| Layerweaver: Maximizing resource utilization of neural processing units via layer-wise scheduling YH Oh, S Kim, Y Jin, S Son, J Bae, J Lee, Y Park, DU Kim, TJ Ham, ... 2021 IEEE International Symposium on High-Performance Computer Architecture …, 2021 | 37 | 2021 |
| {FlashNeuron}:{SSD-Enabled}{Large-Batch} Training of Very Deep Neural Networks J Bae, J Lee, Y Jin, S Son, S Kim, H Jang, TJ Ham, JW Lee 19th USENIX Conference on File and Storage Technologies (FAST 21), 387-401, 2021 | 36 | 2021 |
| A case for hardware-based demand paging G Lee, W Jin, W Song, J Gong, J Bae, TJ Ham, JW Lee, J Jeong 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture …, 2020 | 25 | 2020 |
| Practical erase suspension for modern low-latency {SSDs} S Kim, J Bae, H Jang, W Jin, J Gong, S Lee, TJ Ham, JW Lee 2019 USENIX Annual Technical Conference (USENIX ATC 19), 813-820, 2019 | 24 | 2019 |
| Behemoth: a flash-centric training accelerator for extreme-scale {DNNs} S Kim, Y Jin, G Sohn, J Bae, TJ Ham, JW Lee 19th USENIX Conference on File and Storage Technologies (FAST 21), 371-385, 2021 | 20 | 2021 |
| {ASAP}: Fast mobile application switch via adaptive prepaging S Son, SY Lee, Y Jin, J Bae, J Jeong, TJ Ham, JW Lee, H Yoon 2021 USENIX Annual Technical Conference (USENIX ATC 21), 365-380, 2021 | 16 | 2021 |
| Jointly optimizing task granularity and concurrency for in-memory mapreduce frameworks J Bae, H Jang, W Jin, J Heo, J Jang, JY Hwang, S Cho, JW Lee 2017 IEEE International Conference on Big Data (Big Data), 130-140, 2017 | 11 | 2017 |
| Ssdstreamer: Specializing i/o stack for large-scale machine learning J Bae, H Jang, J Gong, W Jin, S Kim, J Jang, TJ Ham, J Jeong, JW Lee IEEE Micro 39 (5), 73-81, 2019 | 4 | 2019 |
| DDStore: Distributed Data Store for Scalable Training of Graph Neural Networks on Large Atomistic Modeling Datasets JY Choi, M Lupo Pasini, P Zhang, K Mehta, F Liu, J Bae, K Ibrahim Proceedings of the SC'23 Workshops of The International Conference on High …, 2023 | 1 | 2023 |
| L3: accelerator-friendly lossless image format for high-resolution, high-throughput dnn training J Bae, W Baek, TJ Ham, JW Lee European Conference on Computer Vision, 171-188, 2022 | 1 | 2022 |
| Liquid: Mix-and-Match Multiple Image Formats to Balance DNN Training Pipeline W Baek, J Bae, D Lee, H Bae, Y Park, JW Lee Proceedings of the 14th ACM SIGOPS Asia-Pacific Workshop on Systems, 50-57, 2023 | | 2023 |
| Accelerator system for training deep neural network model using nand flash memory and operating method thereof JW Lee, Y Jin, JH Bae, GA Sohn, TJ Ham US Patent App. 18/089,141, 2023 | | 2023 |
| Eager Memory Management for In-Memory Data Analytics H Jang, J Bae, TJ Ham, JW Lee IEICE TRANSACTIONS on Information and Systems 102 (3), 632-636, 2019 | | 2019 |
Jae W. LeeProfessor of Computer Science and Engineering, Seoul National University, KoreaZweryfikowany adres z snu.ac.kr
