Concurrent error detection in nonlinear digital filters using checksum linearization and residue prediction

Soft errors due to alpha particles, neutrons and environmental noise are of increasing concern due to aggressive technology scaling. While prior work has focused mostly on error resilience of linear signal processing algorithms, there is increasing need to address the same for nonlinear systems used in emerging applications for sensing and control. In this paper, a new approach for detecting errors in nonlinear digital filters is developed that does not require full duplication of all the nonlinear operations in the filter. First, a checksum of the linear least squares fit to the nonlinear function of the filter is derived that is ideally zero when the filter nonlinearities are not excited. Next, in residue prediction, linear predictive codes are used to predict the nonzero checksum error values that result exclusively from filter nonlinearity excitation. This allows fine granularity soft error detection at low hardware cost. Simulation experiments on a nonlinear Volterra filter prove the viability of the proposed concurrent error detection methodology.