Cross-layer approaches for improving the dependability of deep learning systems

Deep Neural Networks (DNNs) - the state-of-the-art computational models for many Artificial Intelligence (AI) applications - are inherently compute and resource-intensive and, hence, cannot exploit traditional redundancy-based fault mitigation techniques for enhancing the dependability of DNN-based systems. Therefore, there is a dire need to search for alternate methods that can improve their reliability without high expenditure of resources by exploiting the intrinsic characteristics of these networks. In this paper, we present cross-layer approaches that, based on the intrinsic characteristics of DNNs, employ software and hardware-level modifications for improving the resilience of DNN-based systems to hardware-level faults, e.g., soft errors and permanent faults.