1.15mW mixed-mode neuro-fuzzy accelerator for keypoint localization in image processing

Author

Injoon Hong ; Jinwook Oh ; Hoi-Jun Yoo

Author_Institution

Dept. of Electr. Eng., Korea Adv. Inst. of Sci. & Technol. (KAIST), Daejeon, South Korea

fYear

2011

fDate

7-10 Aug. 2011

Firstpage

1

Lastpage

4

Abstract

A mixed-mode neuro-fuzzy accelerator is proposed for keypoint localization of image features of Scale Invariant Feature Transform (SIFT) algorithm. To reduce processing time of keypoint localization with low power consumption, analog Adaptive Neuro-Fuzzy Inference System (ANFIS) and digital controller are implemented together. It is implemented in 0.13μm CMOS process and achieves 1.15mW power consumption. Compared to the conventional digital standalone system, 0.733mm² neuro-fuzzy accelerator achieves 43% processing time reduction and also results in 19.4% time reduction of image feature extraction process.

Keywords

feature extraction; fuzzy neural nets; transforms; CMOS process; analog adaptive neuro-fuzzy inference system; digital controller; digital standalone system; image feature extraction process; image processing; keypoint localization; low power consumption; mixed-mode neuro-fuzzy accelerator; power 1.15 mW; scale invariant feature transform algorithm; Noise measurement; Process control;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems (MWSCAS), 2011 IEEE 54th International Midwest Symposium on

Conference_Location

Seoul

ISSN

1548-3746

Print_ISBN

978-1-61284-856-3

Electronic_ISBN

1548-3746

Type

conf

DOI

10.1109/MWSCAS.2011.6026495

Filename

6026495