A User Scheduling with Minimum-Rate Requirement for Maximum Sum-Rate in MIMO-BC. - Seungkyu Choi; Chungyong Lee (IEICE Trans. Commun., 2011)

Power Allocation Algorithms for GMD or UCD Based Joint Transceiver Designs. - Seungkyu Choi; Chungyong Lee (Wirel. Pers. Commun., 2014)

SENIN: An energy-efficient sparse neuromorphic system with on-chip learning. - Myung-Hoon Choi; Seungkyu Choi; Jaehyeong Sim; Lee-Sup Kim (ISLPED, 2017)

Energy-Efficient Design of Processing Element for Convolutional Neural Network. - Yeongjae Choi; Dongmyung Bae; Jaehyeong Sim; Seungkyu Choi; Minhye Kim; Lee-Sup Kim (IEEE Trans. Circuits Syst. II Express Briefs, 2017)

TrainWare: A Memory Optimized Weight Update Architecture for On-Device Convolutional Neural Network Training. - Seungkyu Choi; Jaehyeong Sim; Myeonggu Kang; Lee-Sup Kim (ISLPED, 2018)

A 47.4µJ/epoch Trainable Deep Convolutional Neural Network Accelerator for In-Situ Personalization on Smart Devices. - Seungkyu Choi; Jaehyeong Sim; Myeonggu Kang; Yeongjae Choi; Hyeonuk Kim; Lee-Sup Kim (A-SSCC, 2019)

An Optimized Design Technique of Low-bit Neural Network Training for Personalization on IoT Devices. - Seungkyu Choi; Jaekang Shin; Yeongjae Choi; Lee-Sup Kim (DAC, 2019)

Compressing Sparse Ternary Weight Convolutional Neural Networks for Efficient Hardware Acceleration. - Hyeonwook Wi; Hyeonuk Kim; Seungkyu Choi; Lee-Sup Kim (ISLPED, 2019)

A Pragmatic Approach to On-device Incremental Learning System with Selective Weight Updates. - Jaekang Shin; Seungkyu Choi; Yeongjae Choi; Lee-Sup Kim (DAC, 2020)

An Energy-Efficient Deep Convolutional Neural Network Training Accelerator for In Situ Personalization on Smart Devices. - Seungkyu Choi; Jaehyeong Sim; Myeonggu Kang; Yeongjae Choi; Hyeonuk Kim; Lee-Sup Kim (IEEE J. Solid State Circuits, 2020)

A Convergence Monitoring Method for DNN Training of On-Device Task Adaptation. - Seungkyu Choi; Jaekang Shin; Lee-Sup Kim (ICCAD, 2021)

Algorithm/architecture co-design for energy-efficient acceleration of multi-task DNN. - Jaekang Shin; Seungkyu Choi; Jongwoo Ra; Lee-Sup Kim (DAC, 2022)

A Deep Neural Network Training Architecture With Inference-Aware Heterogeneous Data-Type. - Seungkyu Choi; Jaekang Shin; Lee-Sup Kim (IEEE Trans. Computers, 2022)

Rare Computing: Removing Redundant Multiplications From Sparse and Repetitive Data in Deep Neural Networks. - Kangkyu Park; Seungkyu Choi; Yeongjae Choi; Lee-Sup Kim (IEEE Trans. Computers, 2022)

SONA: An Accelerator for Transform-Domain Neural Networks with Sparse-Orthogonal Weights. - Pierre Abillama; Zichen Fan; Yu Chen 070; Hyochan An; Qirui Zhang 001; Seungkyu Choi; David T. Blaauw; Dennis Sylvester; Hun-Seok Kim (ASAP, 2023)

Energy-Efficient CNN Personalized Training by Adaptive Data Reformation. - Youngbeom Jung; Hyeonuk Kim; Seungkyu Choi; Jaekang Shin; Lee-Sup Kim (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2023)

Accelerating On-Device DNN Training Workloads via Runtime Convergence Monitor. - Seungkyu Choi; Jaekang Shin; Lee-Sup Kim (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2023)

MERSIT: A Hardware-Efficient 8-bit Data Format with Enhanced Post-Training Quantization DNN Accuracy. - Nguyen-Dong Ho; Gyujun Jeong; Cheol-Min Kang; Seungkyu Choi; Ik-Joon Chang (DAC, 2024)

ParaBase: A Configurable Parallel Baseband Processor for Ultra-High-Speed Inter-Satellite Optical Communications. - Seungkyu Choi; Huanshihong Deng; Kuan-Yu Chen 001; Yufan Yue; David T. Blaauw; Hun-Seok Kim (ISLPED, 2024)

Qrazor: Reliable and effortless 4-bit llm quantization by significant data razoring. - Dongyoung Lee; Seungkyu Choi; Ik Joon Chang (CoRR, 2025)