A 2.71 nJ/Pixel Gaze-Activated Object Recognition System for Low-Power Mobile Smart Glasses

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dc.contributor.authorHong, Injoonko
dc.contributor.authorBong, Kyeongryeolko
dc.contributor.authorShin, Dongjooko
dc.contributor.authorPark, Seongwookko
dc.contributor.authorLee, Kyuho Jasonko
dc.contributor.authorKim, Youchangko
dc.contributor.authorYoo, Hoi-Junko
dc.date.accessioned2016-06-07T09:04:15Z-
dc.date.available2016-06-07T09:04:15Z-
dc.date.created2016-02-01-
dc.date.created2016-02-01-
dc.date.issued2016-01-
dc.identifier.citationIEEE JOURNAL OF SOLID-STATE CIRCUITS, v.51, no.1, pp.45 - 55-
dc.identifier.issn0018-9200-
dc.identifier.urihttp://hdl.handle.net/10203/207723-
dc.description.abstractA low-power object recognition (OR) system with intuitive gaze user interface (UI) is proposed for battery-powered smart glasses. For low-power gaze UI, we propose a low-power single-chip gaze estimation sensor, called gaze image sensor (GIS). In GIS, a novel column-parallel pupil edge detection circuit (PEDC) with new pupil edge detection algorithm XY pupil detection (XY-PD) is proposed which results in 2.9x power reduction with 16x larger resolution compared to previous work. Also, a logarithmic SIMD processor is proposed for robust pupil center estimation, <1 pixel error, with low-power floating-point implementation. For OR, low-power multicore OR processor (ORP) is implemented. In ORP, task-level pipeline with keypoint-level scoring is proposed to reduce the number of cores as well as the operating frequency of keypoint-matching processor (KMP) for low-power consumption. Also, dual-mode convolutional neural network processor (CNNP) is designed for fast tile selection without external memory accesses. In addition, a pipelined descriptor generation processor (DGP) with LUT-based nonlinear operation is newly proposed for low-power OR. Lastly, dynamic voltage and frequency scaling (DVFS) for dynamic power reduction in ORP is applied. Combining both of the GIS and ORP fabricated in 65 nm CMOS logic process, only 75 mW average power consumption is achieved with real-time OR performance, which is 1.2x and 4.4x lower power than the previously published work.-
dc.languageEnglish-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.subjectPROCESSOR-
dc.titleA 2.71 nJ/Pixel Gaze-Activated Object Recognition System for Low-Power Mobile Smart Glasses-
dc.typeArticle-
dc.identifier.wosid000367719400005-
dc.identifier.scopusid2-s2.0-84943184281-
dc.type.rimsART-
dc.citation.volume51-
dc.citation.issue1-
dc.citation.beginningpage45-
dc.citation.endingpage55-
dc.citation.publicationnameIEEE JOURNAL OF SOLID-STATE CIRCUITS-
dc.identifier.doi10.1109/JSSC.2015.2476786-
dc.contributor.localauthorYoo, Hoi-Jun-
dc.type.journalArticleArticle-
dc.subject.keywordAuthorConvolutional neural network (CNN)-
dc.subject.keywordAuthordynamic voltage and frequency scaling (DVFS)-
dc.subject.keywordAuthoreye tracking-
dc.subject.keywordAuthorfocal-plane processing-
dc.subject.keywordAuthorgaze estimation-
dc.subject.keywordAuthorlogarithmic approximation-
dc.subject.keywordAuthorobject recognition (OR)-
dc.subject.keywordAuthorsmart glasses-
dc.subject.keywordAuthorvision chip-
dc.subject.keywordPlusPROCESSOR-
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