Taming Algorithmic Priority Inversion in Mission-Critical Perception Pipelines.
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 im...
| Publicado en: | Communications of the ACM Vol. 67; no. 2; pp. 110 - 118 |
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| Autores principales: | , , , , , , , , |
| Formato: | Artículo |
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Association for Computing Machinery
Feb2024
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=175048203&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 175048203 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00010782 ACM jtl: Communications of the ACM issn: 00010782 maglogo: N pubinfo: dt: Feb2024 vid: 67 iid: 2 pid: 68 pub: Association for Computing Machinery artinfo: ui: 175048203 10.1145/3610801 ppf: 110 ppct: 8 formats: tig: atl: Taming Algorithmic Priority Inversion in Mission-Critical Perception Pipelines. aug: au: Liu, Shengzhong Yao, Shuochao Fu, Xinzhe Tabish, Rohan Yu, Simon Bansal, Ayoosh Yun, Heechul Sha, Lui Abdelzaher, Tarek affil: University of Illinois at Urbana-Champaign, Urbana, IL, USA George Mason University, Fairfax, VA, USA Massachusetts Institute of Technology, Cambridge, MA, USA University of Kansas, Lawrence, KS, USA su: Algorithms Systems design Cyber physical systems Computer scheduling Artificial intelligence Artificial neural networks First in, first out (Queuing theory) sug: subj: Algorithms Systems design Cyber physical systems Computer scheduling Artificial intelligence Artificial neural networks First in, first out (Queuing theory) ab: 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. pubtype: Periodical doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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