Error-adaptive classifier boosting (EACB): Exploiting data-driven training for highly fault-tolerant hardware

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

12 Scopus citations

Abstract

Technological scaling and system-complexity scaling have dramatically increased the prevalence of hardware faults, to the point where traditional approaches based on design margining are becoming un-viable. The challenges are exacerbated in embedded sensing applications due to constraints on system resources (energy, area). Given the importance of classification functions in such applications, this paper presents an architecture for overcoming faults within a classification processor. The approach employs machine learning for modeling not only complex sensor signals but also error manifestations due to hardware faults. Adaptive boosting is exploited in the architecture for performing iterative data-driven training. This enables the effects of faults in preceding iterations to be modeled and overcome during subsequent iterations. We demonstrate a system integrating the proposed classifier, capable of training its model entirely within the architecture by generating estimated training labels. FPGA experiments show that high fault rates (affecting >3% of all circuit nodes) occurring on >80% of the hardware can be overcome, restoring system performance to fault-free levels.

Original languageEnglish (US)
Title of host publication2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3884-3888
Number of pages5
ISBN (Print)9781479928927
DOIs
StatePublished - Jan 1 2014
Event2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 - Florence, Italy
Duration: May 4 2014May 9 2014

Publication series

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

Other

Other2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014
CountryItaly
CityFlorence
Period5/4/145/9/14

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • Boosting
  • Circuit faults
  • Fault tolerance
  • Machine learning
  • Sensor systems

Fingerprint Dive into the research topics of 'Error-adaptive classifier boosting (EACB): Exploiting data-driven training for highly fault-tolerant hardware'. Together they form a unique fingerprint.

  • Cite this

    Wang, Z., Schapire, R., & Verma, N. (2014). Error-adaptive classifier boosting (EACB): Exploiting data-driven training for highly fault-tolerant hardware. In 2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014 (pp. 3884-3888). [6854329] (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2014.6854329