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Proceedings Paper

Optical and systolic implementation of an artificial neural network
Author(s): Susamma Barua
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Paper Abstract

The optical implementation of a locally interconnected systolic architecture of a neural network is considered in this paper. In the design presented here, the Hopfield model, one of the widely researched artificial neural network, is formulated as a consecutive matrix-vector multiplication problem with some prespecified threshold operations. The multiplication array structure is derived from a cascaded dependence graph with nonlinear assignment. By the same nonlinear assignment, a locally interconnected systolic array with bidirectional communicational links is then obtained. Each processing element in the systolic array is treated as a neuron and the synaptic strengths are stored in it. The optical design employs a liquid crystal light valve (LCLV) structure to implement the matrix-vector multiplier. The paper will show that the optical and systolic implementation of the neural networks achieves a higher precision in computation.

Paper Details

Date Published: 2 February 1993
Proc. SPIE 1773, Photonics for Computers, Neural Networks, and Memories, (2 February 1993); doi: 10.1117/12.983197
Show Author Affiliations
Susamma Barua, California State Univ./Fullerton (United States)

Published in SPIE Proceedings Vol. 1773:
Photonics for Computers, Neural Networks, and Memories
Stephen T. Kowel; John A. Neff; William J. Miceli, Editor(s)

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