5 citations · 16 across the 10 of their papers we have counts for
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cs.DC2022★ 5 cited
COMET: A Comprehensive Cluster Design Methodology for Distributed Deep Learning Training
Divya Kiran Kadiyala, Saeed Rashidi, Taekyung Heo +3
Modern Deep Learning (DL) models have grown to sizes requiring massive clusters of specialized, high-end nodes to train. Designing such clusters to maximize both performance and ut…
cs.AR2022★ 3 cited
Enabling Flexibility for Sparse Tensor Acceleration via Heterogeneity
Eric Qin, Raveesh Garg, Abhimanyu Bambhaniya +5
Recently, numerous sparse hardware accelerators for Deep Neural Networks (DNNs), Graph Neural Networks (GNNs), and scientific computing applications have been proposed. A common ch…