4 papers
PIM-DRAM: Accelerating Machine Learning Workloads using Processing in Commodity DRAM
Sourjya Roy, Mustafa Ali, Anand Raghunathan
Deep Neural Networks (DNNs) have transformed the field of machine learning and are widely deployed in many applications involving image, video, speech and natural language processi…
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…