Deep Learning Reliability: Towards Mitigating Reliability Threats in Deep Learning Systems by Exploiting Intrinsic Characteristics of DNNs

Deep Learning (DL) models have shown remarkable performance for a variety of AI applications such as image classification, object detection, semantic segmentation, and natural language processing. Due to their state-of-the-art performance, DL models are also being used in safety-critical applications, e.g., autonomous driving and smart healthcare. However, these applications have strict robustness constraints which have to be satisfied to meet the industrial standards. Therefore, the robustness of these DL models against different reliability threats has to be thoroughly studied and methods have to be designed to improve their inherent resilience against all types of reliability threats. Towards this, this chapter first highlights different types of reliability threats that can significantly degrade the performance of a DL system. The chapter also highlight different error-resilience characteristics of DL models that can be exploited to design low-cost fault-mitigation techniques. The chapter then covers different techniques for mitigating hardware-induced reliability threats in DL systems without incurring high design-time and run-time overheads.