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Proceedings Paper

A new architecture for all-optical neural network computing
Author(s): Y. Hayasaki
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Paper Abstract

The main features of artificial neural networks are a large number of nonlinear processing elements and massively parallel interconnections among themselves. Many researchers have studied hardware of such neural artificial networks and software for highly parallel computing. In terms of the hardware, two different approaches, VLSI techniques and optical neural networks.have been proposed. Basic neural operations in a simple artificial neural network model are based on a spatial weight sum operation, including arithmetic operation and addition, and a nonlinear operation. In each neuron, the synaptic weights and the input signals form other neurons are multiplied, and their sum is subjected by a nonlinear operation to obtain an output. In a general neural network model, arithmetic operations in a neuron include subtraction and negative multiplication, because of bipolar weights corresponding to excitatory weights and inhibitory weights.

Paper Details

Date Published: 26 July 1993
PDF: 2 pages
Proc. SPIE 1983, 16th Congress of the International Commission for Optics: Optics as a Key to High Technology, 19835G (26 July 1993); doi: 10.1117/12.2308615
Show Author Affiliations
Y. Hayasaki, Univ. of Tsukuba (Japan)


Published in SPIE Proceedings Vol. 1983:
16th Congress of the International Commission for Optics: Optics as a Key to High Technology
Gyorgy Akos; Tivadar Lippenyi; Gabor Lupkovics; Andras Podmaniczky, Editor(s)

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