Energy-efficient digital filtering using ML-based error correction (ML-EC) technique

Jun Won Choi, Byonghyo Shim, Andrew C. Singer, Nam Ik Cho

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

1 Citation (Scopus)

Abstract

In this paper, we present a maximum likelihood-based error correction (ML-EC) technique which achieves significant power savings in digital filtering. Although voltage over-scaling (VOS) can achieve high energy efficiency, it can introduce "soft errors" which severely degrade the performance of the filter. The proposed scheme detects, estimates and corrects these soft errors via an ML-based algorithm that achieves up to 47% power savings without any SNR loss and up to 60% power savings with a 1.5 dB SNR loss for an example case study of a frequency-selective low-pass filter.

Original languageEnglish
Title of host publication2005 IEEE ICASSP '05 - Proc. - Design and Implementation of Signal Proces.Syst.,Indust. Technol. Track,Machine Learning for Signal Proces. Signal Proces. Education, Spec. Sessions
PublisherInstitute of Electrical and Electronics Engineers Inc.
PagesIV733-IV736
ISBN (Print)0780388747, 9780780388741
DOIs
Publication statusPublished - 2005
Event2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05 - Philadelphia, PA, United States
Duration: 2005 Mar 182005 Mar 23

Publication series

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

Other

Other2005 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '05
CountryUnited States
CityPhiladelphia, PA
Period05/3/1805/3/23

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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