Scheduling for Cyber-Physical Systems with Heterogeneous Processing Units under Real-World Constraints

Abstract

Cyber-physical systems (CPS) such as robots and self-driving cars pose strict physical requirements to avoid failure. The scheduling choices impact these requirements. This presents a challenge: How do we find efficient schedules for CPS with heterogeneous processing units, such that the schedules are resource-bounded to meet the physical requirements? For example, tasks that require significant computation time in a self-driving car can delay reaction, decreasing available braking time. Heterogeneous computing systems — containing CPUs, GPUs, and other types of domain-specific accelerators — offer effective capabilities to reduce computation time or energy consumption and expand such operating conditions. However, doing so under physical requirements presents several challenges that existing scheduling solutions fail to address.