3 papers
cs.DC2025
Characterizing the Performance of Accelerated Jetson Edge Devices for Training Deep Learning Models
Prashanthi S. K., Sai Anuroop Kesanapalli, Yogesh Simmhan
Deep Neural Networks (DNNs) have had a significant impact on domains like autonomous vehicles and smart cities through low-latency inferencing on edge computing devices close to th…
cs.DC2024
PowerTrain: Fast, Generalizable Time and Power Prediction Models to Optimize DNN Training on Accelerated Edges
Prashanthi S. K., Saisamarth Taluri, Beautlin S +2
Accelerated edge devices, like Nvidia's Jetson with 1000+ CUDA cores, are increasingly used for DNN training and federated learning, rather than just for inferencing workloads. A u…
cs.DC2024
Performance Characterization of Containerized DNN Training and Inference on Edge Accelerators
Prashanthi S. K., Vinayaka Hegde, Keerthana Patchava +2
Edge devices have typically been used for DNN inferencing. The increase in the compute power of accelerated edges is leading to their use in DNN training also. As privacy becomes a…