| International Journal Papers |
| Yanggon Kim, Yunki Han, Jaekang Shin, Junkyum Kim, Lee-Sup Kim |
| Accelerating Deep Reinforcement Learning Via Phase-Level Parallelism for Robotics Applications |
| IEEE Computer Architecture Letters, 2023 |
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| 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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| International Conference Papers |
| Junyoung Park, Myeonggu Kang, Yunki Han, Yanggon Kim, Jaekang Shin, Lee-Sup Kim |
| Token-Picker: Accelerating Attention in Text Generation with Minimized Memory Transfer via Probability Estimation |
| IEEE/ACM Design Automation Conference, 2024 |
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| 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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| Myeonggu Kang, Hyein Shin, Jaekang Shin, Lee-Sup Kim |
| A Framework for Area-efficient Multi-task BERT Execution on ReRAM-based Accelerators |
| 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, 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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