Efficient GHA-based hardware architecture for texture classification

Shiow-Jyu Lin*, Yi Tsan Hung, Wen Jyi Hwang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper presents a novel hardware architecture based on generalized Hebbian algorithm (GHA) for texture classification. In the architecture, the weight vector updating process is separated into a number of stages for lowering area costs and increasing computational speed. Both the weight vector updating process and principle component computation process can also operate concurrently to further enhance the throughput. The proposed architecture has been embedded in a system-on-programmable-chip (SOPC) platform for physical performance measurement. Experimental results show that the proposed architecture is an efficient design for attaining both high speed performance and low area costs.

Original languageEnglish
Title of host publicationComputational Collective Intelligence
Subtitle of host publicationTechnologies and Applications - Second International Conference, ICCCI 2010, Proceedings
Pages203-212
Number of pages10
EditionPART 2
DOIs
StatePublished - 6 Dec 2010
Event2nd International Conference on Computational Collective Intelligence - Technologies and Applications, ICCCI 2010 - Kaohsiung, Taiwan
Duration: 10 Nov 201012 Nov 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6422 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Conference on Computational Collective Intelligence - Technologies and Applications, ICCCI 2010
CountryTaiwan
CityKaohsiung
Period10/11/1012/11/10

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