Conjugate gradients based stochastic adaptive filters

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.