VLSI architecture for fast memetic vector quantizer design on reconfigurable hardware

Sheng Kai Weng, Chien Min Ou, Wen Jyi Hwang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A novel hardware architecture for memetic vector quantizer (VQ) design is presented in this paper. The architecture uses steady-state genetic algorithm (GA) for global search, and C-means algorithm for local refinement. It adopts a shift register based circuit for accelerating mutation and crossover operations for steady state GA operations. It also uses a pipeline architecture for the hardware implementation of C-means algorithm. The proposed architecture has been embedded in a softcore CPU for physical performance measurement. Experimental results show that the proposed architecture is an effective alternative for VQ optimization attaining both high performance and low computational time.

Original languageEnglish
Title of host publicationAlgorithms and Architectures for Parallel Processing - 9th International Conference, ICA3PP 2009, Proceedings
Pages513-524
Number of pages12
DOIs
Publication statusPublished - 2009 Sep 21
Event9th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2009 - Taipei, Taiwan
Duration: 2009 Jun 82009 Jun 11

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5574 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other9th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2009
CountryTaiwan
CityTaipei
Period09/6/809/6/11

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ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Weng, S. K., Ou, C. M., & Hwang, W. J. (2009). VLSI architecture for fast memetic vector quantizer design on reconfigurable hardware. In Algorithms and Architectures for Parallel Processing - 9th International Conference, ICA3PP 2009, Proceedings (pp. 513-524). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5574 LNCS). https://doi.org/10.1007/978-3-642-03095-6_49