TY - GEN
T1 - Conjugate gradients based stochastic adaptive filters
AU - Radhakrishnan, Chandrasekhar
AU - Singer, Andrew
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85050991854
U2 - 10.1109/ACSSC.2017.8335621
DO - 10.1109/ACSSC.2017.8335621
M3 - Conference contribution
AN - SCOPUS:85050991854
T3 - Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
SP - 1569
EP - 1572
BT - Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
A2 - Matthews, Michael B.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Y2 - 29 October 2017 through 1 November 2017
ER -