework::dataset::make("StrideX", 1), framework::dataset::make("StrideY", 1));
+const auto data_ksize_one        = combine(framework::dataset::make("PadX", 0, 1), combine(framework::dataset::make("PadY", 0, 1), framework::dataset::make("KernelSize", 1)));
+const auto data_ksize_one_small  = combine(framework::dataset::make("PadX", 0), combine(framework::dataset::make("PadY", 0), framework::dataset::make("KernelSize", 1)));
+const auto data_ksize_three      = combine(framework::dataset::make("PadX", 0, 2), combine(framework::dataset::make("PadY", 0, 2), framework::dataset::make("KernelSize", 3)));
+const auto data_ksize_five       = combine(framework::dataset::make("PadX", 0, 3), combine(framework::dataset::make("PadY", 0, 3), framework::dataset::make("KernelSize", 5)));
+const auto data_ksize_nine       = combine(framework::dataset::make("PadX", 0, 3), combine(framework::dataset::make("PadY", 0, 3), framework::dataset::make("KernelSize", 9)));
+const auto data_ksize_nine_small = combine(framework::dataset::make("PadX", 0, 1), combine(framework::dataset::make("PadY", 0, 1), framework::dataset::make("KernelSize", 9)));
 
-const auto data       = combine(datasets::SmallDirectConvolutionShapes(), combine(data_strides, data_all_kernels));
-const auto data_small = combine(datasets::SmallDirectConvolutionShapes(), combine(data_strides_small, data_ksize_one_small));
+const auto data_all_kernels = concat(concat(data_ksize_one, data_ksize_three), data_ksize_five);
+
+const auto data          = combine(datasets::SmallDirectConvolutionShapes(), combine(data_strides, data_all_kernels));
+const auto data9x9       = combine(datasets::SmallDirectConvolutionShapes(), combine(data_strides, data_ksize_nine));
+const auto data_small    = combine(datasets::SmallDirectConvolutionShapes(), combine(data_strides_small, data_ksize_one_small));
+const auto data_small9x9 = combine(datasets::SmallDirectConvolutionShapes(), combine(data_strides_small, data_ksize_nine_small));
 
 /** Direct convolution nightly data set. */
-const auto data_nightly = combine(data, framework::dataset::make("NumKernels", { 1, 4 }));
+const auto data_nightly     = combine(data, framework::dataset::make("NumKernels", { 1, 4 }));
+const auto data_nightly_9x9 = combine(data9x9, framework::dataset::make("NumKernels", { 1, 4 }));
 /** Direct convolution precommit data set. */
-const auto data_precommit = combine(data_small, framework::dataset::make("NumKernels", { 1 }));
+const auto data_precommit     = combine(data_small, framework::dataset::make("NumKernels", { 1 }));
+const auto data_precommit_9x9 = combine(data_small9x9, framework::dataset::make("NumKernels", { 1 }));
 
 /** Activation function Dataset*/
 const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo",
@@ -92,7 +99,7 @@ DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip(
                                                      }),
                framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F16),
                                                         TensorInfo(TensorShape(3U, 3U, 3U, 4U), 1, DataType::F32),
-                                                        TensorInfo(TensorShape(9U, 9U, 2U, 4U), 1, DataType::F32),
+                                                        TensorInfo(TensorShape(11U, 11U, 2U, 4U), 1, DataType::F32),
                                                         TensorInfo(TensorShape(5U, 3U, 2U, 4U), 1, DataType::F32),
                                                         TensorInfo(TensorShape(3U, 3U, 2U, 4U, 3U), 1, DataType::F32),
                                                         TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32),
@@ -172,6 +179,20 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolutionLayerFixture<half>, framewor
     // Validate output
     validate(CLAccessor(_target), _reference, tolerance_fp16, tolerance_num);
 }
+FIXTURE_DATA_TEST_CASE(RunLarge9x9, CLDirectConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data_nightly_9x9, framework::dataset::make("DataType",
+                                                                                                                    DataType::F16)),
+                                                                                                                    ActivationFunctionsDataset),
+                                                                                                                    framework::dataset::make("DataLayout", { DataLayout::NHWC })))
+{
+    validate(CLAccessor(_target), _reference, tolerance_fp16, tolerance_num);
+}
+FIXTURE_DATA_TEST_CASE(RunSmall9x9, CLDirectConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit_9x9, framework::dataset::make("DataType",
+                                                                                                                      DataType::F16)),
+                                                                                                                      ActivationFunctionsDataset),
+                                                                                                                      framework::dataset::make("DataLayout", { DataLayout::NHWC })))
+{
+    validate(CLAccessor(_target), _reference, tolerance_fp16, tolerance_num);
+}
 TEST_SUITE_END() // FP16
 
 TEST_SUITE(FP32)
@@ -188,6 +209,20 @@ FIXTURE_DATA_TEST_CASE(RunLarge, CLDirectConvolutionLayerFixture<float>, framewo
 {
     validate(CLAccessor(_target), _reference, tolerance_fp32);
 }
+FIXTURE_DATA_TEST_CASE(RunLarge9x9, CLDirectConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data_nightly_9x9, framework::dataset::make("DataType",
+                                                                                                                     DataType::F32)),
+                                                                                                                     ActivationFunctionsDataset),
+                                                                                                                     framework::dataset::make("DataLayout", { DataLayout::NHWC })))
+{
+    validate(CLAccessor(_target), _reference, tolerance_fp32);
+}
+FIXTURE_DATA_TEST_CASE(RunSmall9x9, CLDirectConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit_9x9, framework::dataset::make("DataType",
+                                                                                                                       DataType::F32)),
+                                                                                                                       ActivationFunctionsDataset),
+                                                                                                                       framework::dataset::make("DataLayout", { DataLayout::NHWC })))
+{
+    validate(CLAccessor(_target), _reference, tolerance_fp32);
+}
 TEST_SUITE_END() // FP32
 
 TEST_SUITE(FP32_CustomDataset)