doc: incorporated additional Intel GPU input

RELEASE_NOTES.md

@@ -1,4 +1,5 @@
 # Performance Optimizations
+
 ## Intel Architecture Processors
   * Improved fp16/bf16 softmax performance with relaxed [accumulation mode](https://oneapi-src.github.io/oneDNN/dev_guide_attributes_accumulation_mode.html#doxid-dev-guide-attributes-accumulation-mode).
   * Improved performance for int8 RNN primitive on processors with Intel AVX2 and Intel AVX512 instruction set support.
@@ -8,42 +9,55 @@
   * Improved performance of int8 depthwise separable convolution primitive with pre-channel zero points on processors with Intel AVX2 and Intel AVX512 instruction set support.
 
 ## Intel Graphics Products
-  * Introduced initial optimizations for GPUs based on Xe3 architecture.
+  * Introduced initial optimizations for Intel GPUs based on Xe3 architecture.
   * Improved performance for Intel Arc Graphics for Intel Core Ultra processors (Series 2) (formerly Lunar Lake) and Intel Arc B-series discrete graphics (formerly Battlemage).
-  * Improved performance of the following subgraphs with Graph API
-    * Scaled Dot-Product Attention (SDPA) [with implicit causal mask](https://oneapi-src.github.io/oneDNN/dev_guide_graph_sdpa.html#doxid-dev-guide-graph-sdpa)
-    * Scaled Dot-Product Attention (SDPA) [with int8/int4 compressed key and value](https://oneapi-src.github.io/oneDNN/dev_guide_graph_sdpa_compressed_kv.html#doxid-dev-guide-graph-sdpa-compressed-kv)
+  * Improved performance of the following subgraphs with Graph API:
+    * Scaled Dot-Product Attention (SDPA) [with implicit causal mask](https://oneapi-src.github.io/oneDNN/dev_guide_graph_sdpa.html#doxid-dev-guide-graph-sdpa).
+    * Scaled Dot-Product Attention (SDPA) [with int8/int4 compressed key and value](https://oneapi-src.github.io/oneDNN/dev_guide_graph_sdpa_compressed_kv.html#doxid-dev-guide-graph-sdpa-compressed-kv).
+  * Improved performance of convolution with source zero points by pre-packing compenstation.
+  * Improved performance of backward by data convolution with strides for large filter.
+
 ## AArch64-based Processors
 
 # Functionality
+
+## Common
   * Introduced support for `select` algorithm in binary primitive. The functionality is optimized for Intel CPUs.
-  * Enabled support for matmul primitive with grouped quantization on weight along N dimension
-  * Graph API: new [`Select`](https://oneapi-src.github.io/oneDNN/dev_guide_op_select.html), [`GenIndex`](https://oneapi-src.github.io/oneDNN/dev_guide_op_genindex.html) and [`GreaterEqual`](https://oneapi-src.github.io/oneDNN/dev_guide_op_greaterequal.html) operations.
-  * Introduced support for fp16/bf16 compressed weights in fp32 matmul on Intel CPUs.
-  * Introduced support for grouped scales and zero points in reorder primitive.
-  * Enabled support for 4d weight scale in matmul primitive.
-  * Graph API: Added support for quantized and non-quantized Gated MLP patterns
-  * Introduced preliminary support for 4-bit floating-point data types `f4_e2m1` and `f4_e3m0` in matmul and reorder, as well as `e8m0` scales data type in matmul and reorder.
-  * [experimental] Extended microkernel API:
-		Introduced int4 quantization support.
-		Fpmath mode API
+  * Extended quantization support in matmul and reorder with grouped scales and zero-points for weights. This functionality is optimized for Intel CPUs and GPUs.
+  * Introduced initial support for 4-bit floating-point data types `f4_e2m1` and `f4_e3m0` in matmul and reorder, as well as `e8m0` scales data type in matmul and reorder. This functionality is available on Intel CPUs and GPUs.
+  * Introduced [`Select`](https://oneapi-src.github.io/oneDNN/dev_guide_op_select.html), [`GenIndex`](https://oneapi-src.github.io/oneDNN/dev_guide_op_genindex.html) and [`GreaterEqual`](https://oneapi-src.github.io/oneDNN/dev_guide_op_greaterequal.html) operations in Graph API.
+  * Graph API: Added support for quantized and non-quantized Gated MLP patterns.
+
+## Intel Architecture Processors
+  * Introduced support for fp16/bf16 compressed weights in fp32 matmul.
+
+## Intel Graphics Products
+  * Introduced stochastic rounding support for convolution, matmul and reorder based on Philox counter-based random number generator.
+  * Added support for strided memory formats in convolution.
+
 # Usability
   * With the SYCL runtime, memory objects on the CPU engine are now reference-counted and no longer need to be explicitly kept alive for the duration of the primitive execution. This aligns memory object lifetime behavior on CPU and GPU engines.
+  * Improved support of large size tensors in convolution, matmul and reduction primitives on Intel GPUs.
+  * Reduced scratchpad usage for NCHW convolution on Intel GPUs.
   * Improved verbose diagnostics to better identify issues during dispatching, primitive and kernel creation for CPU primitive and GPU (in case of OpenCL implementation) primitive implementations.
   * Improved verbose diagnostics for Intel GPU driver compatibility issues.
   * Enabled frame pointers support on Intel64 platforms to improve integration with profilers.
   * Added [examples](https://github.com/oneapi-src/oneDNN/tree/main/examples/graph) for Gated MLP and int4 Gated MLP
+
 # Validation
   * Extended benchdnn with support and validation for fp8 matmul patterns for tensor tags in RNN primitive validation.
   * Extended benchdnn with support for rewriting data types in the test JSON files in the graph driver.
   * Extended benchdnn with support and validation for the number of partitions returned from the test JSON files.
+
 # Deprecated Functionality
 
 # Breaking Changes
   * Updated minimal supported CMake version to 3.13 (was 2.8.12).
   * Updated minimal supported GCC version to 8.0 (was 4.8).
   * Updated minimal supported Clang version to 11.0 (was 3.0).
-  * Removed support for SYCL older than 2020
+  * Removed support for SYCL older than 2020.
+  * Enforced `fp32` accumulation mode in `fp16` matmul and inner product primitives on Intel Graphics products without Intel XMX cores. Previous behavir can be enabled with relaxed [accumulation mode](https://oneapi-src.github.io/oneDNN/dev_guide_attributes_accumulation_mode.html#doxid-dev-guide-attributes-accumulation-mode).
+
 # Thanks to these Contributors
 
 This release contains contributions from the [project core team] as well as Aditya Tewari @aditew01, Alexandra Sidorova @a-sidorova, Atharva Dubey @AD2605, Deb Taylor @deb-intel, Dmitriy Ovchinnikov @inteldimitrius, Fadi Arafeh @fadara01, Hengyu Meng @airMeng, @hmaciak, John Osorio @kala855, Marek Michalowski @michalowski-arm, Michael Froelich @MichaelFroelich, Michał Górny @mgorny, Nikhil Sharma @nikhilfujitsu, Permanence AI Coder @Permanence-AI-Coder, @raistefintel, Ravi Pushkar @rpushkarr, Renato Barros Arantes @renato-arantes, Romain Biessy @Rbiessy, Ryo Suzuki @Ryo-not-rio, @Shreyas-fuj, Varad Ahirwadkar @varad-ahirwadkar, @vishwascm, and Ye Tao @taoye9. We would also like to thank everyone who asked questions and reported issues.