Can someone help me Grok how the 3D printed Neural Networks back propagate? As I am trying to go through the paper they describe it as a pure optical approach, but what adjusts the refraction elements during the learning process?
"[...] learnable network parameter that is iteratively adjusted during the training process of the diffractive network, using an error back-propagation method. After this numerical training phase implemented in a computer, the D^2NN design is fixed and the transmission/reflection coefficients of the neurons of all the layers are determined. This D^2NN design, once physically fabricated using e.g., 3D-printing, 3lithography, etc., can then perform, at the speed of light propagation, the specific task that it is trained for, using only optical diffraction and passive optical components/layers, creating an efficient and fast way of implementing machine learning tasks."
Can't access the article, but the abstract reads like this is inference only. If that's so, this still could be useful. For instance, you could train a network somewhere else, then implement the learned function optically.
