Training neural networks with end-to-end optical backpropagation

Understand

Optics is an exciting route for the next generation of computing hardware for machine learning, promising several orders of magnitude enhancement in both computational speed and energy efficiency.

• However, to reach the full capacity of an optical neural network it is necessary that the computing not only for the inference, but also for the training be implemented optically.
• The primary algorithm for training a neural network is backpropagation, in which the calculation is performed in the order opposite to the information flow for inference.
• While straightforward in a digital computer, optical implementation of backpropagation has so far remained elusive, particularly because of the conflicting requirements for the optical element that implements the nonlinear activation function.