Can We Trust AI-Powered Real-Time Embedded Systems? (Invited Paper)
Some of the major problems existing today in AI-powered embedded systems are presented, highlighting possible solutions and research directions to support them, increasing their security, safety, and time predictability.
Abstract
The excellent performance of deep neural networks and machine learning algorithms is pushing the industry to adopt such a technology in several application domains, including safety-critical ones, as self-driving vehicles, autonomous robots, and diagnosis support systems for medical applications. However, most of the AI methodologies available today have not been designed to work in safety-critical environments and several issues need to be solved, at different architecture levels, to make them trustworthy. This paper presents some of the major problems existing today in AI-powered embedded systems, highlighting possible solutions and research directions to support them, increasing their security, safety, and time predictability.