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Progress in Neural Processing - Vol. 3

PARALLEL IMPLEMENTATIONS OF BACKPROPAGATION NEURAL NETWORKS ON TRANSPUTERS: A STUDY OF TRAINING SET PARALLELISM

by P Saratchandran, N Sundararajan & S K Foo (NTU, S'pore)

This book presents a systematic approach to parallel implementation of feedforward neural networks on an array of transputers. The emphasis is on backpropagation learning and training set parallelism. Using systematic analysis, a theoretical model has been developed for the parallel implementation. The model is used to find the optimal mapping to minimize the training time for large backpropagation neural networks. The model has been validated experimentally on several well known benchmark problems. Use of genetic algorithms for optimizing the performance of the parallel implementations is described. Guidelines for efficient parallel implementations are highlighted.


Contents:

  • Introduction
  • Transputer Topologies for Parallel Implementation
  • Development of a Theoretical Model for Training Set Parallelism in a Homogeneous Array of Transputers
  • Equal Distribution of Patterns Amongst a Homogeneous Array of Transputers
  • Optimization Model for Unequal Distribution of Patterns in a Homogeneous Array of Transputers
  • Pattern Allocation Schemes Using Genetic Algorithm
  • Bibliography
  • Index


Readership: Graduate students, research scientists and practising engineers in artificial neural networks and parallel computing.

220pp Pub. date: Jul 1996
ISBN 978-981-02-2654-1
981-02-2654-3
US$38 / £26


Copyright © 2008 World Scientific Publishing Co. All rights reserved.
Updated on 19 August 2008