Resilience of Deep Learning Applications: Where We are and Where We Want to Go

Deep Learning (DL) [1] is currently one of the most intensively and widely used predictive models in the field of machine learning. DL has proven to give very good results for many complex tasks and applications, such as object recognition in images/videos, natural language processing, robotics, aerospace, smart healthc are, and autonomous driving. Nowa-days, there is intense activity in designing custom Artificial Intelligence (AI) hardware accelerators to support the energy-hungry data movement, speed of computation, and memory resources that DL requires to realize its full potential [2]. Furthermore, there is an incentive to migrate AI from cloud to edge devices, i.e., Internet-of- Things devices, to address data confidentiality issues and bandwidth limitations, and also to alleviate the communication latency, especially for real-time safety-critical decisions, e.g., in autonomous driving.