[mikewarot]

Deep networks these days can have hundreds of layers. Any non-digital system is going to pick up noise and gain variations at each stage. It might be possible to get around this with threshold circuits.

The problem then becomes training. The algorithm of choice is back propagation, which requires determining derivatives across the whole network. Doing training on an analog system would require tweaking each value as a batch of values is input multiple times to find the slope. This is impractical for large networks, as training usually requires a billion rounds.