Taming Algorithmic Priority Inversion in Mission-Critical Perception Pipelines

Shengzhong Liu, Shuochao Yao, Xinzhe Fu, Rohan Tabish, Simon Yu, Ayoosh Bansal, Heechul Yun, Lui Sha, Tarek Abdelzaher

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)110-117
Number of pages8
JournalCommunications of the ACM
Volume67
Issue number2
DOIs
StatePublished - Jan 25 2024

ASJC Scopus subject areas

  • General Computer Science

Fingerprint

Dive into the research topics of 'Taming Algorithmic Priority Inversion in Mission-Critical Perception Pipelines'. Together they form a unique fingerprint.

Cite this