A mixed parallel-sequential SHNN for large networks

  • A. Torralba
  • F. Colodro
  • L. G. Franquelo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 930)


This paper presents an architecture for the implementation of a large Stochastic Hopfield Neural Networks (SHNNs). The sequential SHNN, originally proposed in [1], takes a long time to convergence. On the other hand, the connection between chips limits to one hundred the number of neurons of the fully parallel SHNN proposed in [2]–[3]. A multichip approach proposed in this paper overcome both problems. The architecture, using a mixed parallel-sequential strategy, reduces the number of interconection lines to k while accelerates the convergence time of the network in [1] by a factor k. A partitioning problem is simulated to evaluate the behavior of the network.


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Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • A. Torralba
    • 1
  • F. Colodro
    • 1
  • L. G. Franquelo
    • 1
  1. 1.Dpto. de Ingeniería Electrónica, de Sistemas y AutomáticaEscuela Superior de IngenierosSevilla

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