Update documentation for 24.04 release
Change-Id: Ifec7015ad5712d8b84d65203a5fa21cbefcb04ad
Signed-off-by: Michael Kozlov <michael.kozlov@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/11438
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Reviewed-by: <felixjohnny.thomasmathibalan@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
diff --git a/docs/user_guide/release_version_and_change_log.dox b/docs/user_guide/release_version_and_change_log.dox
index 3737dbf..b29b815 100644
--- a/docs/user_guide/release_version_and_change_log.dox
+++ b/docs/user_guide/release_version_and_change_log.dox
@@ -43,15 +43,14 @@
v24.04 Public major release
- Add Bfloat16 data type support for @ref NEMatMul.
- - Optimize start-up time of @ref NEConvolutionLayer for some input configurations where GeMM is selected as the convolution algorithm
- - Optimize @ref NEConvolutionLayer for input tensor size > 1e7 bytes and weight tensor height > 7
- Add support for SoftMax in SME2 for FP32 and FP16.
- - Performance optimizations:
- - Optimize @ref NESoftmaxLayer for axis != 0 by natively supporting higher axes up to axis 3.
- Add support for in place accumulation to CPU GEMM kernels.
- Add low-precision Int8 * Int8 -> FP32 CPU GEMM which dequantizes after multiplication
- Add is_dynamic flag to QuantizationInfo to signal to operators that it may change after configuration
-
+ - Performance optimizations:
+ - Optimize start-up time of @ref NEConvolutionLayer for some input configurations where GeMM is selected as the convolution algorithm
+ - Optimize @ref NEConvolutionLayer for input tensor size > 1e7 bytes and weight tensor height > 7
+ - Optimize @ref NESoftmaxLayer for axis != 0 by natively supporting higher axes up to axis 3.
v24.02.1 Public patch release
- Fix performance regression in fixed-format kernels