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ORCIDSeungkyu Choi
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2020 – today
- 2024
[c11]Nguyen-Dong Ho
, Gyujun Jeong, Cheol-Min Kang, Seungkyu Choi, Ik-Joon Chang:
MERSIT: A Hardware-Efficient 8-bit Data Format with Enhanced Post-Training Quantization DNN Accuracy. DAC 2024: 52:1-52:6- [c10]Seungkyu Choi, Huanshihong Deng, Kuan-Yu Chen, Yufan Yue, David T. Blaauw, Hun-Seok Kim:
ParaBase: A Configurable Parallel Baseband Processor for Ultra-High-Speed Inter-Satellite Optical Communications. ISLPED 2024: 1-6 - 2023
- [j8]Youngbeom Jung, Hyeonuk Kim, Seungkyu Choi, Jaekang Shin, Lee-Sup Kim:
Energy-Efficient CNN Personalized Training by Adaptive Data Reformation. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 42(1): 332-336 (2023) - [j7]Seungkyu Choi, Jaekang Shin, Lee-Sup Kim:
Accelerating On-Device DNN Training Workloads via Runtime Convergence Monitor. IEEE Trans. Comput. Aided Des. Integr. Circuits Syst. 42(5): 1574-1587 (2023) - [c9]Pierre Abillama, Zichen Fan, Yu Chen, Hyochan An, Qirui Zhang, Seungkyu Choi, David T. Blaauw, Dennis Sylvester, Hun-Seok Kim:
SONA: An Accelerator for Transform-Domain Neural Networks with Sparse-Orthogonal Weights. ASAP 2023: 18-26 - 2022
- [j6]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) - [j5]Seungkyu Choi, Jaekang Shin, Lee-Sup Kim:
A Deep Neural Network Training Architecture With Inference-Aware Heterogeneous Data-Type. IEEE Trans. Computers 71(5): 1216-1229 (2022) - [c8]Jaekang Shin, Seungkyu Choi, Jongwoo Ra, Lee-Sup Kim:
Algorithm/architecture co-design for energy-efficient acceleration of multi-task DNN. DAC 2022: 253-258 - 2021
- [c7]Seungkyu Choi, Jaekang Shin, Lee-Sup Kim:
A Convergence Monitoring Method for DNN Training of On-Device Task Adaptation. ICCAD 2021: 1-9 - 2020
- [j4]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) - [c6]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
- [c5]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 - [c4]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 - [c3]Hyeonwook Wi, Hyeonuk Kim, Seungkyu Choi, Lee-Sup Kim:
Compressing Sparse Ternary Weight Convolutional Neural Networks for Efficient Hardware Acceleration. ISLPED 2019: 1-6 - 2018
- [c2]Seungkyu Choi, Jaehyeong Sim, Myeonggu Kang, Lee-Sup Kim:
TrainWare: A Memory Optimized Weight Update Architecture for On-Device Convolutional Neural Network Training. ISLPED 2018: 19:1-19:6 - 2017
- [j3]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) - [c1]Myung-Hoon Choi, Seungkyu Choi, Jaehyeong Sim, Lee-Sup Kim:
SENIN: An energy-efficient sparse neuromorphic system with on-chip learning. ISLPED 2017: 1-6 - 2014
- [j2]Seungkyu Choi, Chungyong Lee:
Power Allocation Algorithms for GMD or UCD Based Joint Transceiver Designs. Wirel. Pers. Commun. 79(1): 105-118 (2014) - 2011
- [j1]Seungkyu Choi, Chungyong Lee:
A User Scheduling with Minimum-Rate Requirement for Maximum Sum-Rate in MIMO-BC. IEICE Trans. Commun. 94-B(11): 3179-3182 (2011)
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