Optical and systolic implementation of an artificial neural network

Susamma Barua

doi:10.1117/12.983197

2 February 1993 Optical and systolic implementation of an artificial neural network

Susamma Barua

Proceedings Volume 1773, Photonics for Computers, Neural Networks, and Memories; (1993) https://doi.org/10.1117/12.983197
Event: San Diego '92, 1992, San Diego, CA, United States

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.

Citation Download Citation

Susamma Barua "Optical and systolic implementation of an artificial neural network", Proc. SPIE 1773, Photonics for Computers, Neural Networks, and Memories, (2 February 1993); https://doi.org/10.1117/12.983197

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