A Safe, Secure, and Predictable Software Architecture for Deep Learning in Safety-Critical Systems

Abstract

In the last decade, deep learning techniques reached human-level performance in several specific tasks as image recognition, object detection, and adaptive control. For this reason, deep learning is being seriously considered by the industry to address difficult perceptual and control problems in several safety-critical applications (e.g., autonomous driving, robotics, and space missions). However, at the moment, deep learning software poses a number of issues related to safety, security, and predictability, which prevent its usage in safety-critical systems. This letter proposes a visionary software architecture that allows embracing deep learning while guaranteeing safety, security, and predictability by design. To achieve this goal, the architecture integrates multiple and diverse technologies, as hypervisors, run time monitoring, redundancy with diversity, predictive fault detection, fault recovery, and predictable resource management. Open challenges that stems from the proposed architecture are finally discussed.