Yes. That is precisely what i'm saying. If i'm mistaken in saying that that's one thing, but as far as it being what i'm saying, it very much is. It's been an important guiding principle for some time now in the project, that recent chips--including FPGA's--tend to have hard IP for floating point multiplication.
Now spending a lot of chip area on getting more FLOPS is not necessarily a bad decision if there is no alternative for achieving fast matrix multiplication. Almost any method is sensible if there was no better alternative available when the decision to use that method was made. In addition, fgemm only really makes sense when matrices contain over 1000 elements per row or column, not sure how much more than 1000 per vector but more than that. Small and in particular small and dense matrices are still best multiplied exactly the way GPUs multiply them, with many floating-point multiplier circuits in parallel. It's not stupid in the least.
Yeah so NVidia n Google have the same business model i'm going for, Google having TPUs in its datacenters that do work that cannot be reverse engineered. Google does not sell TPUs. You can use them by sending Google the work, and you'll benefit from much lower cost and faster speed. NVidia has a similar offering, just not as well-known. That's the correct business model in my analysis, and what fgemm will sell. Sell the work.