Enabling hardware relaxations through statistical learning

Zhuo Wang, Naveen Verma

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

4 Scopus citations

Abstract

Machine-learning algorithms are playing an increasingly important role in embedded sensing applications, by enabling the analysis of signals derived from physically complex processes. Given the severe resource constraints faced in such applications (energy, functional capacity, reliability, etc.), there is the need to think about how the algorithms can be implemented with very high efficiency. This paper examines the opportunities on three levels: (1) inherent resilience against computational errors, enabling some degree of fault tolerance; (2) top-down training of statistical models using data explicitly affected by errors, enabling substantial fault tolerance; and (3) bottom-up specification of inference kernels based on preferred hardware implementation, enabling reduced hardware complexity. Implementations employing the last two approaches are proposed and evaluated through hardware measurements and simulation.

Original languageEnglish (US)
Title of host publication2014 52nd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages319-326
Number of pages8
ISBN (Electronic)9781479980093
DOIs
StatePublished - Jan 30 2014
Event2014 52nd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2014 - Monticello, United States
Duration: Sep 30 2014Oct 3 2014

Publication series

Name2014 52nd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2014

Other

Other2014 52nd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2014
CountryUnited States
CityMonticello
Period9/30/1410/3/14

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications

Keywords

  • Embedded systems
  • Hardware reliability
  • Low-energy design
  • Sensing systems
  • Statistical learning

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  • Cite this

    Wang, Z., & Verma, N. (2014). Enabling hardware relaxations through statistical learning. In 2014 52nd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2014 (pp. 319-326). [7028472] (2014 52nd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ALLERTON.2014.7028472