[dahart]

One of the potentially large applications for optical analog computing is for training neural networks, which is an application where noise is a feature and is tolerable. People are already using noise (and low precision) intentionally for regularization and also doing things like intentionally not synchronizing GPU kernels for performance, which causes the inputs for the next round to be in a potentially random state when read, and noticing that the regularization side effects on the network are positive.