Efficient GHA-based hardware architecture for texture classification

Shiow-Jyu Lin; Yi Tsan Hung; Wen Jyi Hwang

doi:10.1007/978-3-642-16732-4_22

Efficient GHA-based hardware architecture for texture classification

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

^*Corresponding author for this work

Institute of Lighting and Energy Photonics

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-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 language	English
Title of host publication	Computational Collective Intelligence
Subtitle of host publication	Technologies and Applications - Second International Conference, ICCCI 2010, Proceedings
Pages	203-212
Number of pages	10
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-16732-4_22
State	Published - 6 Dec 2010
Event	2nd International Conference on Computational Collective Intelligence - Technologies and Applications, ICCCI 2010 - Kaohsiung, Taiwan Duration: 10 Nov 2010 → 12 Nov 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	6422 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Conference on Computational Collective Intelligence - Technologies and Applications, ICCCI 2010
Country	Taiwan
City	Kaohsiung
Period	10/11/10 → 12/11/10

Access to Document

10.1007/978-3-642-16732-4_22

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Lin, S-J., Hung, Y. T., & Hwang, W. J. (2010). Efficient GHA-based hardware architecture for texture classification. In Computational Collective Intelligence: Technologies and Applications - Second International Conference, ICCCI 2010, Proceedings (PART 2 ed., pp. 203-212). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6422 LNAI, No. PART 2). https://doi.org/10.1007/978-3-642-16732-4_22

Lin, Shiow-Jyu ; Hung, Yi Tsan ; Hwang, Wen Jyi. / Efficient GHA-based hardware architecture for texture classification. Computational Collective Intelligence: Technologies and Applications - Second International Conference, ICCCI 2010, Proceedings. PART 2. ed. 2010. pp. 203-212 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

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title = "Efficient GHA-based hardware architecture for texture classification",

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.",

author = "Shiow-Jyu Lin and Hung, {Yi Tsan} and Hwang, {Wen Jyi}",

year = "2010",

month = dec,

day = "6",

doi = "10.1007/978-3-642-16732-4_22",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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note = "null ; Conference date: 10-11-2010 Through 12-11-2010",

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Lin, S-J, Hung, YT & Hwang, WJ 2010, Efficient GHA-based hardware architecture for texture classification. in Computational Collective Intelligence: Technologies and Applications - Second International Conference, ICCCI 2010, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 6422 LNAI, pp. 203-212, 2nd International Conference on Computational Collective Intelligence - Technologies and Applications, ICCCI 2010, Kaohsiung, Taiwan, 10/11/10. https://doi.org/10.1007/978-3-642-16732-4_22

Efficient GHA-based hardware architecture for texture classification. / Lin, Shiow-Jyu; Hung, Yi Tsan; Hwang, Wen Jyi.

Computational Collective Intelligence: Technologies and Applications - Second International Conference, ICCCI 2010, Proceedings. PART 2. ed. 2010. p. 203-212 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6422 LNAI, No. PART 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Hung, Yi Tsan

AU - Hwang, Wen Jyi

PY - 2010/12/6

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N2 - 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.

AB - 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.

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DO - 10.1007/978-3-642-16732-4_22

M3 - Conference contribution

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SN - 9783642167317

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Lin S-J, Hung YT, Hwang WJ. Efficient GHA-based hardware architecture for texture classification. In Computational Collective Intelligence: Technologies and Applications - Second International Conference, ICCCI 2010, Proceedings. PART 2 ed. 2010. p. 203-212. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). https://doi.org/10.1007/978-3-642-16732-4_22

Efficient GHA-based hardware architecture for texture classification

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