On-device deep neural network (DNN) training holds the potential to enable a rich set of privacy-aware and infrastructure-independent personalized mobile applications. However, despite advancements in mobile hardware, locally training a complex DNN is still a nontrivial task given its resource demands. In this work, we show that the limited memory resources on mobile devices are the main constraint and propose Sage as a framework for efficiently optimizing memory resources for on-device DNN training. Specifically, Sage configures a flexible computation graph for DNN gradient evaluation and reduces the memory footprint of the graph using operator- and graph-level optimizations. In run-time, Sage employs a hybrid of gradient checkpointing and micro-batching techniques to dynamically adjust its memory use to the available system memory budget. Using implementation on off-the-shelf smartphones, we show that Sage enables local training of complex DNN models by reducing memory use by more than 20-fold compared to a baseline approach. We also show that Sage successfully adapts to run-time memory budget variations, and evaluate its energy consumption to show Sage's practical applicability.
|Title of host publication||MobiSys 2022 - Proceedings of the 2022 20th Annual International Conference on Mobile Systems, Applications and Services|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||13|
|Publication status||Published - 2022 Jun 27|
|Event||20th ACM International Conference on Mobile Systems, Applications and Services, MobiSys 2022 - Portland, United States|
Duration: 2022 Jun 27 → 2022 Jul 1
|Name||MobiSys 2022 - Proceedings of the 2022 20th Annual International Conference on Mobile Systems, Applications and Services|
|Conference||20th ACM International Conference on Mobile Systems, Applications and Services, MobiSys 2022|
|Period||22/6/27 → 22/7/1|
Bibliographical notePublisher Copyright:
© 2022 ACM.
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Computer Science Applications