85 citations · 119 across the 9 of their papers we have counts for
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Pruning Filters while Training for Efficiently Optimizing Deep Learning Networks
Sourjya Roy, Priyadarshini Panda, Gopalakrishnan Srinivasan +1
Modern deep networks have millions to billions of parameters, which leads to high memory and energy requirements during training as well as during inference on resource-constrained…
TxSim:Modeling Training of Deep Neural Networks on Resistive Crossbar Systems
Sourjya Roy, Shrihari Sridharan, Shubham Jain +1
Resistive crossbars have attracted significant interest in the design of Deep Neural Network (DNN) accelerators due to their ability to natively execute massively parallel vector-m…
Gradual Channel Pruning while Training using Feature Relevance Scores for Convolutional Neural Networks
Sai Aparna Aketi, Sourjya Roy, Anand Raghunathan +1
The enormous inference cost of deep neural networks can be scaled down by network compression. Pruning is one of the predominant approaches used for deep network compression. Howev…