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A mixed parallel-sequential SHNN for large networks

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From Natural to Artificial Neural Computation (IWANN 1995)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 930))

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Abstract

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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References

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Authors and Affiliations

  1. Dpto. de Ingeniería Electrónica, de Sistemas y Automática, Escuela Superior de Ingenieros, Avda. Reina Mercedes, s/n, 41012, Sevilla

    A. Torralba, F. Colodro & L. G. Franquelo

Authors
  1. A. Torralba
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  2. F. Colodro
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  3. L. G. Franquelo
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Editor information

José Mira Francisco Sandoval

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© 1995 Springer-Verlag Berlin Heidelberg

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Torralba, A., Colodro, F., Franquelo, L.G. (1995). A mixed parallel-sequential SHNN for large networks. In: Mira, J., Sandoval, F. (eds) From Natural to Artificial Neural Computation. IWANN 1995. Lecture Notes in Computer Science, vol 930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-59497-3_251

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  • DOI: https://doi.org/10.1007/3-540-59497-3_251

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-59497-0

  • Online ISBN: 978-3-540-49288-7

  • eBook Packages: Springer Book Archive

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