Conjugate gradients based stochastic adaptive filters

Chandrasekhar Radhakrishnan, Andrew Singer

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

Abstract

Reliable execution of optimization algorithms is an essential requirement in both digital signal processing (DSP) and machine learning applications. DSP systems designed using nanoscale process technologies are susceptible to transient errors. In addition, power saving techniques like voltage over-scaling can also cause reliability issues in circuits. These errors often manifest themselves as large magnitude errors at the application level and can considerably slow down the convergence speed of the chosen algorithm. In this work we explore the behavior of Conjugate Gradient (CG) algorithm under stochastic computational errors. The expanding subspace property and modular redundancy is exploited to develop a robust conjugate gradient based method with applications in adaptive filtering and machine learning.

Original languageEnglish (US)
Title of host publicationConference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
EditorsMichael B. Matthews
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1569-1572
Number of pages4
ISBN (Electronic)9781538618233
DOIs
StatePublished - Apr 10 2018
Event51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 - Pacific Grove, United States
Duration: Oct 29 2017Nov 1 2017

Publication series

NameConference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Volume2017-October

Other

Other51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
CountryUnited States
CityPacific Grove
Period10/29/1711/1/17

ASJC Scopus subject areas

  • Control and Optimization
  • Computer Networks and Communications
  • Hardware and Architecture
  • Signal Processing
  • Biomedical Engineering
  • Instrumentation

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