Performance of an optoelectronic neural network in the presence of noise

Abstract

Optoelectronic neural networks must not only be highly parallel but also fast to compete with electrical systems. Receiver noise becomes an important consideration at high data rates; so the limits set by noise to network size and speed are analyzed. A network incorporating an array of high-speed multi-quantum-well modulators was constructed. It employed a general method for optical representation of bipolar values, which required only a minimal increase in network dimensions and gave the network immunity to common-mode parameter variations. Different ways of partitioning pattern-recognition problems were compared, and the accuracy of one configuration was tested with the experimental network over a range of noise levels.