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International Parallel Processing Symposium

IPPS 1998: Parallel and Distributed Processing pp 234–242Cite as

Artificial neural networks on reconfigurable meshes

  • Workshop on Biologically Inspired Solutions to Parallel Processing Problems Albert Y. Zomaya, The University of Western Australia Fikret Ercal, University of Missouri-Rolla Stephan Olariu, Old Dominion Univesity
  • Conference paper
  • First Online:

  • 115 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1388))

Abstract

Artificial neural networks(ANN) have been used successfully in applications such as pattern recognition, image processing, automation and control. Majority of today's applications use backpropagate feedforward ANN. In this paper, two methods of P pattern L layer ANN learning on n x n RMESH have been presented. One required memory space of O(nL) but conceptually is simpler to develop and the other uses pipelined approach which reduces the memory requirement to O(L). Both of these algorithms take O(PL) time and are optimal for RMESH architecture.

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Author information

Authors and Affiliations

  1. CS Department, Tennessee State University, USA

    Jing-Fu Jenq

  2. CS Department, University of Arkansas, USA

    Wing Ning Li

Authors
  1. Jing-Fu Jenq
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  2. Wing Ning Li
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Editor information

José Rolim

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© 1998 Springer-Verlag Berlin Heidelberg

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Jenq, JF., Ning Li, W. (1998). Artificial neural networks on reconfigurable meshes. In: Rolim, J. (eds) Parallel and Distributed Processing. IPPS 1998. Lecture Notes in Computer Science, vol 1388. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-64359-1_693

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  • DOI: https://doi.org/10.1007/3-540-64359-1_693

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-64359-3

  • Online ISBN: 978-3-540-69756-5

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