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Proceedings Paper

Optical Realizations Of Neural Network Models
Author(s): Demetri Psaltis
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Paper Abstract

The optical implementation of computing systems whose structure and function are motivated by natural in-telligence systems is a subject that involves optical computing and neural network models for computation. These are two areas that have individually received attention in yecent years and they share the common property that they promise to provide solutions for fundamental problems in computation. In the case of optical computers the limitation that is being addressed is communication. With optics it is possible to have large arrays of processing elements communicating with each other without connecting a wire between each pair. The need to provide wires for communication in an electronic circuit is perceived as a major technological limitation of VLSI [1]. The primary justification for optical computing is therefore to extend the communication capability in computers [2,3]. It is not clear however precisely how this global communication capability can be put to good use. Through free space inter-connects and volume holograms we can have thousands of computing elements all talking to each other at the same time. Is it possible to do useful computation in such a system? We look at neural network models for an answer to this question.

Paper Details

Date Published: 8 January 1987
PDF: 5 pages
Proc. SPIE 0700, 1986 Intl Optical Computing Conf, (8 January 1987); doi: 10.1117/12.936987
Show Author Affiliations
Demetri Psaltis, California Institute of Technology (United States)


Published in SPIE Proceedings Vol. 0700:
1986 Intl Optical Computing Conf
Asher A. Friesem; Emanuel Marom; Joseph Shamir, Editor(s)

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