The optimizations focused on three key areas: GEMM kernels, bfloat16 support, primitive caching and the memory allocator. For aarch64 platforms, PyTorch supports Arm Compute Library (ACL) GEMM kernels via Mkldnn(OneDNN) backend. The ACL library provides Neon/SVE GEMM kernels for fp32 and bfloat16 formats. The bfloat16 support on c7g allows efficient deployment of bfloat16 trained, AMP (Automatic Mixed Precision) trained, or even the standard fp32 trained models. The standard fp32 models leverage bfloat16 kernels via OneDNN fast math mode, without any model quantization. Next we implemented primitive caching for conv, matmul and inner product operators. More information on the updated PyTorch user guide with the upcoming 2.0 release improvements and TorchBench benchmark details can be found here.