| Towards an optimized distributed deep learning framework for a heterogeneous multi-GPU cluster Y Kim, H Choi, J Lee, JS Kim, H Jei, H Roh Cluster Computing 23 (3), 2287-2300, 2020 | 16 | 2020 |
| Efficient multi-GPU memory management for deep learning acceleration Y Kim, J Lee, JS Kim, H Jei, H Roh 2018 IEEE 3rd International Workshops on Foundations and Applications of …, 2018 | 13 | 2018 |
| Efficient large-scale deep learning framework for heterogeneous multi-gpu cluster Y Kim, H Choi, J Lee, JS Kim, H Jei, H Roh 2019 IEEE 4th International Workshops on Foundations and Applications of …, 2019 | 12 | 2019 |
| Comprehensive techniques of multi-GPU memory optimization for deep learning acceleration Y Kim, J Lee, JS Kim, H Jei, H Roh Cluster Computing 23, 2193-2204, 2020 | 11 | 2020 |
| ALADDIN: Asymmetric Centralized Training for Distributed Deep Learning Y Ko, K Choi, H Jei, D Lee, SW Kim Proceedings of the 30th ACM International Conference on Information …, 2021 | 6 | 2021 |
| Method of operating a memory system having a meta data manager WOO Yeong-Jae, SEO Sungyong, O Bae, HS Jei US Patent 9,891,838, 2018 | 6 | 2018 |
| 파라미터 서버 기반의 분산 딥 러닝 시스템을 위한 파라미터 샤딩 기술 성능평가 최기봉， 고윤용， 제현승， 노홍찬， 김상욱 2019 년 한국컴퓨터종합학술대회 논문집, 1088-1090, 2019 | | 2019 |
