@inproceedings{3704733816bd4479a657230800399820,
title = "Flamingo: A User-Centric System for Fast and Energy-Efficient DNN Training on Smartphones",
abstract = "Training DNNs on a smartphone system-on-A-chip (SoC) without carefully considering its resource constraints leads to suboptimal training performance and significantly affects user experience. To this end, we present Flamingo, a system for smartphones that optimizes DNN training for time and energy under dynamic resource availability, by scaling parallelism and exploiting compute heterogeneity in real-Time. As AI becomes a part of the mainstream smartphone experience, the need to train on-device becomes crucial to fine-Tune predictive models while ensuring data privacy. Our experiments show that Flamingo achieves significant improvement in reducing time (12×) and energy (8×) for on-device training, while nearly eliminating detrimental user experience. Extensive large-scale evaluations show that Flamingo can improve end-To-end training performance by 1.2-23.3× and energy efficiency by 1.6-7× over the state-of-The-Art.",
keywords = "energy efficiency, federated learning, training latency, user experience",
author = "Singapuram, \{Sanjay Sri Vallabh\} and Chuheng Hu and Fan Lai and Chengsong Zhang and Mosharaf Chowdhury",
note = "We thank the anonymous reviewers for their insightful feedback which improved the final paper. We thank the CloudLab team for providing GPU servers for our experiments. We also thank members of the SymbioticLab, Vasudha Varadarajan and Nishil Talati for their valuable comments and suggestions. This work was supported in part by NSF grant CNS-2106184 and a grant from Cisco.; 4th International Workshop on Distributed Machine Learning, DistributedML 2023 ; Conference date: 08-12-2023",
year = "2023",
month = dec,
day = "8",
doi = "10.1145/3630048.3630183",
language = "English (US)",
series = "DistributedML 2023 - Proceedings of the 4th International Workshop on Distributed Machine Learning",
publisher = "Association for Computing Machinery",
pages = "1--10",
booktitle = "DistributedML 2023 - Proceedings of the 4th International Workshop on Distributed Machine Learning",
address = "United States",
}