| Just a few thoughts I've been mulling for a while about this topic: Machine learning is something that I believe can take advantage of analog computing. A machine learning algorithm does not need highly precise or accurate representations. Most current implementations of such processing units use fewer bits (usually 8). However, even if we use fewer bits, the engineering effort (design, layout, lithography, etc.) that goes into making the processing unit still assumes that those few bits are error free. The manufacturing process treats it like any other digital circuit. It assumes data processing part should be fault free (e.g. treat MSB and LSB the same). Digital circuits also demand higher power compared to analog versions. If an analog circuit can be designed for such algorithms, not only could it be much faster, it will probably consume far less power. With a super high bandwidth consuming little power, an analog processing chip may give us a much better playground to try advanced algorithms. The materials can then be optimized and we might end up with something like a brain. Brains (all animals) process far more information for the power they consume. Digital circuits give us low level reliability and so they are really good for simple control. Analog/biology don't give us that. But they can give us a high level reliability while delegating the low level reliability to digital counterparts. |
Typically you can’t even use floating point representation: not accurate enough.