On Removing Algorithmic Priority Inversion from Mission-critical Machine Inference Pipelines

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The paper discusses algorithmic priority inversion in mission-critical machine inference pipelines used in modern neural-network-based cyber-physical applications, and develops a scheduling solution to mitigate its effect. In general, priority inversion occurs in real-time systems when computations that are of lower priority are performed together with or ahead of those that are of higher priority.1 In current machine intelligence software, significant priority inversion occurs on the path from perception to decision-making, where the execution of underlying neural network algorithms does not differentiate between critical and less critical data. We describe a scheduling framework to resolve this problem, and demonstrate that it improves the system's ability to react to critical inputs, while at the same time reducing platform cost.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE 41st Real-Time Systems Symposium, RTSS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages14
ISBN (Electronic)9781728183244
StatePublished - Dec 2020
Event41st IEEE Real-Time Systems Symposium, RTSS 2020 - Virtual, Houston, United States
Duration: Dec 1 2020Dec 4 2020

Publication series

NameProceedings - Real-Time Systems Symposium
ISSN (Print)1052-8725


Conference41st IEEE Real-Time Systems Symposium, RTSS 2020
Country/TerritoryUnited States
CityVirtual, Houston


  • Algorithmic Priority Inversion
  • Cyber-Physical Systems (CPS)
  • Machine Inference

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications


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