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That seems to be the wrong way around. We're constrained by the availability of GPUs because those are the processors of choice for this kind of solution. If we had a better one we'd be using it!


Related: The Hardware Lottery (https://arxiv.org/abs/2009.06489)

It goes both ways: processors are built to accelerate existing applications, and applications are built to run with the constraints of the hardware. This holds back progress towards better systems where both new kind of hardware and software is required.

This is part of the reason why research into neural networks was so slow for a long time, and took off only after GPUs for 3d graphics came around.


What GP seems to be saying is that deep learning flourished because of the availability of GPUs, if fast SAT solving chips had appeared before.




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