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The paper is linked at the bottom of the article: Deep physical neural networks enabled by a backpropagation algorithm for arbitrary physical systems -- https:/
by rch 5y ago
The paper is linked at the bottom of the article: Deep physical neural networks enabled by a backpropagation algorithm for arbitrary physical systems -- https://arxiv.org/abs/2104.13386 https://arxiv.org/abs/2104.13386
> physics-aware training (PAT)... allows us to efficiently and accurately execute the backpropagation algorithm on any sequence of physical input-output transformations, directly in situ.
- p1esk 5y agoJust looked at the paper - yep, looks like "chip in the loop" training method. This was very popular with analog NN accelerators in the 90s, and a few years ago I remember reading a paper which described an extension where they measured forward pass outputs at every layer on the chip (not just the final layer outputs as it's typically done). I fail to see much novelty here. I wanted to do that for my phd on hardware aware training (graduated before our chip was ready), but the disadvantages of the method are well-known - it's extremely slow (data has be pass between the chip and a GPU server on every iteration, and that's assuming you have access to each layer outputs.