The Good, the Bad, Algorithmic Noise Tolerance (Ant), the Ugly

Noyan C. Sevuktekin, Andrew Carl Singer

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

Abstract

Computational units implemented on nanoscale physical substrates are susceptible to errors that can be catastrophic if not mitigated. Statistical error compensation techniques have become prevalent to safeguard computational units against such hardware-failures. Algorithmic Noise Tolerance (ANT) is one such technique that utilizes a low-fidelity replica unit to detect and bypass such failures occurring within the primary (main) computational unit. Connections between ANT and the binary hypothesis testing as well as the information theoretic CEO problem have been explored for sub-exponential error profiles, quadratic and logarithmic distortion functions. However, there exist fundamental performance limits of ANT approach even without such model-dependent restrictions. The purpose of this paper is to explore fidelity-dependent conditions that are universal over the statistical properties of the computational units under which, the overall performance of ANT is arbitrarily close to the fundamental limits.

Original languageEnglish (US)
Title of host publication2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5366-5370
Number of pages5
ISBN (Electronic)9781479981311
DOIs
StatePublished - May 2019
Event44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Brighton, United Kingdom
Duration: May 12 2019May 17 2019

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2019-May
ISSN (Print)1520-6149

Conference

Conference44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019
CountryUnited Kingdom
CityBrighton
Period5/12/195/17/19

Keywords

  • Algorithmic Noise Tolerance
  • Calibration
  • Decision
  • Mixture Models
  • Statistical Error Compensation

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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  • Cite this

    Sevuktekin, N. C., & Singer, A. C. (2019). The Good, the Bad, Algorithmic Noise Tolerance (Ant), the Ugly. In 2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings (pp. 5366-5370). [8683247] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol. 2019-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2019.8683247