Compact Architectures For Adaptive Neural Nets

Abstract

A compact optical architecture capable of implementing a number of adaptive neural net models is described. Compact architectures do not involve traditional optical imaging systems and are potentially rugged, easily constructed, and scalable. The critical devices of the generic architecture include 1-D electro-optic modulators and detectors to implement the neural processing elements, along with the Pockels readout optical modulator (PROM) to encode the analog weights. The architectural advancements include an input/output compatible method of handing both positive and negative values for the elements of the neuron activation vectors and the synaptic weight matrices during learning and recall. These hybrid architectures are capable of implementing linear and nonlinear associators with adaptive learning algorithms.