1.1k citations · 2.2k across the 10 of their papers we have counts for
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Staleness-aware Async-SGD for Distributed Deep Learning
Wei Zhang, Suyog Gupta, Xiangru Lian +1
Deep neural networks have been shown to achieve state-of-the-art performance in several machine learning tasks. Stochastic Gradient Descent (SGD) is the preferred optimization algo…
Model Accuracy and Runtime Tradeoff in Distributed Deep Learning:A Systematic Study
Suyog Gupta, Wei Zhang, Fei Wang
This paper presents Rudra, a parameter server based distributed computing framework tuned for training large-scale deep neural networks. Using variants of the asynchronous stochast…
Deep Learning with Limited Numerical Precision
Suyog Gupta, Ankur Agrawal, Kailash Gopalakrishnan +1
Training of large-scale deep neural networks is often constrained by the available computational resources. We study the effect of limited precision data representation and computa…