New Wine in an Old Bottle: N-Version Programming for Machine Learning Components

Abstract

We revisit N-version programming in the context of machine learning (ML). Generating N versions of an ML component does not require additional programming effort, but only extra computations. This opens up the possibility of executing hundreds of diverse replicas, which, if carefully deployed, can improve their overall reliability by a significant margin. We use mathematical modeling to evaluate these benefits.