mlir-aie

AMD/Xilinx's software support for the NPU is fully open, it's only FFLM's models that are proprietary. See https://github.com/amd/iron https://github.com/Xilinx/mlir-aie https://github.com/amd/RyzenAI-SW/ . It would be nice to explore whether one can simply develop kernels for these NPU's using Vulkan Compute and drive them that way; that would provide the closest unification with the existing cross-platform support for GPU's.

FYI, amd has some prototype alternative programming models for these NPU engines now, although they are certainly very immature: https://github.com/Xilinx/mlir-aie/tree/main/programming_gui...

>come up with a "better" (performant, cheaper, easier to use, etc.) solution than GPUs for ML applications you probably are aware but Xilinx themselves is attempting this with their versal aie boards which (in spirit) similar to GPUs, in that they group together a programmable fabric of programmable SIMD type compute cores. https://www.xilinx.com/support/documentation/architecture-ma... i have not played with one but i've been told (by a xilinx person, so grain of salt) the flow from high-level representation to that arch is more open https://github.com/Xilinx/mlir-aie