oneDNN v3.7 release notes

RELEASE_NOTES.md

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+# Performance Optimizations
+
+## Intel Architecture Processors
+  * Improved performance of convolution and matmul primitives on Intel Xeon processors with Intel AMX support (formerly Sapphire Rapids and Granite Rapids).
+  * Improved performance of `fp8` matmul primitives with `bf16` and `fp16` bias data type on Intel Xeon processors with Intel AMX instruction set support (formerly Sapphire Rapids and Granite Rapids).
+  * Improved performance of `int8` RNN primitive on processors with Intel AVX2 and Intel AVX-512 instruction set support.
+  * Improved performance of `int8` depthwise separable convolution primitive with per-channel zero points on processors with Intel AVX2 and Intel AVX-512 instruction set support.
+  * Improved `fp16` and `bf16` softmax performance with relaxed [accumulation mode].
+  * Improved performance of `int8` matmul primitive with `fp16` output data type.
+  * Improved performance of the following subgraphs with Graph API:
+    * [Gated Multi-Layer Perceptron (Gated MLP)].
+
+[accumulation mode]: https://oneapi-src.github.io/oneDNN/dev_guide_attributes_accumulation_mode.html#doxid-dev-guide-attributes-accumulation-mode
+
+## Intel Graphics Products
+  * 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 convolution with source zero points by pre-packing compenstation.
+  * Improved performance of backward by data convolution with strides for large filter.
+  * Improved performance of the following subgraphs with Graph API:
+    * Scaled Dot-Product Attention (SDPA) with [implicit causal mask].
+    * SDPA with [`int8` or `int4` compressed key and value].
+    * Gated-MLP.
+
+[implicit causal mask]: https://oneapi-src.github.io/oneDNN/dev_guide_graph_sdpa.html#doxid-dev-guide-graph-sdpa
+[`int8` or `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
+[Gated Multi-Layer Perceptron (Gated MLP)]: https://oneapi-src.github.io/oneDNN/dev_guide_graph_gated_mlp.html#doxid-dev-guide-graph-gated-mlp
+
+## AArch64-based Processors
+
+# Functionality
+
+## Common
+  * Introduced support for `select` algorithm in binary primitive. The functionality is optimized for Intel CPUs.
+  * 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`], [`GenIndex`], and [`GreaterEqual`] operations in Graph API.
+
+[`Select`]: https://oneapi-src.github.io/oneDNN/dev_guide_op_select.html
+[`GenIndex`]: https://oneapi-src.github.io/oneDNN/dev_guide_op_genindex.html
+[`GreaterEqual`]: https://oneapi-src.github.io/oneDNN/dev_guide_op_greaterequal.html
+
+## Intel Architecture Processors
+  * Introduced support for `fp32` matmul with `fp16` and `bf16` weights.
+
+## Intel Graphics Products
+  * Introduced stochastic rounding support for convolution, matmul and reorder based on Philox counter-based random number generator.
+  * Introduced support for strided memory formats in convolution.
+
+# Usability
+
+## Common
+  * 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.
+  * Added Graph API examples for [Gated MLP] and [`int4` Gated MLP] patterns.
+
+[Gated MLP]: https://github.com/oneapi-src/oneDNN/blob/rls-v3.7/examples/graph/gated_mlp.cpp
+[`int4` Gated MLP]: https://github.com/oneapi-src/oneDNN/blob/rls-v3.7/examples/graph/gated_mlp_int4.cpp
+
+## Intel Architecture Processors
+  * Improved verbose diagnostics to better identify issues during dispatching, primitive and kernel creation for Intel CPU and Intel GPU implementations.
+  * Enabled frame pointers support on Intel64 platforms to improve integration with profilers.
+
+## Intel Processor Graphics
+  * Improved verbose diagnostics for Intel GPU driver compatibility issues.
+  * Improved support of large size tensors in convolution, matmul and reduction primitives on Intel GPUs.
+  * Reduced scratchpad usage for NCHW convolution on Intel GPUs.
+
+# 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
+  * Experimental [Graph Compiler] is deprecated and will be removed in future releases.
+
+[Graph Compiler]: https://oneapi-src.github.io/oneDNN/v3.7/dev_guide_graph_compiler.html
+
+# 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.
+  * 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].
+
+# Thanks to our 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