AI Meets Real-Time: Addressing Real-World Complexities in Graph Response-Time Analysis

Abstract

Artificial-intelligence algorithms are enabling ever more sophisticated autonomous features in safety-critical application domains. These algorithms can be quite complex—consisting of many tasks interconnected in processing graphs—and often must execute on complex heterogeneous hardware—typically multicore machines augmented with one or more hardware accelerators. To further complicate matters, these processing graphs often must be supported in contexts where a large system is broken into smaller components. With this confluence of factors, existing response-time analysis for processing graphs is not applicable. In this paper, such analysis is extended to address these complexities in systems where components are isolated via time partitioning. Additionally, graph restructuring methods are presented that enable response-time bounds to be reduced.