10 citations · 17 across the 3 of their papers we have counts for
3 papers
cs.LG2022
Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices
Kshitij Bhardwaj, James Diffenderfer, Bhavya Kailkhura +1
The prediction accuracy of the deep neural networks (DNNs) after deployment at the edge can suffer with time due to shifts in the distribution of the new data. To improve robustnes…
cs.LG2021★ 7 cited
Semi-supervised on-device neural network adaptation for remote and portable laser-induced breakdown spectroscopy
Kshitij Bhardwaj, Maya Gokhale
Laser-induced breakdown spectroscopy (LIBS) is a popular, fast elemental analysis technique used to determine the chemical composition of target samples, such as in industrial anal…
cs.LG2019★ 10 cited
SMAUG: End-to-End Full-Stack Simulation Infrastructure for Deep Learning Workloads
Sam Likun Xi, Yuan Yao, Kshitij Bhardwaj +3
In recent years, there has been tremendous advances in hardware acceleration of deep neural networks. However, most of the research has focused on optimizing accelerator microarchi…