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

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

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 controll...

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Bibliographische Detailangaben
Hauptverfasser: Injoon Hong, Jinwook Oh, Hoi-Jun Yoo
Format: Tagungsbericht
Sprache:eng
Schlagworte:
Noise measurement
Process control
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Beschreibung
Zusammenfassung: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, 0.733mm 2 neuro-fuzzy accelerator achieves 43% processing time reduction and also results in 19.4% time reduction of image feature extraction process.
ISSN:1548-3746
1558-3899
DOI:10.1109/MWSCAS.2011.6026495