ABC: Abstract prediction before Concreteness

Jung Eun Kim, Richard Bradford, Man Ki Yoon, Zhong Shao

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

5 Scopus citations

Abstract

Learning techniques are advancing the utility and capability of modern embedded systems. However, the challenge of incorporating learning modules into embedded systems is that computing resources are scarce. For such a resource-constrained environment, we have developed a framework for learning abstract information early and learning more concretely as time allows. The intermediate results can be utilized to prepare for early decisions/actions as needed. To apply this framework to a classification task, the datasets are categorized in an abstraction hierarchy. Then the framework classifies intermediate labels from the most abstract level to the most concrete. Our proposed method outperforms the existing approaches and reference baselines in terms of accuracy. We show our framework with different architectures and on various benchmark datasets CIFAR-10, CIFAR-100, and GTSRB. We measure prediction times on GPUequipped embedded computing platforms as well.

Original languageEnglish (US)
Title of host publicationProceedings of the 2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020
EditorsGiorgio Di Natale, Cristiana Bolchini, Elena-Ioana Vatajelu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1103-1108
Number of pages6
ISBN (Electronic)9783981926347
DOIs
StatePublished - Mar 2020
Externally publishedYes
Event2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020 - Grenoble, France
Duration: Mar 9 2020Mar 13 2020

Publication series

NameProceedings of the 2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020

Conference

Conference2020 Design, Automation and Test in Europe Conference and Exhibition, DATE 2020
Country/TerritoryFrance
CityGrenoble
Period3/9/203/13/20

Keywords

  • adaptive concreteness
  • adaptive neural network
  • cyber-physical system
  • resource-constrained system

ASJC Scopus subject areas

  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality
  • Modeling and Simulation
  • Electrical and Electronic Engineering

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