Xu, R., Lee, J., Wang, P., Bagchi, S., Li, Y., and Chaterji, S. (2022). LiteReconfig: cost and content aware reconfiguration of video object detection systems for mobile GPUs. Proceedings of the Seventeenth European Conference on Computer Systems, ACM-EuroSys 2022.

Paper BibTeX Alternative Link News Recording at ACM-EuroSys 2022

Students
Ran Xu, Jayoung Lee, Pengcheng Wang

Abstract
An adaptive video object detection system selects different execution paths at runtime, based on video content and available resources, so as to maximize accuracy under a target latency objective (e.g., 30 frames per second). Such a system is well suited to mobile devices with limited computing resources, and often running multiple contending applications. Existing solutions suffer from two major drawbacks. First, collecting feature values to decide on an execution branch is expensive. Second, there is a switching overhead for transitioning between branches and this overhead depends on the transition pair. LiteReconfig, an efficient and adaptive video object detection framework, addresses these challenges. LiteReconfig features a cost-benefit analyzer to decide which features to use, and which execution branch to run, at inference time. Furthermore, LiteReconfig has a content-aware accuracy prediction model, to select an execution branch tailored for frames in a video stream. We demonstrate that LiteReconfig achieves significantly improved accuracy under a set of varying latency objectives than existing systems, while maintaining up to 50 fps on an NVIDIA AGX Xavier board. Our code, with DOI, is available at https://doi.org/10.5281/zenodo.6345733.