ReiNN: Efficient error resilience in artificial neural networks using encoded consistency checks

Abstract

In this research, a low cost error detection and correction approach is developed for multilayer perceptron networks, where checker neurons are used to encode hidden layer functions using independent training experiments. Error detection and correction is predicated on validating consistency properties of the encoded checks and shows that high coverage of injected errors can be achieved with extremely low computational overhead.