- Google using RL for chip layout optimization [1]
- Stanford profs optimizing lenses end to end with image processing [2]
As for the "diffractive deep neural network" paper it is not really a deep network as they don't implement the non-linearities in the optical domain. That still is the hard bit, so maybe the monocle is not realistic, but fortunately there are ways it can still speed up computation.
There is a general consensus that GPU-s enabled the current deep learning revolution and perhaps as argued by the Hardware Lottery paper [3] backprop is really just the current lucky ticket. So why not try to evolve hardware with ML for other algorithms too?
As for the questions raised, I do want to know your answer to this:
> a question for computer scientists, what single question would you ask if you have a dedicated computer that was [that multiplier] faster?
[1] https://ai.googleblog.com/2020/04/chip-design-with-deep-rein...