Not sure if I completely understand what "Ryzen AI" does, but tinygrad for example has some limited support for RDNA3[0]. It isn't quite there yet in matters of

Not sure if I completely understand what "Ryzen AI" does, but tinygrad for example has some limited support for RDNA3[0]. It isn't quite there yet in matters of performance though, as you can read in the comments of that file. There's also a small tutorial by AMD on how to use the WMMA intrinsics[1] using their hipcc[2] compiler. Documentation is sparse kinda sparse, but the instruction set is not huge. The RDNA3 ISA guide[3] might also be helpful (and only a fraction of the pages are relevant.) So it doesn't seem like RE is necessary? AMD dumped most code on GitHub. 0. https://github.com/tinygrad/tinygrad/blob/master/extra/gemm/hip_matmul.py https://github.com/tinygrad/tinygrad/blob/master/extra/gemm/... 1. https://gpuopen.com/learn/wmma_on_rdna3/ https://gpuopen.com/learn/wmma_on_rdna3/ 2. https://github.com/ROCm/HIPCC https://github.com/ROCm/HIPCC 3. https://www.amd.com/content/dam/amd/en/documents/radeon-tech-docs/instruction-set-architectures/rdna3-shader-instruction-set-architecture-feb-2023_0.pdf https://www.amd.com/content/dam/amd/en/documents/radeon-tech...