I'll start off by saying that this type of research is very interesting, and in this case well done. However, the issue to me in "all optical neural nets" has a

I'll start off by saying that this type of research is very interesting, and in this case well done. However, the issue to me in "all optical neural nets" has always been the non-linearity. The non-lineartiy (or activation function) is crucial to the high performance we see with deep neural networks. For example, on the task of interest here (MNIST, a classic hand-written digit classification task), a old-school linear classifier can get between 88% and 92.4% accuracy [1]. Whereas non-linear neural networks easily achieve 95%+ [1]. So until they figure out a way to represent a nice non-linearity (unless I missed something), these all optical techniques are at a permanant disadvantage. [1] http://yann.lecun.com/exdb/mnist/ http://yann.lecun.com/exdb/mnist/