Specifically to the point of the comparatively low reliability / high variation of analog systems: an interesting property of neural nets is that they can be robust relative to noise when trained with the same type of noise witnessed under inference.
Whether or not speed/etc. would be better in the digital vs. analog design-space, it's an interesting thing to consider that neural nets can automatically account for the encoding-medium's variability. This perhaps makes neural solutions a good fit for low power analog media which otherwise aren't useful for classical computing.
Whether or not speed/etc. would be better in the digital vs. analog design-space, it's an interesting thing to consider that neural nets can automatically account for the encoding-medium's variability. This perhaps makes neural solutions a good fit for low power analog media which otherwise aren't useful for classical computing.
See https://arxiv.org/abs/2104.13386 for an exploration of physically encoding neural architectures.