Mapping of artificial neural networks onto message passing systems

Kumar, MJ and Patnaik, LM (1996) Mapping of artificial neural networks onto message passing systems. In: IEEE Transactions on Systems Man And Cybernetics Part B-Cybernetics, 26 (6). pp. 822-835.

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Abstract

Various Artificial Neural Networks (ANN's) have been proposed in recent years to mimic the human brain in solving problems involving human-like intelligence, Efficient mapping of ANN's comprising of large number of neurons onto various distributed MIMD architectures is discussed in this paper, The massive interconnection among neurons demands a communication-efficient architecture. Issues related to the suitability of MIMD architectures for simulating neural networks are discussed, Performance analysis of ring, torus, binary tree, hypercube, and extended hypercube for simulating artificial neural networks is presented. Our studies reveal that the performance of the extended hypercube is better than those of ring, torus, binary tree, and hypercube topologies.

Item Type:	Journal Article
Publication:	IEEE Transactions on Systems Man And Cybernetics Part B-Cybernetics
Publisher:	IEEE-Inst Electrical Electronics Engineers
Additional Information:	Copyright 1996 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Keywords:	artificialneural networks;backpropagation;extended hypercube;multiprocessors;multinode broadcast;total exchange
Department/Centre:	Division of Electrical Sciences > Electronic Systems Engineering (Formerly Centre for Electronic Design & Technology)
Date Deposited:	09 Dec 2009 05:54
Last Modified:	28 Nov 2018 15:13
URI:	http://eprints.iisc.ac.in/id/eprint/19078

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