Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

3-2022

Abstract

While many pervasive computing applications increasingly utilize real-time context extracted from a vision sensing infrastructure, the high energy overhead of DNN-based vision sensing pipelines remains a challenge for sustainable in-the-wild deployment. One common approach to reducing such energy overheads is the capture and transmission of lower-resolution images to an edge node (where the DNN inferencing task is executed), but this results in an accuracy-vs-energy tradeoff, as the DNN inference accuracy typically degrades with a drop in resolution. In this work, we introduce MRIM, a simple but effective framework to tackle this tradeoff. Under MRIM, the vision sensor platform first executes a lightweight preprocessing step to determine the saliency of different sub-regions within a single captured image frame, and then performs a saliency-aware non-uniform downscaling of individual sub-regions to produce a “mixed-resolution” image. We describe two novel low-complexity algorithms that the sensor platform can use to quickly compute suitable resolution choices for different regions under different energy/accuracy constraints. Experimental studies, involving object detection tasks evaluated traces from two benchmark urban monitoring datasets as well as a prototype Raspberry Pi-based MRIM implementation, demonstrate MRIM’s efficacy: even with unoptimized embedded platform, MRIM can provide system energy savings of 35+% or increase task accuracy by 8+%, over conventional baselines of uniform resolution downscaling or image encoding, while supporting high throughput.

Keywords

Mixed resolution, imaging tasks, energy consumption

Discipline

Graphics and Human Computer Interfaces | Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

2022 IEEE International Conference on Pervasive Computing and Communications (PerCom): Pisa Italy, March 21-25: Proceedings

First Page

44

Last Page

53

ISBN

9781665416443

Identifier

10.1109/PerCom53586.2022.9762398

Publisher

IEEE

City or Country

Piscataway, NJ

Embargo Period

5-23-2022

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/PerCom53586.2022.9762398

Download

Share

COinS