Fix documentation about BF16 acceleration
* Fix the heading and the code block.
Resolves: COMPMID-5546
Signed-off-by: Viet-Hoa Do <viet-hoa.do@arm.com>
Change-Id: I60162b0e0aaf2a71a70e517aaeb8c75dd82d8dd9
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/8652
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: Pablo Marquez Tello <pablo.tello@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/docs/user_guide/library.dox b/docs/user_guide/library.dox
index b95e0ba..0501322 100644
--- a/docs/user_guide/library.dox
+++ b/docs/user_guide/library.dox
@@ -54,14 +54,16 @@
- no-fast-math: No Winograd support
- fast-math: Supports Winograd 3x3,3x1,1x3,5x1,1x5,7x1,1x7,5x5,7x7
-@section BF16 acceleration
+@section bf16_acceleration BF16 acceleration
-- Required toolchain: android-ndk-r23-beta5 or later
-- To build for BF16: "neon" flag should be set "=1" and "arch" has to be "=armv8.6-a", "=armv8.6-a-sve", or "=armv8.6-a-sve2" using following command:
-- scons arch=armv8.6-a-sve neon=1 opencl=0 extra_cxx_flags="-fPIC" benchmark_tests=0 validation_tests=0 validation_examples=1 os=android Werror=0 toolchain_prefix=aarch64-linux-android29
-- To enable BF16 acceleration when running FP32 "fast-math" has to be enabled and that works only for Neon convolution layer using cpu gemm.
- In this scenario on CPU: the CpuGemmConv2d kernel performs the conversion from FP32, type of input tensor, to BF16 at block level to exploit the arithmetic capabilities dedicated to BF16. Then transforms back to FP32, the output
- tensor type.
+Required toolchain: android-ndk-r23-beta5 or later.
+
+To build for BF16: "neon" flag should be set "=1" and "arch" has to be "=armv8.6-a", "=armv8.6-a-sve", or "=armv8.6-a-sve2". For example:
+
+ scons arch=armv8.6-a-sve neon=1 opencl=0 extra_cxx_flags="-fPIC" benchmark_tests=0 validation_tests=0 validation_examples=1 os=android Werror=0 toolchain_prefix=aarch64-linux-android29
+
+To enable BF16 acceleration when running FP32 "fast-math" has to be enabled and that works only for Neon convolution layer using cpu gemm.
+In this scenario on CPU: the CpuGemmConv2d kernel performs the conversion from FP32, type of input tensor, to BF16 at block level to exploit the arithmetic capabilities dedicated to BF16. Then transforms back to FP32, the output tensor type.
@section architecture_thread_safety Thread-safety