Paired Training Framework for Time-Constrained Learning

Jung Eun Kim, Richard Bradford, Max Del Giudice, Zhong Shao

Research output: Chapter in Book/Entry/PoemConference contribution


This paper presents a design framework for machine learning applications that operate in systems such as cyber-physical systems where time is a scarce resource. We manage the tradeoff between processing time and solution quality by performing as much preprocessing of data as time will allow. This approach leads us to a design framework in which there are two separate learning networks: one for preprocessing and one for the core application functionality. We show how these networks can be trained together and how they can operate in an anytime fashion to optimize performance.

Original languageEnglish (US)
Title of host publicationProceedings of the 2021 Design, Automation and Test in Europe, DATE 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9783981926354
StatePublished - Feb 1 2021
Externally publishedYes
Event2021 Design, Automation and Test in Europe Conference and Exhibition, DATE 2021 - Virtual, Online
Duration: Feb 1 2021Feb 5 2021

Publication series

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


Conference2021 Design, Automation and Test in Europe Conference and Exhibition, DATE 2021
CityVirtual, Online

ASJC Scopus subject areas

  • General Engineering


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