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      KCI등재후보

      1b-16b Variable Bit Precision DNN Processor for Emotional HRI System in Mobile Devices

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      https://www.riss.kr/link?id=A107131984

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      다국어 초록 (Multilingual Abstract)

      We propose an energy-efficient DNN processor with the proposed look-up-table-based processing engine (LPE) and near-zero skipper. A CNN-based facial emotion recognition model and an RNN-based emotional dialogue generation model are integrated for the natural human-robot interaction (HRI) system, and it is evaluated by the proposed processor. LPE supports 1 to 16 bit variable weight bit precision, and it achieves 57.6% and 28.5% lower energy consumption than the conventional multiplier-accumulator (MAC) units in 1-16 bit weight precision. Furthermore, the near-zero skipper reduces 36% of MAC operations and consumes 28% lower energy consumption in facial emotion recognition tasks. Implemented in 65 nm CMOS process, the proposed processor occupies 1784×1784 μm2 areas and dissipates 0.28 mW and 34.4 mW at 1 frame-per-second (fps) and 30 fps facial emotion recognition tasks.
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      We propose an energy-efficient DNN processor with the proposed look-up-table-based processing engine (LPE) and near-zero skipper. A CNN-based facial emotion recognition model and an RNN-based emotional dialogue generation model are integrated for the ...

      We propose an energy-efficient DNN processor with the proposed look-up-table-based processing engine (LPE) and near-zero skipper. A CNN-based facial emotion recognition model and an RNN-based emotional dialogue generation model are integrated for the natural human-robot interaction (HRI) system, and it is evaluated by the proposed processor. LPE supports 1 to 16 bit variable weight bit precision, and it achieves 57.6% and 28.5% lower energy consumption than the conventional multiplier-accumulator (MAC) units in 1-16 bit weight precision. Furthermore, the near-zero skipper reduces 36% of MAC operations and consumes 28% lower energy consumption in facial emotion recognition tasks. Implemented in 65 nm CMOS process, the proposed processor occupies 1784×1784 μm2 areas and dissipates 0.28 mW and 34.4 mW at 1 frame-per-second (fps) and 30 fps facial emotion recognition tasks.

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      참고문헌 (Reference)

      1 Suja, P, "Real-time emotion recognition from facial images using Raspberry Pi II" IEEE 2016

      2 Jiang, Shiqi, "Memento: An Emotion-driven Lifelogging System with Wearables" 15 (15): 8-, 2019

      3 C. Pierre-Luc, "FER-2013 face database" Universitde Montral 2013

      4 Li, Shan, "Deep facial expression recognition: A survey"

      5 Hazarika, Devamanyu, "Conversational memory network for emotion recognition in dyadic dialogue videos" 1 : 2018

      1 Suja, P, "Real-time emotion recognition from facial images using Raspberry Pi II" IEEE 2016

      2 Jiang, Shiqi, "Memento: An Emotion-driven Lifelogging System with Wearables" 15 (15): 8-, 2019

      3 C. Pierre-Luc, "FER-2013 face database" Universitde Montral 2013

      4 Li, Shan, "Deep facial expression recognition: A survey"

      5 Hazarika, Devamanyu, "Conversational memory network for emotion recognition in dyadic dialogue videos" 1 : 2018

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      학술지 이력

      학술지 이력
      연월일 이력구분 이력상세 등재구분
      2022 평가예정 계속평가 신청대상 (계속평가)
      2020-01-01 평가 등재후보학술지 선정 (신규평가) KCI등재후보
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