All-Inclusive ECC: Thorough End-to-End Protection for Reliable Computer Memory. - Jungrae Kim; Michael B. Sullivan 001; Sangkug Lym; Mattan Erez (ISCA, 2016)

Hamartia: A Fast and Accurate Error Injection Framework. - Chun-Kai Chang; Sangkug Lym; Nicholas Kelly; Michael B. Sullivan 001; Mattan Erez (DSN Workshops, 2018)

DUO: Exposing On-Chip Redundancy to Rank-Level ECC for High Reliability. - Seong-Lyong Gong; Jungrae Kim; Sangkug Lym; Michael B. Sullivan 001; Howard David; Mattan Erez (HPCA, 2018)

ERUCA: Efficient DRAM Resource Utilization and Resource Conflict Avoidance for Memory System Parallelism. - Sangkug Lym; Heonjae Ha; Yongkee Kwon; Chun-Kai Chang; Jungrae Kim; Mattan Erez (HPCA, 2018)

Evaluating and accelerating high-fidelity error injection for HPC. - Chun-Kai Chang; Sangkug Lym; Nicholas Kelly; Michael B. Sullivan 001; Mattan Erez (SC, 2018)

DeLTA: GPU Performance Model for Deep Learning Applications with In-Depth Memory System Traffic Analysis. - Sangkug Lym; Donghyuk Lee; Mike O'Connor; Niladrish Chatterjee; Mattan Erez (ISPASS, 2019)

Mini-batch Serialization: CNN Training with Inter-layer Data Reuse. - Sangkug Lym; Armand Behroozi; Wei Wen; Ge Li; Yongkee Kwon; Mattan Erez (SysML, 2019)

PruneTrain: fast neural network training by dynamic sparse model reconfiguration. - Sangkug Lym; Esha Choukse; Siavash Zangeneh; Wei Wen; Sujay Sanghavi; Mattan Erez (SC, 2019)

PruneTrain: Gradual Structured Pruning from Scratch for Faster Neural Network Training. - Sangkug Lym; Esha Choukse; Siavash Zangeneh; Wei Wen; Mattan Erez; Sujay Shanghavi (CoRR, 2019)

CHoNDA: Near Data Acceleration with Concurrent Host Access. - Benjamin Y. Cho; Yongkee Kwon; Sangkug Lym; Mattan Erez (CoRR, 2019)

Near Data Acceleration with Concurrent Host Access. - Benjamin Y. Cho; Yongkee Kwon; Sangkug Lym; Mattan Erez (ISCA, 2020)

BranchNet: A Convolutional Neural Network to Predict Hard-To-Predict Branches. - Siavash Zangeneh; Stephen Pruett; Sangkug Lym; Yale N. Patt (MICRO, 2020)

FlexSA: Flexible Systolic Array Architecture for Efficient Pruned DNN Model Training. - Sangkug Lym; Mattan Erez (CoRR, 2020)

Reducing Activation Recomputation in Large Transformer Models. - Vijay Anand Korthikanti; Jared Casper; Sangkug Lym; Lawrence McAfee; Michael Andersch; Mohammad Shoeybi; Bryan Catanzaro (MLSys, 2023)