1.1k citations · 1.7k across the 2 of their papers we have counts for
2 papers
stat.ML2017★ 661 cited
To prune, or not to prune: exploring the efficacy of pruning for model compression
Michael Zhu, Suyog Gupta
Model pruning seeks to induce sparsity in a deep neural network's various connection matrices, thereby reducing the number of nonzero-valued parameters in the model. Recent reports…
cs.LG2015★ 1.1k cited
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…