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Proceedings Paper

Neural network simulation on a reduced-mesh-of-trees organization
Author(s): Manavendra Misra; V. K. Prasanna Kumar
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Paper Abstract

The theory of Artificial Neural Networks (ANN's) shows that ANN's can perform useful image recognition functions. Simulations on uniprocessor sequential machines, however, destroy the parallelism inherent in ANN models and this results in a significant loss of speed. Simulations on parallel machines are therefore essential to fully exploit the advantages of ANN's. We show how to simulate ANN's on an SIMD architecture, the Reduced Mesh of Trees (RMOT). The architecture has p PE's and n2 memory arranged in a p x p array of modules (p is a constant less than or equal to n). This massive memory is used to store connection weights. A fully connected, single layer neural network with n neurons can be mapped easily onto the architecture. An update in this case requires O(n2/p) time steps. A sparse network can also be simulated efficiently on the architecture. The proposed architecture can also be used for the efficient simulation of multilayer networks with a Back Propagation learning scheme. The architecture can easily be implemented within the framework of existing hardware technology.

Paper Details

Date Published: 1 July 1990
PDF: 12 pages
Proc. SPIE 1246, Parallel Architectures for Image Processing, (1 July 1990); doi: 10.1117/12.19586
Show Author Affiliations
Manavendra Misra, Univ. of Southern California (United States)
V. K. Prasanna Kumar, Univ. of Southern California (United States)

Published in SPIE Proceedings Vol. 1246:
Parallel Architectures for Image Processing
Joydeep Ghosh; Colin G. Harrison, Editor(s)

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