Special Session: Does Approximation Make Testing Harder (or Easier)?

R. Iris Bahar, Ulya Karpuzcu, Sasa Misailovic

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

2 Scopus citations


Many important application domains, including machine learning, feature intrinsically noise tolerant algorithms. These algorithms process massive, yet noisy and redundant data, by probabilistic and often iterative techniques. As a result, there is a range of valid outputs rather than a single golden value. While this may translate into relaxed constraints for testing and verification of approximate systems, distinguishing actual design bugs from what is being approximated also becomes harder. In this paper, using representative case studies, we pose several challenges for the test and verification community as approximate computing becomes more prevalent as a design of choice in order to achieve performance gains, power or energy savings, improved reliability or reduced software and/or hardware complexity.

Original languageEnglish (US)
Title of host publication2019 IEEE 37th VLSI Test Symposium, VTS 2019
PublisherIEEE Computer Society
ISBN (Electronic)9781728111704
StatePublished - Apr 2019
Event37th IEEE VLSI Test Symposium, VTS 2019 - Monterey, United States
Duration: Apr 23 2019Apr 25 2019

Publication series

NameProceedings of the IEEE VLSI Test Symposium


Conference37th IEEE VLSI Test Symposium, VTS 2019
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2019 IEEE.


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