@inproceedings{c47a52374fa048ed94efea3516841b41,
title = "Paired Training Framework for Time-Constrained Learning",
abstract = "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.",
author = "Kim, {Jung Eun} and Richard Bradford and Giudice, {Max Del} and Zhong Shao",
note = "Publisher Copyright: {\textcopyright} 2021 EDAA.; 2021 Design, Automation and Test in Europe Conference and Exhibition, DATE 2021 ; Conference date: 01-02-2021 Through 05-02-2021",
year = "2021",
month = feb,
day = "1",
doi = "10.23919/DATE51398.2021.9473934",
language = "English (US)",
series = "Proceedings -Design, Automation and Test in Europe, DATE",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "591--596",
booktitle = "Proceedings of the 2021 Design, Automation and Test in Europe, DATE 2021",
}