Timing-Predictable Vision Processing for Autonomous Systems

Tanya Amert, Michael Balszun, Martin Geier, F. Donelson Smith, James H. Anderson, Samarjit Chakraborty

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Vision processing for autonomous systems today involves implementing machine learning algorithms and vision processing libraries on embedded platforms consisting of CPUs, GPUs and FPGAs. Because many of these use closed-source proprietary components, it is very difficult to perform any timing analysis on them. Even measuring or tracing their timing behavior is challenging, although it is the first step towards reasoning about the impact of different algorithmic and implementation choices on the end-to-end timing of the vision processing pipeline. In this paper we discuss some recent progress in developing tracing, measurement and analysis infrastructure for determining the timing behavior of vision processing pipelines implemented on state-of-the-art FPGA and GPU platforms.

Original languageEnglish
Title of host publicationProceedings of the 2021 Design, Automation and Test in Europe, DATE 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1739-1744
Number of pages6
ISBN (Electronic)9783981926354
DOIs
StatePublished - 1 Feb 2021
Externally publishedYes
Event2021 Design, Automation and Test in Europe Conference and Exhibition, DATE 2021 - Virtual, Online
Duration: 1 Feb 20215 Feb 2021

Publication series

NameProceedings -Design, Automation and Test in Europe, DATE
Volume2021-February
ISSN (Print)1530-1591

Conference

Conference2021 Design, Automation and Test in Europe Conference and Exhibition, DATE 2021
CityVirtual, Online
Period1/02/215/02/21

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