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Is it approximate computing or malicious computing?

  • Ye Wang
  • , Jian Dong*
  • , Qian Xu
  • , Zhaojun Lu
  • , Gang Qu
  • *Corresponding author for this work
  • School of Computer Science and Technology, Harbin Institute of Technology
  • University of Maryland, College Park

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

Abstract

Approximate computing (AC) is an attractive energy efficient technique that can be implemented at almost all the design levels including data, algorithm, and hardware. The basic idea behind AC is to deliberately control the trade-off between computation accuracy and energy efficiency. However, with the introduction of AC, traditional computing frameworks are having many potential security vulnerabilities. In this paper, we analyze these vulnerabilities and the associated attacks as well as corresponding countermeasures. More importantly, we propose the vulnerability at data level and demonstrate that without appropriate security mechanism, adversaries can modify the data and convert a secure and trusted AC process to one that produces unexpected errors in the final output. Furthermore, it is difficult to distinguish whether such errors are caused by the approximation nature of AC or from malicious modification and injection. Finally, we propose the information hiding based countermeasures to defend against both existing attacks and the proposed data level attacks, which helps to answer the question: given an error in AC, whether it comes from approximation or it is maliciously introduced.

Original languageEnglish
Title of host publicationGLSVLSI 2020 - Proceedings of the 2020 Great Lakes Symposium on VLSI
PublisherAssociation for Computing Machinery
Pages333-338
Number of pages6
ISBN (Electronic)9781450379441
DOIs
StatePublished - 7 Sep 2020
Externally publishedYes
Event30th Great Lakes Symposium on VLSI, GLSVLSI 2020 - Virtual, Online, China
Duration: 7 Sep 20209 Sep 2020

Publication series

NameProceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI

Conference

Conference30th Great Lakes Symposium on VLSI, GLSVLSI 2020
Country/TerritoryChina
CityVirtual, Online
Period7/09/209/09/20

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Approximate computing
  • Information hiding
  • Security

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