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Sangkug Lym
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Conference and Workshop Papers
- 2023
- [c11]Vijay Anand Korthikanti, Jared Casper, Sangkug Lym, Lawrence McAfee, Michael Andersch, Mohammad Shoeybi, Bryan Catanzaro:
Reducing Activation Recomputation in Large Transformer Models. MLSys 2023 - 2020
- [c10]Benjamin Y. Cho, Yongkee Kwon, Sangkug Lym, Mattan Erez:
Near Data Acceleration with Concurrent Host Access. ISCA 2020: 818-831 - [c9]Siavash Zangeneh, Stephen Pruett, Sangkug Lym, Yale N. Patt:
BranchNet: A Convolutional Neural Network to Predict Hard-To-Predict Branches. MICRO 2020: 118-130 - 2019
- [c8]Sangkug Lym, Donghyuk Lee, Mike O'Connor, Niladrish Chatterjee, Mattan Erez:
DeLTA: GPU Performance Model for Deep Learning Applications with In-Depth Memory System Traffic Analysis. ISPASS 2019: 293-303 - [c7]Sangkug Lym, Armand Behroozi, Wei Wen, Ge Li, Yongkee Kwon, Mattan Erez:
Mini-batch Serialization: CNN Training with Inter-layer Data Reuse. SysML 2019 - [c6]Sangkug Lym, Esha Choukse, Siavash Zangeneh, Wei Wen, Sujay Sanghavi, Mattan Erez:
PruneTrain: fast neural network training by dynamic sparse model reconfiguration. SC 2019: 36:1-36:13 - 2018
- [c5]Chun-Kai Chang, Sangkug Lym, Nicholas Kelly, Michael B. Sullivan, Mattan Erez:
Hamartia: A Fast and Accurate Error Injection Framework. DSN Workshops 2018: 101-108 - [c4]Sangkug Lym, Heonjae Ha, Yongkee Kwon, Chun-Kai Chang, Jungrae Kim, Mattan Erez:
ERUCA: Efficient DRAM Resource Utilization and Resource Conflict Avoidance for Memory System Parallelism. HPCA 2018: 670-682 - [c3]Seong-Lyong Gong, Jungrae Kim, Sangkug Lym, Michael B. Sullivan, Howard David, Mattan Erez:
DUO: Exposing On-Chip Redundancy to Rank-Level ECC for High Reliability. HPCA 2018: 683-695 - [c2]Chun-Kai Chang, Sangkug Lym, Nicholas Kelly, Michael B. Sullivan, Mattan Erez:
Evaluating and accelerating high-fidelity error injection for HPC. SC 2018: 45:1-45:13 - 2016
- [c1]Jungrae Kim, Michael B. Sullivan, Sangkug Lym, Mattan Erez:
All-Inclusive ECC: Thorough End-to-End Protection for Reliable Computer Memory. ISCA 2016: 622-633
Informal and Other Publications
- 2022
- [i6]Vijay Korthikanti, Jared Casper, Sangkug Lym, Lawrence McAfee, Michael Andersch, Mohammad Shoeybi, Bryan Catanzaro:
Reducing Activation Recomputation in Large Transformer Models. CoRR abs/2205.05198 (2022) - 2020
- [i5]Sangkug Lym, Mattan Erez:
FlexSA: Flexible Systolic Array Architecture for Efficient Pruned DNN Model Training. CoRR abs/2004.13027 (2020) - 2019
- [i4]Sangkug Lym, Esha Choukse, Siavash Zangeneh, Wei Wen, Mattan Erez, Sujay Shanghavi:
PruneTrain: Gradual Structured Pruning from Scratch for Faster Neural Network Training. CoRR abs/1901.09290 (2019) - [i3]Sangkug Lym, Donghyuk Lee, Mike O'Connor, Niladrish Chatterjee, Mattan Erez:
DeLTA: GPU Performance Model for Deep Learning Applications with In-depth Memory System Traffic Analysis. CoRR abs/1904.01691 (2019) - [i2]Benjamin Y. Cho, Yongkee Kwon, Sangkug Lym, Mattan Erez:
CHoNDA: Near Data Acceleration with Concurrent Host Access. CoRR abs/1908.06362 (2019) - 2018
- [i1]Sangkug Lym, Armand Behroozi, Wei Wen, Ge Li, Yongkee Kwon, Mattan Erez:
Mini-batch Serialization: CNN Training with Inter-layer Data Reuse. CoRR abs/1810.00307 (2018)
Coauthor Index
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