| International Journal Papers |
| Youngbeom Jung, Hyeonuk Kim, Seungkyu Choi, Jaekang Shin, Lee-Sup Kim |
| Energy-Efficient CNN Personalized Training by Adaptive Data Reformation |
| IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2023 |
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| Seungkyu Choi, Jaekang Shin, Lee-Sup Kim |
| Accelerating On-Device DNN Training Workloads via Runtime Convergence Monitoring |
| IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2022 |
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| Seungkyu Choi, Jaekang Shin, Lee-Sup Kim |
| A Deep Neural Network Training Architecture with Inference-aware Heterogeneous Data-type |
| IEEE Transactions on Computers, 2022 |
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| Kangkyu Park, Seungkyu Choi, Yeongjae Choi, Lee-Sup Kim |
| Rare Computing: Removing Redundant Multiplications from Sparse and Repetitive Data in Deep Neural Networks |
| IEEE Transactions on Computers, 2022 |
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| 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 Journal of Solid-State Circuits, 2020 |
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| Yeongjae Choi, Dongmyung Bae, Jaehyeong Sim, Seungkyu Choi, Minhye Kim, Lee-Sup Kim |
| Energy-efficient design of processing element for convolutional neural network |
| IEEE Transactions on Circuits and Systems II, 2017 |
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| International Conference Papers |
| Jaekang Shin, Seungkyu Choi, Jongwoo Ra, Lee-Sup Kim |
| Algorithm/Architecture Co-Design for Energy-Efficient Acceleration of Multi-Task DNN |
| IEEE/ACM Design Automation Conference, 2022 |
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| Seungkyu Choi, Jaekang Shin, Lee-Sup Kim |
| A Convergence Monitoring Method for DNN Training of On-Device Task Adaptation |
| IEEE/ACM International Conference On Computer Aided Design, 2021 |
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| Jaekang Shin, Seungkyu Choi, Yeongjae Choi, Lee-Sup Kim |
| A Pragmatic Approach to On-device Incremental Learning System with Selective Weight Updates |
| IEEE/ACM Design Automation Conference , 2020 |
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| Seungkyu Choi, Jaehyeong Sim, Myeonggu Kang, Yeongjae Choi, Hyeonuk Kim, Lee-Sup Kim |
| A 47.4uJ/epoch Trainable Deep Convolutional Neural Network Accelerator for In-Situ Personalization on Smart Devices |
| IEEE Asian Solid-State Circuits Conference, 2019 |
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| Hyeonwook Wi, Hyeonuk Kim, Seungkyu Choi, Lee-Sup Kim |
| Compressing Sparse Ternary Weight Convolutional Neural Networks for Efficient Hardware Acceleration |
| ACM/IEEE International Symposium on Low Power Electronics and Design, 2019 |
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| Seungkyu Choi, Jaekang Shin, Yeongjae Choi, Lee-Sup Kim |
| An Optimized Design Technique of Low-bit Neural Network Training for Personalization on IoT Devices |
| IEEE/ACM Design Automation Conference, 2019 |
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| Seungkyu Choi, Jaehyeong Sim, Myeonggu Kang, Lee-Sup Kim |
| TrainWare: A Memory Optimized Weight Update Architecture for On-Device Convolutional Neural Network Training |
| ACM/IEEE International Symposium on Low Power Electronics and Design, 2018 |
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| Myunghoon Choi, Seungkyu Choi, Jaehyeong Sim, Lee-Sup Kim |
| SENIN: An Energy-Efficient Sparse Neuromorphic System |
| The International Symposium on Low Power Electronics and Design , 2017 |
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