Taming Algorithmic Priority Inversion in Mission-Critical Perception Pipelines
Author(s)
Liu, Shengzhong
Yao, Shuochao
Fu, Xinzhe
Tabish, Rohan
Yu, Simon
Bansal, Ayoosh
Yun, Heechul
Sha, Liu
Abdelzaher, Tarek
Date Issued
January 25, 2024
Publisher
ACM|Communications of the ACM
Citation
Liu, Shengzhong, Yao, Shuochao, Fu, Xinzhe, Tabish, Rohan, Yu, Simon et al. 2024. "Taming Algorithmic Priority Inversion in Mission-Critical Perception Pipelines."
Version
Final published version
Abstract
The paper discusses algorithmic priority inversion in mission-critical machine inference pipelines used in modern neural-network-based perception subsystems and describes a solution to mitigate its effect. In general, priority inversion occurs in computing systems when computations that are “less important” are performed together with or ahead of those that are “more important.” Significant priority inversion occurs in existing machine inference pipelines when they do not differentiate between critical and less critical data. We describe a framework to resolve this problem and demonstrate that it improves a perception system’s ability to react to critical inputs, while at the same time reducing platform cost.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3610801
