Binary Recursive Estimation on Noisy Hardware

Elsa Dupraz, Lav R. Varshney

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

Abstract

Recursive estimation is a basic operation in statistical inference that may be implemented and deployed on faulty hardware with error rates governed by energy consumption. We analyze the loss in estimation performance due to noise in recursive probability computation for the binary case, and develop an optimal energy allocation strategy. Simulations show the validity of analytical bounds.

Original languageEnglish (US)
Title of host publication2019 IEEE International Symposium on Information Theory, ISIT 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages877-881
Number of pages5
ISBN (Electronic)9781538692912
DOIs
StatePublished - Jul 2019
Event2019 IEEE International Symposium on Information Theory, ISIT 2019 - Paris, France
Duration: Jul 7 2019Jul 12 2019

Publication series

NameIEEE International Symposium on Information Theory - Proceedings
Volume2019-July
ISSN (Print)2157-8095

Conference

Conference2019 IEEE International Symposium on Information Theory, ISIT 2019
CountryFrance
CityParis
Period7/7/197/12/19

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Information Systems
  • Modeling and Simulation
  • Applied Mathematics

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  • Cite this

    Dupraz, E., & Varshney, L. R. (2019). Binary Recursive Estimation on Noisy Hardware. In 2019 IEEE International Symposium on Information Theory, ISIT 2019 - Proceedings (pp. 877-881). [8849626] (IEEE International Symposium on Information Theory - Proceedings; Vol. 2019-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIT.2019.8849626