Four-quadrant optical matrix-vector multiplication machine as a neural-network processor

Optical processors for neural networks are primarily fast matrix-vector multiplication machines that can potentially compete with serial computers owing to their parallelism and their ability to facilitate densely connected networks. However, in most proposed systems the multiplication supports only two quadrants and is thus unable to provide bipolar neuron outputs for increasing network capabilities and learning rate. We propose and demonstrate an opto-electronic four-quadrant matrix-vector multiplier that can be used for feed-forward neural-network recall and learning. Experimental results obtained with common commercial components demonstrate a novel, useful, and reliable approach for four-quadrant matrix-vector multiplication in general and for feed-forward neural-network training and recall in particular.