u/Acrobatic_Lawyer2965

I know that some of the advantages of the fpgas are low latency, low power usage and creating specialized data paths like 4-bit 6-bit etc.

But when I think about feature matching or LLM models, they have so much complex math computations like softmax, large matrix multiplications etc.

And if you try to implement a model into your fpga, I think you have to use HLS because developing RTL modules would take months. And in HLS it is really difficult to specialize the modules in the most efficient way.

So I really wonder, does anyone use fpga in their AI projects where they implement complex models. If so, what are the main techniques that you use to implement so large models?