An Approximation-based Fault Detection Scheme for Image Processing Applications
Matteo Biasiellia, Luca Cassanob and Antonio Mielec
Dip. Elettronica, Informazione e Bioingegneria – Politecnico di Milano – Italy
aMatteo.Biasielli@polimi.it
bLuca.Cassano@polimi.it
cAntonio.Miele@polimi.it
ABSTRACT
Image processing applications expose an intrinsic resilience to faults. In this application field the classical Duplication with Comparison (DWC) scheme, where output images are discarded as soon as the two replicas’ outputs differ for at least one pixel, may be over-conseravative. This paper introduces a novel lightweight fault detection scheme for image processing applications; i) it extends the DWC scheme by substituting one of the two exact replicas with a faster approximated one; and ii) it features a Neural Network-based checker designed to distinguish between usable and unusable images instead of faulty/unfaulty ones. The application of the hardening scheme on a case study has shown an execution time reduction from 27% to 34% w.r.t. the DWC, while guaranteeing a comparable fault detection capability.
Keywords: Approximate Computing, Convolutional Neural Networks, Fault Detection, Image Processing.
