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Parallel Implementation of Self-Organizing Map on the Partial Tree Shape Neurocomputer

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Abstract

A parallel mapping of self-organizing map (SOM) algorithm is presented for a partial tree shape neurocomputer (PARNEU). PARNEU is a general purpose parallel neurocomputer that is designed for soft computing applications. Practical scalability and a reconfigurable partial tree network are the main architectural features. The presented neuron parallel mapping of SOM with on-line learning illustrates a parallel winner neuron search and a coordinate transfer that are performed in the partial tree network. Phase times are measured to analyse speedup and scalability of the mapping. The performance of the learning phase in SOM with a four processor PARNEU configuration is about 26 MCUPS and the recall phase performs 30 MCPS. Compared to other mappings done for general purpose neurocomputers, PARNEU's performance is very good.

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Authors and Affiliations

  1. Signal Processing Laboratory, Tampere University of Technology, Hermiankatu 12 C, FIN-33720, Tampere, Finland

    Pasi Kolinummi & Pasi Pulkkinen

  2. Signal Processing Laboratory, Tampere University of Technology, Hermiankatu 12 C, FIN-33720, Tampere, Finland

    Timo Hämäläinen

  3. Signal Processing Laboratory, Tampere University of Technology, Hermiankatu 12 C, FIN-33720, Tampere, Finland

    Jukka Saarinen

Authors
  1. Pasi Kolinummi
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  2. Pasi Pulkkinen
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  3. Timo Hämäläinen
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  4. Jukka Saarinen
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Kolinummi, P., Pulkkinen, P., Hämäläinen, T. et al. Parallel Implementation of Self-Organizing Map on the Partial Tree Shape Neurocomputer. Neural Processing Letters 12, 171–182 (2000). https://doi.org/10.1023/A:1009665814041

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