55 citations · 156 across the 9 of their papers we have counts for
3 papers · 1 filter
Near Data Acceleration with Concurrent Host Access
Benjamin Y. Cho, Yongkee Kwon, Sangkug Lym +1
Near-data accelerators (NDAs) that are integrated with main memory have the potential for significant power and performance benefits. Fully realizing these benefits requires the la…
DeLTA: GPU Performance Model for Deep Learning Applications with In-depth Memory System Traffic Analysis
Sangkug Lym, Donghyuk Lee, Mike O'Connor +2
Training convolutional neural networks (CNNs) requires intense compute throughput and high memory bandwidth. Especially, convolution layers account for the majority of the executio…
PruneTrain: Fast Neural Network Training by Dynamic Sparse Model Reconfiguration
Sangkug Lym, Esha Choukse, Siavash Zangeneh +3
State-of-the-art convolutional neural networks (CNNs) used in vision applications have large models with numerous weights. Training these models is very compute- and memory-resourc…