On-device model training framework

In this work, we show that the limited memory resources on mobile devices are the main constraint for on-device DNN training and propose Sage as a framework for efficiently optimizing memory resources for on-device DNN training. 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.

Technical implementation of Sage framework

N/A

Academics and Industrial Researchers

GPLv3 licence