A full-parallel implementation of Self-Organizing Maps on hardware

Leonardo A. Dias, Augusto M. P. Damasceno, Elena Gaura, Marcelo A.C. Fernandes

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Self-Organizing Maps (SOMs) are extensively used for data clustering and dimensionality reduction. However, if applications are to fully benefit from SOM based techniques, high-speed processing is demanding, given that data tends to be both highly dimensional and yet “big”. Hence, a fully parallel architecture for the SOM is introduced to optimize the system’s data processing time. Unlike most literature approaches, the architecture proposed here does not contain sequential steps - a common limiting factor for processing speed. The architecture was validated on FPGA and evaluated concerning hardware throughput and the use of resources. Comparisons to the state of the art show a speedup of 8.91x over a partially serial implementation, using less than 15% of hardware resources available. Thus, the method proposed here points to a hardware architecture that will not be obsolete quickly.
Original languageEnglish
JournalNeural Networks
Early online date21 May 2021
DOIs
Publication statusE-pub ahead of print - 21 May 2021

Funder

funded in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) - Institutional Program for Internationalization (CAPES - PrInt), Brazil.

Keywords

  • FPGA
  • Hardware
  • Parallel design
  • Self-Organizing Map

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

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