PowerTrain: Fast, Generalizable Time and Power Prediction Models to Optimize DNN Training on Accelerated Edges

18 July 2024

Papers citing "PowerTrain: Fast, Generalizable Time and Power Prediction Models to Optimize DNN Training on Accelerated Edges"

9 / 9 papers shown

Title
Predicting Forest Fire Using Remote Sensing Data And Machine Learning Suwei Yang M. Lupascu Kuldeep S. Meel 116 34 0 06 Jan 2021
HoughNet: Integrating near and long-range evidence for bottom-up object detection Nermin Samet Samet Hicsonmez Emre Akbas ObjD 80 47 0 05 Jul 2020
Characterizing and Modeling Distributed Training with Transient Cloud GPU Servers Shijian Li R. Walls Tian Guo 38 23 0 07 Apr 2020
TiFL: A Tier-based Federated Learning System Zheng Chai Ahsan Ali Syed Zawad Stacey Truex Ali Anwar Nathalie Baracaldo Yi Zhou Heiko Ludwig Feng Yan Yue Cheng FedML 49 279 0 25 Jan 2020
Searching for MobileNetV3 Andrew G. Howard Mark Sandler Grace Chu Liang-Chieh Chen Bo Chen ... Yukun Zhu Ruoming Pang Vijay Vasudevan Quoc V. Le Hartwig Adam 348 6,782 0 06 May 2019
Towards Federated Learning at Scale: System Design Keith Bonawitz Hubert Eichner W. Grieskamp Dzmitry Huba A. Ingerman ... H. B. McMahan Timon Van Overveldt David Petrou Daniel Ramage Jason Roselander FedML 121 2,666 0 04 Feb 2019
Know What You Don't Know: Unanswerable Questions for SQuAD Pranav Rajpurkar Robin Jia Percy Liang RALM ELM 279 2,845 0 11 Jun 2018
Incremental Learning of Object Detectors without Catastrophic Forgetting K. Shmelkov Cordelia Schmid Alahari Karteek ObjD 73 520 0 23 Aug 2017
Communication-Efficient Learning of Deep Networks from Decentralized Data H. B. McMahan Eider Moore Daniel Ramage S. Hampson Blaise Agüera y Arcas FedML 406 17,486 0 17 Feb 2016