oneDNN v3.7 release notes

RELEASE_NOTES.md

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+# 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).
+  * Added support and improved perfomance for fp8 matmul with bf16/fp16.
+
+## Intel Graphics Products
+  * Introduced initial optimizations for 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)
+## AArch64-based Processors
+
+# Functionality
+  * 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 pattern
+  * 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
+# Usability
+  * With SYCL runtime, memory objects on CPU engine are now reference-counted and no more need to be explicitly kept alive by user for the duration of the primitive execution. This align memory object lifetime behavior on CPU and GPU engines.
+  * Improve verbose diagnostic to better identify issues during dispatching, primitive and kernel creation for CPU primitive and GPU (in case of OpenCL implementation) primitive implementations.
+  * Improve verbose diagnostic to simplify debugging of nGEN fallbacks.
+  * 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 graph driver.
+  * Extended benchdnn with support and validation for the number of partition 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
+# 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.
+
+[project core team]: https://github.com/oneapi-src/oneDNN/blob/rls-v3.7/MAINTAINERS.md