Statistical analysis of algorithmic noise tolerance

Eric P. Kim, Naresh R Shanbhag

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

Abstract

Algorithmic noise tolerance (ANT) is an effective statistical error compensation technique for digital signal processing systems. This paper proves a long held hypothesis that ANT has a strong Bayesian foundation, and develops an analytical framework for predicting the performance of, and designing performance-optimal ANT-based systems. ANT is shown to approximate an optimal Bayesian detector and an optimal minimum mean squared error (MMSE) estimator. We show that the theoretically optimum threshold and the optimal threshold obtained via Monte Carlo simulations agree to within 8%, with performance degradation of at most 2.1% for a variety of error probability mass functions. For a 2D-DCT implemented in a 45nm CMOS process, we find similar results where the thresholds have a 7.8% difference. Furthermore, both analysis and simulations indicate that ANT's probability of error detection is robust to the choice of the threshold.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages2731-2735
Number of pages5
DOIs
StatePublished - Oct 18 2013
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: May 26 2013May 31 2013

Publication series

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

Other

Other2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
CountryCanada
CityVancouver, BC
Period5/26/135/31/13

Keywords

  • Bayesian
  • Low-power
  • detection
  • error-resiliency
  • estimation
  • voltage overscaling

ASJC Scopus subject areas

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

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