ML training is bottlenecked on arithmetic performance. Computational fluid dynamics is bottlenecked on arithmetic performance. There are others, but those are

ML training is bottlenecked on arithmetic performance. Computational fluid dynamics is bottlenecked on arithmetic performance. There are others, but those are the two obvious ones. Nobody in any bottlenecked field is sufficiently motivated to generate a chip that is potentially worth lots of millions of dollars? Really? That's a pretty strong statement. And, if posits can't break those bottlenecks, are they really better? I would cite 3D graphics, but I suspect that memory bandwidth is more the problem as it seems that people are willing to double or quadruple the computational workload to avoid a memory stall or roundtrip. And I don't have enough background to discuss whether HFT is bottlenecked on arithmetic performance.