| An Energy-Efficient Deep Convolutional Neural Network Training Accelerator for In Situ Personalization on Smart Devices S Choi, J Sim, M Kang, Y Choi, H Kim, LS Kim IEEE Journal of Solid-State Circuits 55 (10), 2691-2702, 2020 | 41 | 2020 |
| Energy-efficient design of processing element for convolutional neural network Y Choi, D Bae, J Sim, S Choi, M Kim, LS Kim IEEE Transactions on Circuits and Systems II: Express Briefs 64 (11), 1332-1336, 2017 | 34 | 2017 |
| An optimized design technique of low-bit neural network training for personalization on IoT devices S Choi, J Shin, Y Choi, LS Kim Proceedings of the 56th Annual Design Automation Conference 2019, 1-6, 2019 | 24 | 2019 |
| TrainWare: A memory optimized weight update architecture for on-device convolutional neural network training S Choi, J Sim, M Kang, LS Kim Proceedings of the International Symposium on Low Power Electronics and …, 2018 | 24 | 2018 |
| A pragmatic approach to on-device incremental learning system with selective weight updates J Shin, S Choi, Y Choi, LS Kim 2020 57th ACM/IEEE Design Automation Conference (DAC), 1-6, 2020 | 11 | 2020 |
| A 47.4 µJ/epoch trainable deep convolutional neural network accelerator for in-situ personalization on smart devices S Choi, J Sim, M Kang, Y Choi, H Kim, LS Kim 2019 IEEE Asian Solid-State Circuits Conference (A-SSCC), 57-60, 2019 | 7 | 2019 |
| A deep neural network training architecture with inference-aware heterogeneous data-type S Choi, J Shin, LS Kim IEEE Transactions on Computers 71 (5), 1216-1229, 2021 | 5 | 2021 |
| Compressing sparse ternary weight convolutional neural networks for efficient hardware acceleration H Wi, H Kim, S Choi, LS Kim 2019 IEEE/ACM International Symposium on Low Power Electronics and Design …, 2019 | 5 | 2019 |
| SENIN: An energy-efficient sparse neuromorphic system with on-chip learning MH Choi, S Choi, J Sim, LS Kim 2017 IEEE/ACM International Symposium on Low Power Electronics and Design …, 2017 | 4 | 2017 |
| A convergence monitoring method for DNN training of on-device task adaptation S Choi, J Shin, LS Kim 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD), 1-9, 2021 | 3 | 2021 |
| Algorithm/architecture co-design for energy-efficient acceleration of multi-task DNN J Shin, S Choi, J Ra, LS Kim Proceedings of the 59th ACM/IEEE Design Automation Conference, 253-258, 2022 | 2 | 2022 |
| Method and apparatus with incremental learning moddel K Donghyuk, KIM Leesup, S Jaekang, C SeungKyu US Patent App. 17/089,764, 2021 | 2 | 2021 |
| Accelerating On-Device DNN Training Workloads via Runtime Convergence Monitor S Choi, J Shin, LS Kim IEEE Transactions on Computer-Aided Design of Integrated Circuits and …, 2022 | 1 | 2022 |
| Energy-efficient CNN Personalized training by adaptive data reformation Y Jung, H Kim, S Choi, J Shin, LS Kim IEEE Transactions on Computer-Aided Design of Integrated Circuits and …, 2022 | 1 | 2022 |
| Apparatus and method with multi-task processing JW Jang, S Jaekang, LS Kim, C SeungKyu US Patent App. 17/903,969, 2023 | | 2023 |
| Method and apparatus with neural network compression JW Jang, S Jaekang, LS Kim, C SeungKyu US Patent App. 17/892,481, 2023 | | 2023 |
| Method and device for encoding C Yeongjae, C SeungKyu, LS Kim, S Jaekang US Patent App. 17/401,453, 2022 | | 2022 |
| Method and apparatus with neural network data quantizing C SeungKyu, HA Sangwon, LS Kim, S Jaekang US Patent App. 15/931,362, 2021 | | 2021 |
| Rare Computing: Removing Redundant Multiplications From Sparse and Repetitive Data in Deep Neural Networks K Park, S Choi, Y Choi, LS Kim IEEE Transactions on Computers 71 (4), 795-808, 2021 | | 2021 |
