Perfect error compensation via algorithmic error cancellation

Sujan K. Gonugondla, Byonghyo Shim, Naresh R. Shanbhag

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

Abstract

This paper presents a novel statistical error compensation (SEC) technique - algorithmic error cancellation (AEC)-for designing robust and energy-efficient signal processing and machine learning kernels on scaled process technologies. AEC exhibits a perfect error compensation (PEC) property, i.e., it is able to achieve a post-compensation error rate equal to zero. AEC generates a maximum likelihood (ML) estimate of the hardware error and employs it for error cancellation. AEC is applied to a voltage overscaled 45-tap, 45nm CMOS finite impulse response (FIR) filter employed in a EEG seizure detection system. AEC is shown to perfectly compensate for errors in the main FIR block and its reduced precision replica when they make errors at a rate of up to 73% and 98%, respectively. The AEC-based FIR is compared with an uncompensated architecture, and a fast architecture. AEC's error compensation capability enables it to achieve a 31.5% (at same supply voltage) and 19.7% (at same energy) speed-up over the uncompensated architecture, and a 8. 9% speed-up over a fast architecture at the same energy consumption. At fd, k = 452.3 MHz, AEC results in a 27.7% and 12.4% energy savings over the uncompensated and fast architectures, respectively.

Original languageEnglish (US)
Title of host publication2016 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages966-970
Number of pages5
ISBN (Electronic)9781479999880
DOIs
StatePublished - May 18 2016
Event41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016 - Shanghai, China
Duration: Mar 20 2016Mar 25 2016

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2016-May
ISSN (Print)1520-6149

Other

Other41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016
CountryChina
CityShanghai
Period3/20/163/25/16

Keywords

  • biomedical
  • energy efficiency
  • error resiliency
  • low-power
  • machine learning

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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