Publications
International Conference Papers
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* and Ik Joon Chang*
ACM/IEEE Design Automation Conference (DAC), 2024 (Accepted)
SONA: An Accelerator for Transform-Domain Neural Networks with Sparse-Orthogonal Weights
Pierre Abillama, Zichen Fan, Yu Chen, Hyochan An, Qirui Zhang, Seungkyu Choi, David Blaauw, Dennis Sylvester, and Hun-Seok Kim
IEEE International Conference on Application-specific Systems, Architectures, and Processors (ASAP), 2023 (Best Paper Award)
Algorithm/Architecture Co-Design for Energy-Efficient Acceleration of Multi-Task DNN
Jaekang Shin, Seungkyu Choi, Jongwoo Ra, and Lee-Sup Kim
ACM/IEEE Design Automation Conference (DAC), 2022 [DOI]
A Convergence Monitoring Method for DNN Training of On-device Task Adaptation
Seungkyu Choi, Jaekang Shin, and Lee-Sup Kim
IEEE/ACM International Conference on Computer-Aided Design (ICCAD), 2021 [DOI]
A Pragmatic Approach to On-device Incremental Learning System with Selective Weight Updates
Jaekang Shin, Seungkyu Choi, Yeongjae Choi, and Lee-Sup Kim
ACM/IEEE Design Automation Conference (DAC), 2020 [DOI]
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, and Lee-Sup Kim
IEEE Asian Solid-State Circuits Conference (A-SSCC), 2019 [DOI]
Compressing Sparse Ternary Weight Convolutional Neural Networks for Efficient Hardware Acceleration
Hyeonwook Wi, Hyeonuk Kim, Seungkyu Choi, and Lee-Sup Kim
IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED), 2019 [DOI]
An Optimized Design Technique of Low-bit Neural Network Training for Personalization on IoT Devices
Seungkyu Choi, Jaekang Shin, Yeongjae Choi, and Lee-Sup Kim
ACM/IEEE Design Automation Conference (DAC), 2019 [DOI]
TrainWare: A Memory Optimized Weight Update Architecture for On-Device Convolutional Neural Network Training
Seungkyu Choi, Jaehyeong Sim, Myeonggu Kang, and Lee-Sup Kim
IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED), 2018 [DOI]
SENIN: An Energy-efficient Sparse Neuromorphic System with On-chip Learning
Myung-Hoon Choi, Seungkyu Choi, Jaehyeong Sim, and Lee-Sup Kim
IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED), 2017 [DOI]
International Journal Papers
Accelerating On-Device DNN Training Workloads via Runtime Convergence Monitor
Seungkyu Choi, Jaekang Shin, and Lee-Sup Kim
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, May 2023 [DOI]
Energy-Efficient CNN Personalized Training by Adaptive Data Reformation
Youngbeom Jung, Hyeonuk Kim, Seungkyu Choi, Jaekang Shin, and Lee-Sup Kim
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, Jan. 2023 [DOI]
A Deep Neural Network Training Architecture with Inference-aware Heterogeneous Data-type
Seungkyu Choi, Jaekang Shin, and Lee-Sup Kim
IEEE Transactions on Computers, May 2022 [DOI]
Rare Computing: Removing Redundant Multiplications from Sparse and Repetitive Data in Deep Neural Networks
Kangkyu Park, Seungkyu Choi, Yeongjae Choi, and Lee-Sup Kim
IEEE Transactions on Computers, Apr. 2022 [DOI]
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, and Lee-Sup Kim
IEEE Journal of Solid-State Circuits, Oct. 2020 [DOI]
Energy-Efficient Design of Processing Element for Convolutional Neural Network
Yeongjae Choi, Dongmyung Bae, Jaehyeong Sim, Seungkyu Choi, Minhye Kim, and Lee-Sup Kim
IEEE Transactions on Circuits and Systems II, Nov. 2017 [DOI]