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Simulating neural networks on telmat T-node

  • Conference paper
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High-Performance Computing and Networking (HPCN-Europe 1994)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 796))

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Abstract

Simulation programs for neural networks are notorious for being intensive in computations. This poster presents a toroidal lattice architecture used to simulate fully connected neural networks. An attempt is made to see the problem in its global complexity as the system with its own behaviour. First problem is defined, then the effective solution by virtual processors arranged in toroidal lattice architecture is proposed. Then decomposition, data distribution and mapping issues are explained for physical message passing architecture. Finally experimental results are presented.

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References

  1. Fujimoto, Y., Fukuda, N., Akabane, T.: Massively Parallel Architectures for Large Scale Neural Networks Simulations, IEEE Transactions on Neural Networks, vol. 3, No. 6, (Nov.1992), 876–887

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  2. Paugam-Moisy, H.: A Spy of Parallel Neural Networks Tech. rep. 90-27 Ecole Normale Superieure de Lyon, IMAG, (1990)

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Authors and Affiliations

  1. Dept. of Control Engineering, Czech Technical University in Prague, Karlovo nam 13, 121 35, Prague 2, Czech Republic

    Zdenek Hanzalek

Authors
  1. Zdenek Hanzalek
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Wolfgang Gentzsch Uwe Harms

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© 1994 Springer-Verlag Berlin Heidelberg

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Hanzalek, Z. (1994). Simulating neural networks on telmat T-node. In: Gentzsch, W., Harms, U. (eds) High-Performance Computing and Networking. HPCN-Europe 1994. Lecture Notes in Computer Science, vol 796. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0020408

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  • DOI: https://doi.org/10.1007/BFb0020408

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-57980-9

  • Online ISBN: 978-3-540-48406-6

  • eBook Packages: Springer Book Archive

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