Interesting! Your vinyl analogy is helpful in a few ways. For one, training the memristive networks wouldn't be parallelizable in the same ways that training state of the art digital networks's can be. Right now, you can't just instantly spin up thousands of instances of some memristive hardware without a lot of physical infrastructure. Of course if we start seeing programmable circuits with vast num ers of programmable memristors, maybe this will change.
Also: I would have assumed (naively) that the memristive networks could be "faster", but now I'm realizing that this is going to be a function of how quickly the memristors transition. There are likely to be some complex timing issues in building analog neural networks that behave in remotely predictable ways. I could imagine that errors might compound in much stranger (more chaotic) ways than they would in digital networks, where "error" is pretty tame.
I imagine that the "real-time", asynchronous, and chaotic nature of analog neural networks could be seen, by the right person, in the right light, probably in the future, as a feature (not a bug). But I realize now that we're decades away from that.
My hunch is that the early practical research will be done on FPGAs with inboard memristors.
Also: I would have assumed (naively) that the memristive networks could be "faster", but now I'm realizing that this is going to be a function of how quickly the memristors transition. There are likely to be some complex timing issues in building analog neural networks that behave in remotely predictable ways. I could imagine that errors might compound in much stranger (more chaotic) ways than they would in digital networks, where "error" is pretty tame.
I imagine that the "real-time", asynchronous, and chaotic nature of analog neural networks could be seen, by the right person, in the right light, probably in the future, as a feature (not a bug). But I realize now that we're decades away from that.
My hunch is that the early practical research will be done on FPGAs with inboard memristors.