[p1esk]

Analog computing has a lot of yet unrealized potential for machine learning algorithms.

However, currently it does not make sense to build a specialized analog chip to run specific type of ML algorithms, because algorithms are still being actively developed. I don't see GPUs being replaced by ASICs any time soon. And before you point to something like Google's TPU, the line between such ASICs and latest GPUs such as V100 is blurred.

[sanxiyn]

I define GPU as something that can efficiently implement DirectX. Hence TPU is not GPU. And I predict ML algorithms will run on non-GPU, soon-ish.

[deepnotderp]

Please explain where analog computation has a benefit over digital that outweighs its numerous disadvantages.

[p1esk]

Wait, aren’t you working on analog chips?

[deepnotderp]

No.

You may have confused me with the Isocline/Mythic guys or a red herring comment. Our approach to deep learning chips is very public and amongst the craziest...A̶n̶d̶ ̶e̶v̶e̶n̶ ̶I̶ ̶w̶o̶u̶l̶d̶n̶'̶t̶ ̶t̶o̶u̶c̶h̶ ̶a̶n̶a̶l̶o̶g̶ ̶c̶o̶m̶p̶u̶t̶a̶t̶i̶o̶n̶

To clarify: I'm always open to opposing evidence, but based on the data at the moment, I believe that analog computing buys you very little.

[p1esk]

I'm sure you know both cons and pros of analog computing. As long as you can significantly improve digital tech every year, keep doing that. But as soon as that stops, or becomes too expensive, analog is the way forward.

[deepnotderp]

Again, what advantage does analog have?

People seem to assume that analog intrinsically consumes less power, which due to bias and leakage currents isn't true in the general case.