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Proceedings Paper

Optically Connected Multiprocessors For Simulating Artificial Neural Networks
Author(s): Joydeep Ghosh; Kai Hwang
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Paper Abstract

This paper investigates the architectural requirements in simulating large neural networks using a highly parallel multiprocessor with distributed memory and optical interconnects. First, we model the structure of a neural network and the functional behavior of individual cells. These models are used to estimate the volume of messages that need to be exchanged among physical processors to simulate the weighted connections of the neural network. The distributed processor/memory organization is tailored to an electronic implementation for greater versatility and flexibility. Optical interconnects are used to satisfy the interprocessor communication bandwidth demands. The hybrid implementation attempts to balance the processing, memory and bandwidth demands in simulating asynchronous, value-passing models for cooperative parallel computation with self-learning capabilities.

Paper Details

Date Published: 3 May 1988
PDF: 10 pages
Proc. SPIE 0882, Neural Network Models for Optical Computing, (3 May 1988); doi: 10.1117/12.944094
Show Author Affiliations
Joydeep Ghosh, University of Southern California (United States)
Kai Hwang, University of Southern California (United States)


Published in SPIE Proceedings Vol. 0882:
Neural Network Models for Optical Computing
Ravindra A. Athale; Joel Davis, Editor(s)

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