| International Journal Papers |
| 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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| Youngbeom Jung, Hyeonuk Kim, Yeongjae Choi, Lee-Sup Kim |
| Quantization-Error-Robust Deep Neural Network for Embedded Accelerators |
| IEEE Transactions on Circuits and Systems II, 2022 |
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| Yeongjae Choi, Jaehyeong Sim, Lee-Sup Kim |
| CREMON: Cryptography Embedded on the Convolutional Neural Network Accelerator |
| IEEE Transactions on Circuits and Systems II: Express Briefs, 2020 |
| |
| 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, Yeongjae Choi, Lee-Sup Kim |
| A Pragmatic Approach to On-device Incremental Learning System with Selective Weight Updates |
| IEEE/ACM Design Automation Conference , 2020 |
| |
| 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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| Youngbeom Jung, Yeongjae Choi, Jaehyeong Sim, Lee-Sup Kim |
| eSRCNN: A Framework for Optimizing Super-Resolution Tasks on Diverse Embedded CNN Accelerators |
| IEEE/ACM International Conference On Computer Aided 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 |
| |
| Hyeonuk Kim, Jaehyeong Sim, Yeongjae Choi, Lee-Sup Kim |
| NAND-Net: Minimizing Computational Complexity of In-Memory Processing for Binary Neural Networks |
| IEEE International Symposium on High-Performance Computer Architecture, 2019 |
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| Hyeonuk Kim, Jaehyeong Sim, Yeongjae Choi, Lee-Sup Kim |
| A Kernel Decomposition Architecture for Binary-weight Convolutional Neural Networks |
| ACM/IEEE Design Automation Conference, 2017 |
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| Yeongjae Choi, Jun-Seok Park, Lee-Sup Kim |
| Hardware-Centric Vision Processing for Mobile IoT Environment Exploiting Approximate Graph cut in Resistor Grid |
| IEEE Winter Conference on Applications of Computer Vision, 2017 |
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| Jaehyeong Sim, Jun-Seok Park, Minhye Kim, Dongmyung Bae, Yeongjae Choi, Lee-Sup Kim |
| A 1.42TOPS/W Deep Convolutional Neural Network Recognition Processor for Intelligent IoE Systems |
| IEEE International Solid-State Circuit Conference, 2016 |
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