COMPMID-807: NHWC support in CLDirectConvolution.

Change-Id: I8738aca2cc0104e4c4d7c9605762ab59fce10a33
Reviewed-on: https://eu-gerrit-1.euhpc.arm.com/137333
Reviewed-by: Giorgio Arena <giorgio.arena@arm.com>
Reviewed-by: Anthony Barbier <anthony.barbier@arm.com>
Tested-by: Jenkins <bsgcomp@arm.com>

1 files changed, 209 insertions, 25 deletions

diff --git a/src/core/CL/cl_kernels/direct_convolution5x5.cl b/src/core/CL/cl_kernels/direct_convolution5x5.cl
index e678f6f51b..70be058854 100644
--- a/src/core/CL/cl_kernels/direct_convolution5x5.cl
+++ b/src/core/CL/cl_kernels/direct_convolution5x5.cl
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2016, 2017 ARM Limited.
+ * Copyright (c) 2016-2018 ARM Limited.
  *
  * SPDX-License-Identifier: MIT
  *
@@ -69,6 +69,190 @@
         acc += (VEC_DATA_TYPE(DATA_TYPE, 8))(src0.s468a, src0.sCE, src1.s02) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_value1;     \
     })
 
+#if defined(DATA_LAYOUT_NHWC)
+
+#define PTR_TO_VALUE(PTR, DATA_TYPE) *((__global DATA_TYPE *)(PTR))
+
+#if STRIDE_X == 1
+#define CONVOLUTION1x5_NHWC(acc, row_ptr, weights_ptr) CONVOLUTION1x5_STRIDE1_NHWC(acc, row_ptr, weights_ptr)
+#elif STRIDE_X == 2 /* STRIDE_X == 1 */
+#define CONVOLUTION1x5_NHWC(acc, row_ptr, weights_ptr) CONVOLUTION1x5_STRIDE2_NHWC(acc, row_ptr, weights_ptr)
+#else /* STRIDE_X not equals 1 or 2 */
+#error "STRIDE_X larger than 2 is not supported"
+#endif /* STRIDE_X == 2 */
+
+#define CONVOLUTION1x5_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, 4)                                                                                                    \
+        src1 = (VEC_DATA_TYPE(DATA_TYPE, 4))(                                                                                          \
+                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));               \
+        VEC_DATA_TYPE(DATA_TYPE, 4)                                                                                                    \
+        weights_values0 = (VEC_DATA_TYPE(DATA_TYPE, 4))(                                                                               \
+                          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)); \
+        DATA_TYPE weights_value1 = PTR_TO_VALUE(weights_ptr + 4 * 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.s45, src0.s67, src1.s0123) * (VEC_DATA_TYPE(DATA_TYPE, 8))weights_value1;            \
+    })
+
+#define CONVOLUTION1x5_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, 4)                                                                                                    \
+        src1 = (VEC_DATA_TYPE(DATA_TYPE, 4))(                                                                                          \
+                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));               \
+        VEC_DATA_TYPE(DATA_TYPE, 4)                                                                                                    \
+        weights_values0 = (VEC_DATA_TYPE(DATA_TYPE, 4))(                                                                               \
+                          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)); \
+        DATA_TYPE weights_value1 = PTR_TO_VALUE(weights_ptr + 4 * 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_value1;            \
+    })
+
+/** 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                            Pointer to the biases tensor. Same as @p src_ptr
+ * @param[in]  biases_stride_x                       Stride of the biases tensor in X dimension (in bytes)
+ * @param[in]  biases_step_x                         biases_stride_x * number of elements along X processed per workitem(in bytes)
+ * @param[in]  biases_offset_first_element_in_bytes  The offset of the first element in the biases tensor
+ * @param[in]  weights_stride_w                      Stride of the weights tensor in the 4th dimension
+ */
+__kernel void direct_convolution5x5_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)
+    values0 = 0;
+
+    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) - src_stride_x * id0 + ((id2 * STRIDE_Y) - PAD_TOP) * (int)src_stride_z;
+
+    weights_addr += id0 * weights_stride_w;
+    const int coordy = id2 - PAD_TOP;
+
+    for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d)
+    {
+#if(PAD_TOP)
+        if(coordy < 0) // special case Z = -1 doesn't exists
+        {
+            //skip first row and load the two next ones
+            CONVOLUTION1x5_NHWC(values0, (src_addr + 1 * (int)src_stride_z), (weights_addr + 1 * (int)weights_stride_z));
+            CONVOLUTION1x5_NHWC(values0, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z));
+            CONVOLUTION1x5_NHWC(values0, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z));
+            CONVOLUTION1x5_NHWC(values0, (src_addr + 4 * (int)src_stride_z), (weights_addr + 4 * (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.
+            CONVOLUTION1x5_NHWC(values0, src_addr, weights_addr);
+            CONVOLUTION1x5_NHWC(values0, (src_addr + 1 * (int)src_stride_z), (weights_addr + 1 * (int)weights_stride_z));
+            CONVOLUTION1x5_NHWC(values0, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z));
+            CONVOLUTION1x5_NHWC(values0, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z));
+        }
+        else
+        {
+            CONVOLUTION1x5_NHWC(values0, src_addr, weights_addr);
+            CONVOLUTION1x5_NHWC(values0, (src_addr + 1 * (int)src_stride_z), (weights_addr + 1 * (int)weights_stride_z));
+            CONVOLUTION1x5_NHWC(values0, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z));
+            CONVOLUTION1x5_NHWC(values0, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z));
+            CONVOLUTION1x5_NHWC(values0, (src_addr + 4 * (int)src_stride_z), (weights_addr + 4 * (int)weights_stride_z));
+        }
+#else  //PAD_TOP > 0
+        CONVOLUTION1x5_NHWC(values0, src_addr, weights_addr);
+        CONVOLUTION1x5_NHWC(values0, (src_addr + 1 * (int)src_stride_z), (weights_addr + 1 * (int)weights_stride_z));
+        CONVOLUTION1x5_NHWC(values0, (src_addr + 2 * (int)src_stride_z), (weights_addr + 2 * (int)weights_stride_z));
+        CONVOLUTION1x5_NHWC(values0, (src_addr + 3 * (int)src_stride_z), (weights_addr + 3 * (int)weights_stride_z));
+        CONVOLUTION1x5_NHWC(values0, (src_addr + 4 * (int)src_stride_z), (weights_addr + 4 * (int)weights_stride_z));
+#endif // PAD_TOP > 0
+
+        src_addr += src_stride_x;
+        weights_addr += weights_stride_x;
+    }
+
+#ifdef HAS_BIAS
+    Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);
+    values0 += (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)) = values0.s0;
+    *((__global DATA_TYPE *)(dst.ptr + 1 * dst_stride_y)) = values0.s1;
+    *((__global DATA_TYPE *)(dst.ptr + 2 * dst_stride_y)) = values0.s2;
+    *((__global DATA_TYPE *)(dst.ptr + 3 * dst_stride_y)) = values0.s3;
+    *((__global DATA_TYPE *)(dst.ptr + 4 * dst_stride_y)) = values0.s4;
+    *((__global DATA_TYPE *)(dst.ptr + 5 * dst_stride_y)) = values0.s5;
+    *((__global DATA_TYPE *)(dst.ptr + 6 * dst_stride_y)) = values0.s6;
+    *((__global DATA_TYPE *)(dst.ptr + 7 * dst_stride_y)) = values0.s7;
+}
+
+#endif // defined(DATA_LAYOUT_NHWC)
+
 /** This kernel performs a direct convolution to convolve the low three dimensions.
  *
  * @note The data type must be passed at compile time using -DDATA_TYPE: e.g. -DDATA_TYPE=float
@@ -119,7 +303,7 @@ __kernel void direct_convolution5x5(
     Tensor3D dst     = CONVERT_TO_TENSOR3D_STRUCT(dst);
 
     VEC_DATA_TYPE(DATA_TYPE, 8)
-    pixels0 = 0;
+    values0 = 0;
 
     __global uchar *weights_addr = (__global uchar *)tensor3D_offset(&weights, 0, 0, 0);
     __global uchar *src_addr     = (__global uchar *)offset(&src, 0, 0);
@@ -129,11 +313,11 @@ __kernel void direct_convolution5x5(
 
     for(volatile int d = 0; d < WEIGHTS_DEPTH; ++d)
     {
-        CONVOLUTION1x5(pixels0, (__global DATA_TYPE *)src_addr, (__global DATA_TYPE *)weights_addr);
-        CONVOLUTION1x5(pixels0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y));
-        CONVOLUTION1x5(pixels0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y));
-        CONVOLUTION1x5(pixels0, (__global DATA_TYPE *)(src_addr + 3 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 3 * weights_stride_y));
-        CONVOLUTION1x5(pixels0, (__global DATA_TYPE *)(src_addr + 4 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 4 * weights_stride_y));
+        CONVOLUTION1x5(values0, (__global DATA_TYPE *)src_addr, (__global DATA_TYPE *)weights_addr);
+        CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 1 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 1 * weights_stride_y));
+        CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 2 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 2 * weights_stride_y));
+        CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 3 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 3 * weights_stride_y));
+        CONVOLUTION1x5(values0, (__global DATA_TYPE *)(src_addr + 4 * src_stride_y), (__global DATA_TYPE *)(weights_addr + 4 * weights_stride_y));
 
         src_addr += src_stride_z;
         weights_addr += weights_stride_z;
@@ -142,10 +326,10 @@ __kernel void direct_convolution5x5(
 #ifdef HAS_BIAS
     Vector biases = CONVERT_TO_VECTOR_STRUCT_NO_STEP(biases);
 
-    pixels0 += (VEC_DATA_TYPE(DATA_TYPE, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, kernel_index)));
+    values0 += (VEC_DATA_TYPE(DATA_TYPE, 8)) * ((__global DATA_TYPE *)(vector_offset(&biases, kernel_index)));
 #endif /* defined(HAS_BIAS) */
 
-    vstore8(pixels0, 0, (__global DATA_TYPE *)dst.ptr);
+    vstore8(values0, 0, (__global DATA_TYPE *)dst.ptr);
 }
 #endif // defined(DATA_TYPE) && defined(STRIDE_X) && defined(WEIGHTS_DEPTH)
 
@@ -226,8 +410,8 @@ __kernel void direct_convolution5x5_f32_bifrost(
     Image    src = CONVERT_TO_IMAGE_STRUCT(src);
     Tensor3D dst = CONVERT_TO_TENSOR3D_STRUCT(dst);
 
-    float4 pixels0 = 0.0f;
-    float4 pixels1 = 0.0f;
+    float4 values0 = 0.0f;
+    float4 values1 = 0.0f;
 
     __global uchar *weights_addr = (__global uchar *)(weights_ptr + weights_offset_first_element_in_bytes + kernel_index * weights_stride_w);
     __global uchar *src_addr     = (__global uchar *)offset(&src, 0, 0);
@@ -247,14 +431,14 @@ __kernel void direct_convolution5x5_f32_bifrost(
         src0 = vload8(0, (__global float *)(src_addr + 0 * src_stride_y));
 
         // Accumulate
-        CONVOLUTION1x5_BIFROST(pixels0, src0, weights_row00, weights_row01);
+        CONVOLUTION1x5_BIFROST(values0, src0, weights_row00, weights_row01);
 
         // Load values from row1 of input tensor
         src0 = vload8(0, (__global float *)(src_addr + 1 * src_stride_y));
 
         // Accumulate
-        CONVOLUTION1x5_BIFROST(pixels0, src0, weights_row10, weights_row11);
-        CONVOLUTION1x5_BIFROST(pixels1, src0, weights_row00, weights_row01);
+        CONVOLUTION1x5_BIFROST(values0, src0, weights_row10, weights_row11);
+        CONVOLUTION1x5_BIFROST(values1, src0, weights_row00, weights_row01);
 
         // Load values from row2 of input tensor
         src0 = vload8(0, (__global float *)(src_addr + 2 * src_stride_y));
@@ -264,8 +448,8 @@ __kernel void direct_convolution5x5_f32_bifrost(
         weights_row01 = *((__global float *)(weights_addr + 2 * weights_stride_y) + 4);
 
         // Accumulate
-        CONVOLUTION1x5_BIFROST(pixels0, src0, weights_row00, weights_row01);
-        CONVOLUTION1x5_BIFROST(pixels1, src0, weights_row10, weights_row11);
+        CONVOLUTION1x5_BIFROST(values0, src0, weights_row00, weights_row01);
+        CONVOLUTION1x5_BIFROST(values1, src0, weights_row10, weights_row11);
 
         // Load values from row3 of input tensor
         src0 = vload8(0, (__global float *)(src_addr + 3 * src_stride_y));
@@ -275,8 +459,8 @@ __kernel void direct_convolution5x5_f32_bifrost(
         weights_row11 = *((__global float *)(weights_addr + 3 * weights_stride_y) + 4);
 
         // Accumulate
-        CONVOLUTION1x5_BIFROST(pixels0, src0, weights_row10, weights_row11);
-        CONVOLUTION1x5_BIFROST(pixels1, src0, weights_row00, weights_row01);
+        CONVOLUTION1x5_BIFROST(values0, src0, weights_row10, weights_row11);
+        CONVOLUTION1x5_BIFROST(values1, src0, weights_row00, weights_row01);
 
         // Load values from row4 of input tensor
         src0 = vload8(0, (__global float *)(src_addr + 4 * src_stride_y));
@@ -285,14 +469,14 @@ __kernel void direct_convolution5x5_f32_bifrost(
         weights_row00 = vload4(0, (__global float *)(weights_addr + 4 * weights_stride_y));
         weights_row01 = *((__global float *)(weights_addr + 4 * weights_stride_y) + 4);
 
-        CONVOLUTION1x5_BIFROST(pixels0, src0, weights_row00, weights_row01);
-        CONVOLUTION1x5_BIFROST(pixels1, src0, weights_row10, weights_row11);
+        CONVOLUTION1x5_BIFROST(values0, src0, weights_row00, weights_row01);
+        CONVOLUTION1x5_BIFROST(values1, src0, weights_row10, weights_row11);
 
         // Load values from row5 of input tensor
         src0 = vload8(0, (__global float *)(src_addr + 5 * src_stride_y));
 
         // Accumulate
-        CONVOLUTION1x5_BIFROST(pixels1, src0, weights_row00, weights_row01);
+        CONVOLUTION1x5_BIFROST(values1, src0, weights_row00, weights_row01);
 
         src_addr += src_stride_z;
         weights_addr += weights_stride_z;
@@ -303,11 +487,11 @@ __kernel void direct_convolution5x5_f32_bifrost(
 
     float4 bias = (float4) * ((__global float *)(vector_offset(&biases, kernel_index)));
 
-    pixels0 += bias;
-    pixels1 += bias;
+    values0 += bias;
+    values1 += bias;
 #endif /* defined(HAS_BIAS) */
 
-    vstore4(pixels0, 0, (__global float *)(dst.ptr + 0 * dst_stride_y));
-    vstore4(pixels1, 0, (__global float *)(dst.ptr + 1 * dst_stride_y));
+    vstore4(values0, 0, (__global float *)(dst.ptr + 0 * dst_stride_y));
+    vstore4(values1, 0, (__global float *)(dst.ptr + 1 * dst_stride_y));
 }
 #endif // defined(WEIGHTS_DEPTH)