[ein0p]

People need to read Hamming’s old papers in which he very clearly explains why analog circuits are not viable at scale. This is also why the brain uses spikes rather than continuous signals. The issue is noise, interference, and attenuation. There’s no way to get around this. If they have invented a way, I’d like to see it. But until it’s demonstrated, I’d take such things with a large grain of salt.

[Animats]

You can re-quantize analog signals into a finite number of levels to prevent noise accumulation. That's how TLC (8 levels) and QLC (16 levels) flash memory cells work. The cells store an analog value, but it's forced to a value close to one of N discrete values. The same approach is used in modems.

Deep learning doesn't seem to need that much numerical precision. People started with 32-bit floats, then 16-bit floats, now sometimes 8-bit floats, and recently there are people talking up 2-bit trinary. The number of levels needed may not be too much for analog. If you have a regenerator once in a while to slot values back to the allowed discrete levels, you can clean up the noise. That's an analog to digital to analog conversion, of course.

That's not what these guys are talking about, as far as I can tell.

[twobitshifter]

analog circuits are making a comeback because they are great for simulating the equations of the physical world more efficiently than a digital approach. https://spectrum.ieee.org/not-your-fathers-analog-computer

[sfnrm]

Sounds interesting. Do you have a link? (or at least a title?)

[ein0p]

Not at the moment, but I do recall he has a chapter on this in his book “The Art of Doing Science and Engineering”, which I also recommend. He uses very long transmission lines to explain this, but the same thing applies at the nano scale, and perhaps to an even greater extent due to the much noisier environment and higher frequencies.