alt Hacker NewsFPGA for AI only makes sense when machine learning had diverse model architectures.
After Transformer took over AI, FPGA for AI is totally dead now. Because Transformer is all about math matrix calculation, ASIC is the solution.
Modern Datacenter GPU is nearly AISC now.
Yes, if you're doing what everyone else is doing you can just use tensor cores and libraries which optimize for that.
Contrarily if you're doing something that doesn't map that well to tensor cores you have a problem: every generation a larger portion of the die is devoted to low/mixed precision mma operations. Maybe FGPAs can find a niche that is underserved by current GPUs, but I doubt it. Writing a cuda/hip/kokkos kernel is just soo much cheaper and accessible than vhdl it's not even funny.
AMD needs to invest in that: Let me write a small FPGA kernel in line in a python script, compile it instantly and let me pipe numpy arrays into that (similar to cupy rawkernels). If that workflow works and let's me iterate fast, I could be convinced to get deeper into it.