Neurocomputing with optical pipeline networks

The authors present techniques for realizing connectionist-style neurocomputations on Opcom, an optical computer architecture based on the pipeline networking concept. The primary operation mode of Opcom is massively parallel and pipeline processing at the gate level. Special attention is paid to the implementation of the two most important functions of any neural network: learning and search (retrieval). The architecture of Opcom, the implementation of neural networks in Opcom, and the search process are discussed. The implementation of a supervised learning procedure, the pocket algorithm, is shown. Research results are summarized