  As neural networks grow larger and more complex and data-hungry, training
costs are skyrocketing. Especially when lifelong learning is necessary, such as
in recommender systems or self-driving cars, this might soon become
unsustainable. In this study, we present the first optical co-processor able to
accelerate the training phase of digitally-implemented neural networks. We rely
on direct feedback alignment as an alternative to backpropagation, and perform
the error projection step optically. Leveraging the optical random projections
delivered by our co-processor, we demonstrate its use to train a neural network
for handwritten digits recognition.
