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Training a neural net with analog circuitry would mean an extra challenge in the reproducibility of results. That's currently already a challenge, tiny changes
by Fronzie 7y ago
Training a neural net with analog circuitry would mean an extra challenge in the reproducibility of results.
That's currently already a challenge, tiny changes have large differences in performance of the network.
- amelius 7y agoYes, but reproducibility is not always necessary. For example, drawing a simple anti-aliased line on a computer produces results that depend (at the pixel-level) on the software stack and or graphics card used. Nobody ever complained (much) about that. (Then again, it may allow your computer to be fingerprinted more easily).
- hatsunearu 7y agothing about transistors is that it's easy to have many transistors on the same IC behave in set ratios of each other (By changing the transistor width and length ratio, etc), but getting a performance metric exactly controlled is fairly difficult (not saying it's not possible, just something you try to avoid because cost, reliability and yield). I'd imagine any hypothetical implementation of a NN will be fairly difficult because of that. Also achieving linearity cheaply (especially multiplication) is very difficult.