Flamingo: A User-Centric System for Fast and Energy-Efficient DNN Training on Smartphones

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.