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Can't we make a chip that only does one thing: multiply and add a lot of 32x32 matrices in parallel? I think that would be enough for all AI needs and easy to p
by EVa5I7bHFq9mnYK 2y ago
Can't we make a chip that only does one thing: multiply and add a lot of 32x32 matrices in parallel? I think that would be enough for all AI needs and easy to program.
- jsolson 2y agoYou're sorta describing TPUs, especially early ones: https://cloud.google.com/blog/products/ai-machine-learning/an-in-depth-look-at-googles-first-tensor-processing-unit-tpu https://cloud.google.com/blog/products/ai-machine-learning/a... That first generation also included the ability to apply a handful of fixed activation functions, but really that's about it. The array is bigger than 32x32, also.
- 0-_-0 2y agoBelieve it or not, to be able to do that at a high throughput you need half of a GPU anyway, like a cache hierarchy.
- EVa5I7bHFq9mnYK 2y agoGreat, eliminating half of transistors is a lot.
- deleted 2y ago[deleted]
- marshray 2y agoYou'll need large economies of scale to do better than GPUs, and you'll still have all the same problems of memory IO, packaging, and heat removal. Transistors that aren't in use cost almost nothing. This is a big part of why RISC didn't win and today the largest server chips in use in datacenters are still mostly compatible with an 8 bit part from the early 1980's.