An Efficient Distributed Genetic Algorithm Architecture for Vector Quantizer Design



Wen-Jyi Hwang1, Chien-Min Ou *, 2, Peng-Chieh Hung1, Cheng-Yen Yang 1, Tun-Hao Yu 1
Department of Computer Science and Information Engineering, National Taiwan Normal University, Taipei, Taiwan, 117, R.O.C:
Department of Electronic Engineering, Ching Yun University, Taoyuan, Taiwan, 320, R.O.C


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© 2017 Ou et al.;

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Electronic Engineering, Ching Yun University, Chungli Taiwan, 320, R.O.C; Tel: 886-3-458-1196-5121; Fax: 886-2-458-8924; E-mail: cmou@cyu.edu.tw


Abstract

This paper presents a novel distributed genetic algorithm (GA) architecture for the design of vector quantizers. The design is based on a multi-core architecture, where each island of the GA is associated with a hardware accelerator and a softcore processor for independent genetic evolutions. An on-chip RAM with a mutex circuit is adopted for the migration of genetic strings among different islands. This allows a simple and flexible migration for the implementation of hardware distributed GA. Experimental results shows that the proposed architecture has significantly lower computational time as compared with its software counterparts running on multicore processors with multithreading for GA-based optimization.

Keywords: Distributed GA, SOPC, Multi-core system, Vector quantizers.