1) Noise is an issue as the system gets complex. You can't get away with counting to 1 anymore, all those levels in between matter.
2) Its hard to make an analog computer reconfigurable.
3) Analog computers exist commercially believe it or not, but for niche applications and essentially as coprocessors.
Quantization of parameters in neural networks is roughly analogous to introducing noise into analog signals. We’ve got good evidence that these architectures are robust to quantization - which implies they could be implemented over noisy analog signals.
Not sure who’s working on that but I can’t believe it’s not being examined.
1) Noise is an issue as the system gets complex. You can't get away with counting to 1 anymore, all those levels in between matter. 2) Its hard to make an analog computer reconfigurable. 3) Analog computers exist commercially believe it or not, but for niche applications and essentially as coprocessors.