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I feel like more and more transistors will be dedicated to machine learning tasks. You'll eventually have phones and computers constantly running neural network

I feel like more and more transistors will be dedicated to machine learning tasks. You'll eventually have phones and computers constantly running neural networks to accomplish advanced but relatively mundane tasks (e.g. text-to-speech, speech-to-text, facial recognition, human-seeming AI in videogames, neural rendering (see NeRF from Berkeley), and tons of other random stuff. All of it is parallel and benefits from a sheer increase in transistors and density. Maybe in a few generations we'll see chips that are 99% ML compute and 1% traditional CPU.

ncmncm 6y ago
In those chips, I think practically all the transistors have to be active all the time, so the density must be quite limited unless you have really serious cooling capacity.
- ladberg 6y ago
  I feel like there's a ton of unexplored area in the hardware space for extremely low power circuits used for ML. As we know, you can do ML with very low precision, which is why chips that can do fast INT8 and F16 are getting more popular. However, these lower power chips are still designed like classical computers and are deterministic. What if you had something like a few thousand analog multipliers in parallel computing matrix products? An analog multiply can use far less power than a digital one at the expense of accuracy and determinism, which might not matter for some ML tasks.