| 2026 | ISLPED | L | Myeongjin Kim, Hyeonseong Kim, Ik Joon Chang, Seungkyu Choi |
| 2025 | DAC | Precon: A Precision-Convertible Architecture for Accelerating Quantized Deep Learning Models across Various Domains Including LLMs. | Jongwoo Park, Hyeonseong Kim, Jiyun Han, Seungkyu Choi |
| 2024 | DAC | 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 |
| 2024 | ISLPED | ParaBase: A Configurable Parallel Baseband Processor for Ultra-High-Speed Inter-Satellite Optical Communications. | Seungkyu Choi, Huanshihong Deng, Kuan-Yu Chen, Yufan Yue, David T. Blaauw, Hun-Seok Kim |
| 2022 | DAC | Algorithm/architecture co-design for energy-efficient acceleration of multi-task DNN. | Jaekang Shin, Seungkyu Choi, Jongwoo Ra, Lee-Sup Kim |
| 2021 | ICCAD | A Convergence Monitoring Method for DNN Training of On-Device Task Adaptation. | Seungkyu Choi, Jaekang Shin, Lee-Sup Kim |
| 2020 | DAC | A Pragmatic Approach to On-device Incremental Learning System with Selective Weight Updates. | Jaekang Shin, Seungkyu Choi, Yeongjae Choi, Lee-Sup Kim |
| 2019 | DAC | An Optimized Design Technique of Low-bit Neural Network Training for Personalization on IoT Devices. | Seungkyu Choi, Jaekang Shin, Yeongjae Choi, Lee-Sup Kim |
| 2019 | ISLPED | Compressing Sparse Ternary Weight Convolutional Neural Networks for Efficient Hardware Acceleration. | Hyeonwook Wi, Hyeonuk Kim, Seungkyu Choi, Lee-Sup Kim |
| 2018 | ISLPED | TrainWare: A Memory Optimized Weight Update Architecture for On-Device Convolutional Neural Network Training. | Seungkyu Choi, Jaehyeong Sim, Myeonggu Kang, Lee-Sup Kim |
| 2017 | ISLPED | SENIN: An energy-efficient sparse neuromorphic system with on-chip learning. | Myung-Hoon Choi, Seungkyu Choi, Jaehyeong Sim, Lee-Sup Kim |