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

6 Citations (Scopus)
82 Downloads (Pure)

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
Pages (from-to)818-827
Number of pages10
JournalNeural Networks
Volume143
Early online date21 May 2021
DOIs
Publication statusPublished - Nov 2021

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Neural Networks. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Neural Networks, 143, (2021) DOI: 10.1016/j.neunet.2021.05.021

© 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/

Funder

Funded in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) - Institutional Program for Internationalization (CAPES - PrInt), Brazil and EPSRC Grant EP/S031863/1.

Keywords

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

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

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