Hoi-Jun Yoo

Hoi-Jun Yoo

Korea Advanced Institute of Science and Technology

Professor

School of Electrical Engineering

Research Area

  • #Computer science
  • #CMOS
  • #Computer hardware
  • #Electrical engineering
  • #Artificial intelligence
  • #Electronic engineering
  • #Engineering
  • #Auxiliary memory
  • #Convolutional neural network
  • #Chip

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Related papers to
‘ Computer science ‘ : 49

  • UNPU: An Energy-Efficient Deep Neural Network Accelerator With Fully Variable Weight Bit Precision

    2019/01

    4.9 Impact Factor

    96 citations

    Jinmook Lee, Changhyeon Kim, Sanghoon Kang, Dongjoo Shin, Sangyeob Kim, Hoi-Jun Yoo

    DOI : 10.1109/JSSC.2018.2865489

    • #Computer science
    • #Artificial intelligence
    • #Computer hardware
    • #Artificial neural network
    • #CMOS
    • #Energy consumption
    • #Efficient energy use
    • #Lookup table
    • #Electrical efficiency
    • #16-bit
    • #Deep learning

All papers authored by
‘ Hoi-Jun Yoo ’ : 70

  • UNPU: An Energy-Efficient Deep Neural Network Accelerator With Fully Variable Weight Bit Precision

    2019/01
    IEEE JOURNAL OF SOLID-STATE CIRCUITS

    4.9 Impact Factor

    96 citations

    Jinmook Lee, Changhyeon Kim, Sanghoon Kang, Dongjoo Shin, Sangyeob Kim, Hoi-Jun Yoo

    DOI : 10.1109/JSSC.2018.2865489

    • #Computer science
    • #Artificial intelligence
    • #Computer hardware
    • #Artificial neural network
    • #CMOS
    • #Energy consumption
    • #Efficient energy use
    • #Lookup table
    • #Electrical efficiency
    • #16-bit
    • #Deep learning

Related papers to
‘ Computer science ‘ : 49

  • UNPU: An Energy-Efficient Deep Neural Network Accelerator With Fully Variable Weight Bit Precision

    2019/01
    IEEE JOURNAL OF SOLID-STATE CIRCUITS

    4.9 Impact Factor

    96 citations

    Jinmook Lee, Changhyeon Kim, Sanghoon Kang, Dongjoo Shin, Sangyeob Kim, Hoi-Jun Yoo

    DOI : 10.1109/JSSC.2018.2865489

    • #Computer science
    • #Artificial intelligence
    • #Computer hardware
    • #Artificial neural network
    • #CMOS
    • #Energy consumption
    • #Efficient energy use
    • #Lookup table
    • #Electrical efficiency
    • #16-bit
    • #Deep learning
  • Toward all-day wearable health monitoring: An ultralow-power, reflective organic pulse oximetry sensing patch

    2018/11
    SCIENCE ADVANCES

    12.8 Impact Factor

    62 citations

    Hyeon Woo Lee, Eunhye Kim, Yongsu Lee, Hoyeon Kim, Jaeho Lee, Mincheol Kim, Hoi-Jun Yoo, Seunghyup Yoo

    DOI : 10.1126/SCIADV.AAS9530

    • #Computer science
    • #Electrical engineering
    • #Diode
    • #Signal processing
    • #Light-emitting diode
    • #Electric power
    • #Photodiode
    • #Wearable computer
    • #Continuous monitoring
    • #Pulse oximetry

Get access to
Contact information

Log in

All papers authored by
‘ Hoi-Jun Yoo ’ : 70

  • UNPU: An Energy-Efficient Deep Neural Network Accelerator With Fully Variable Weight Bit Precision

    2019/01
    IEEE JOURNAL OF SOLID-STATE CIRCUITS

    4.9 Impact Factor

    96 citations

    Jinmook Lee, Changhyeon Kim, Sanghoon Kang, Dongjoo Shin, Sangyeob Kim, Hoi-Jun Yoo

    DOI : 10.1109/JSSC.2018.2865489

    • #Computer science
    • #Artificial intelligence
    • #Computer hardware
    • #Artificial neural network
    • #CMOS
    • #Energy consumption
    • #Efficient energy use
    • #Lookup table
    • #Electrical efficiency
    • #16-bit
    • #Deep learning
  • A 79 pJ/b 80 Mb/s Full-Duplex Transceiver and a $42.5\;\upmu\text{W}$ 100 kb/s Super-Regenerative Transceiver for Body Channel Communication

    2016/01
    IEEE JOURNAL OF SOLID-STATE CIRCUITS

    4.2 Impact Factor

    84 citations

    Hyunwoo Cho, Hyunki Kim, Minseo Kim, Jaeeun Jang, Yongsu Lee, Kyuho Jason Lee, Joonsung Bae, Hoi-Jun Yoo

    DOI : 10.1109/JSSC.2015.2498761

    • #Physics
    • #Electrical engineering
    • #Transmitter
    • #CMOS
    • #Transceiver
    • #Duplexer
    • #Frequency modulation
    • #Phase-shift keying
    • #Sine wave
    • #Duplex (telecommunications)

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