Reliability Assessment Methodologies for ANN-based Systems

Abstract

In recent decades, deep learning (DL)-based solutions have gained a great deal of interest in industry and academia due to their outstanding computational capabilities. The usage of electronic devices running applications based on Artificial Neural Networks (ANNs) is spreading in several areas, including safety-critical applications such as self-driving cars, robots, and space applications. ANNs are often regarded as inherently robust and fault-tolerant, being brain-inspired and redundant computing models. However, to use them safely in human contexts, there is a compelling need to assess their reliability. Indeed, when they are deployed on resource-constrained hardware devices, single physical faults might jeopardize the activity of multiple neurons, leading to undesirable results. Since reliability assessment is becoming a growing concern, many efforts have been made in recent decades to propose efficient approaches to assess ANN-based systems reliability. The intent of this article is to overview the main reliability assessment methodologies for ANN-based systems, focusing mainly on Fault Injection techniques used to evaluate the ANN resilience at different abstraction levels.