Reconfigurable logic may be used to implement fairly small models, in applications where they're already employed and adding a coprocessor specifically for ML i

Reconfigurable logic may be used to implement fairly small models, in applications where they're already employed and adding a coprocessor specifically for ML is either infeasible or doesn't make sense. As the paper mentions, you need hard logic blocks for arithmetic (if not floating point), and these are always in short supply. In DSP applications I've worked on we used the fpga for timing and i/o, to control jitter and sample from many ADCs in parallel - but apart from some filtering then ran the numbers on an adjacent non-reconfigurable core. You can get huge chips with a lot of hard logic built in, but they're relatively expensive compared to fpga + traditional coprocessor with shared memory. The high end application specific chips are shockingly expensive - worse than GPUs because there is comparatively little market for them. We had one evaluation board that was like $100K iirc.