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Adaptive cooperation between processors in a parallel Boltzmann machine implementation

  • Bio-inspired Systems
  • Conference paper
  • First Online:
Engineering Applications of Bio-Inspired Artificial Neural Networks (IWANN 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1607))

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Abstract

The fine grain, data-driven parallelism shown by neural models as the Boltzmann machine cannot be implemented in an entirely efficient way either in general-purpose multicomputers or in networks of computers, which are nowadays the most common parallel computer architectures.

In this paper we present a parallel implementation of a modified Boltzmann machine where the processors, with disjoint subsets of neurons allocated, asynchronously compute the evolution of their neurons by using values that might not be updated for the remaining neurons, thus reducing interprocessor communication requirements. An evolutionary algorithm is used to learn the rules that allow the processors to cooperate by interchanging the local optima that they find while concurrently exploring different zones of the Boltzmann machine state space. Thus, the way the processors interact changes dynamically during execution of the algorithm, adapted to the problem at hand. Good figures for speedup with respect to the Boltzmann machine computation in a uniprocessor computer have been experimentally obtained.

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Authors and Affiliations

  1. Departamento de Arquitectura y Tecnología de Computadores, Universidad de Granada, 18071, Granada

    J. Ortega, L. Parrilla, J. L. Bernier, C. Gil, B. Pino & M. Anguita

Authors
  1. J. Ortega
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  2. L. Parrilla
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  3. J. L. Bernier
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  4. C. Gil
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  5. B. Pino
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  6. M. Anguita
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Editor information

José Mira Juan V. Sánchez-Andrés

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© 1999 Springer-Verlag Berlin Heidelberg

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Ortega, J., Parrilla, L., Bernier, J.L., Gil, C., Pino, B., Anguita, M. (1999). Adaptive cooperation between processors in a parallel Boltzmann machine implementation. In: Mira, J., Sánchez-Andrés, J.V. (eds) Engineering Applications of Bio-Inspired Artificial Neural Networks. IWANN 1999. Lecture Notes in Computer Science, vol 1607. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0100487

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  • DOI: https://doi.org/10.1007/BFb0100487

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-66068-2

  • Online ISBN: 978-3-540-48772-2

  • eBook Packages: Springer Book Archive

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