Toward predictable AI-based real-time systems

The outstanding performance of deep neural networks and machine learning algorithms is driving widespread adoption of these technologies across various application domains, including safety-critical systems like self-driving cars, autonomous robots, and medical diagnostic support systems. However, most deep learning models were not designed to guarantee safe, secure, and predictable behavior. Hence, several challenges must be addressed at multiple architectural levels to ensure their reliability and trustworthiness. This paper discusses some key issues related to AI-powered embedded systems, proposing potential solutions and research directions aimed at enhancing their security, safety, and predictability.