Artificial neural networks on reconfigurable meshes

Jing Fu Jenq, Wing Ning Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationParallel and Distributed Processing - 10 IPPS/SPDP 1998 Workshops Held in Conjunction with the 12th International Parallel Processing Symposium and 9th Symposium on Parallel and Distributed Processing, Proceedings
EditorsJose Rolim
PublisherSpringer Verlag
Pages234-242
Number of pages9
ISBN (Print)3540643591, 9783540643593
StatePublished - 1 Jan 1998
Event10 Workshops held in conjunction with 12th International Parallel Symposium and 9th Symposium on Parallel and Distributed Processing, IPPS/SPDP 1998 - Orlando, United States
Duration: 30 Mar 19983 Apr 1998

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume1388
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10 Workshops held in conjunction with 12th International Parallel Symposium and 9th Symposium on Parallel and Distributed Processing, IPPS/SPDP 1998
CountryUnited States
CityOrlando
Period30/03/983/04/98

Keywords

  • Artificial neural networks
  • Parallel algorithms
  • Reconfigurable mesh algorithms

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