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Parallel Implementations of Self-Organizing Maps

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Self-Organizing Neural Networks

Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 78))

Abstract

This chapter focuses on parallel implementations of the Self-Organizing Map (SOM) featuring different levels of parallelism. The basic arithmetic-logical operations of SOM are first reviewed for a consideration of implementation issues such as number precision, memory consumption and time complexity. Mapping involves network, training set, neuron and weight parallelism. Examples of the weight and neuron parallel mappings are given for abstract platforms to conduct general principles. Neuron parallel mapping is considered in great detail as it is the most commonly used approach. A review of implementations is given from supercomputers to VLSI (Very Large Scale Integration) chips with criteria for performance comparison.

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Authors
  1. Timo D. Hämäläinen
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Editors and Affiliations

  1. Faculty of Electrical Engineering, Technical Computer Science Group, University of Magdeburg, Universitätsplatz 2, 39106, Magdeburg, Germany

    Udo Seiffert

  2. Knowledge-Based Intelligent, Engineering Systems Centre, University of South Australia, Mawson Lakes, 5095, Adelaide, South Australia, Australia

    Lakhmi C. Jain

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© 2002 Springer-Verlag Berlin Heidelberg

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Hämäläinen, T.D. (2002). Parallel Implementations of Self-Organizing Maps. In: Seiffert, U., Jain, L.C. (eds) Self-Organizing Neural Networks. Studies in Fuzziness and Soft Computing, vol 78. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1810-9_11

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  • DOI: https://doi.org/10.1007/978-3-7908-1810-9_11

  • Publisher Name: Physica, Heidelberg

  • Print ISBN: 978-3-662-00343-5

  • Online ISBN: 978-3-7908-1810-9

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