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2020 – today
- 2022
[j5]Kangkyu Park
, Seungkyu Choi, Yeongjae Choi, Lee-Sup Kim:
Rare Computing: Removing Redundant Multiplications From Sparse and Repetitive Data in Deep Neural Networks. IEEE Trans. Computers 71(4): 795-808 (2022)- [j4]Youngbeom Jung, Hyeonuk Kim, Yeongjae Choi, Lee-Sup Kim:
Quantization-Error-Robust Deep Neural Network for Embedded Accelerators. IEEE Trans. Circuits Syst. II Express Briefs 69(2): 609-613 (2022) - [c10]Namkyoo Kang, Seungeun Han, Seungyeon Kim, SeungJoon Kwon, Yeongjae Choi, Yong-Tae Lee, Seung-Ik Lee:
Driver Drowsiness Detection based on 3D Convolution Neural Network with Optimized Window Size. ICTC 2022: 425-428 - 2021
- [c9]Jun-Woo Jang, Sehwan Lee, Dongyoung Kim, Hyunsun Park, Ali Shafiee Ardestani, Yeongjae Choi, Channoh Kim, Yoojin Kim, Hyeongseok Yu, Hamzah Abdel-Aziz, Jun-Seok Park, Heonsoo Lee, Dongwoo Lee, Myeong Woo Kim, Hanwoong Jung, Heewoo Nam, Dongguen Lim, Seungwon Lee, Joon-Ho Song, Suknam Kwon, Joseph Hassoun, Sukhwan Lim, Changkyu Choi:
Sparsity-Aware and Re-configurable NPU Architecture for Samsung Flagship Mobile SoC. ISCA 2021: 15-28 - 2020
- [j3]Seungkyu Choi, Jaehyeong Sim, Myeonggu Kang, Yeongjae Choi, Hyeonuk Kim, Lee-Sup Kim:
An Energy-Efficient Deep Convolutional Neural Network Training Accelerator for In Situ Personalization on Smart Devices. IEEE J. Solid State Circuits 55(10): 2691-2702 (2020) - [j2]Yeongjae Choi, Jaehyeong Sim, Lee-Sup Kim:
CREMON: Cryptography Embedded on the Convolutional Neural Network Accelerator. IEEE Trans. Circuits Syst. 67-II(12): 3337-3341 (2020) - [c8]Jaekang Shin, Seungkyu Choi, Yeongjae Choi, Lee-Sup Kim:
A Pragmatic Approach to On-device Incremental Learning System with Selective Weight Updates. DAC 2020: 1-6
2010 – 2019
- 2019
- [c7]Seungkyu Choi, Jaehyeong Sim, Myeonggu Kang, Yeongjae Choi, Hyeonuk Kim, Lee-Sup Kim:
A 47.4µJ/epoch Trainable Deep Convolutional Neural Network Accelerator for In-Situ Personalization on Smart Devices. A-SSCC 2019: 57-60 - [c6]Seungkyu Choi, Jaekang Shin, Yeongjae Choi, Lee-Sup Kim:
An Optimized Design Technique of Low-bit Neural Network Training for Personalization on IoT Devices. DAC 2019: 191 - [c5]Hyeonuk Kim, Jaehyeong Sim, Yeongjae Choi, Lee-Sup Kim:
NAND-Net: Minimizing Computational Complexity of In-Memory Processing for Binary Neural Networks. HPCA 2019: 661-673 - [c4]Youngbeom Jung, Yeongjae Choi, Jaehyeong Sim, Lee-Sup Kim:
eSRCNN: A Framework for Optimizing Super-Resolution Tasks on Diverse Embedded CNN Accelerators. ICCAD 2019: 1-8 - 2017
- [j1]Yeongjae Choi, Dongmyung Bae, Jaehyeong Sim, Seungkyu Choi, Minhye Kim, Lee-Sup Kim:
Energy-Efficient Design of Processing Element for Convolutional Neural Network. IEEE Trans. Circuits Syst. II Express Briefs 64-II(11): 1332-1336 (2017) - [c3]Hyeonuk Kim, Jaehyeong Sim, Yeongjae Choi, Lee-Sup Kim:
A Kernel Decomposition Architecture for Binary-weight Convolutional Neural Networks. DAC 2017: 60:1-60:6 - [c2]Yeongjae Choi, Jun-Seok Park, Lee-Sup Kim:
Hardware-Centric Vision Processing for Mobile IoT Environment Exploiting Approximate Graph Cut in Resistor Grid. WACV 2017: 778-786 - 2016
- [c1]Jaehyeong Sim, Jun-Seok Park, Minhye Kim, Dongmyung Bae, Yeongjae Choi, Lee-Sup Kim:
14.6 A 1.42TOPS/W deep convolutional neural network recognition processor for intelligent IoE systems. ISSCC 2016: 264-265
Coauthor Index
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