PED: Probabilistic Energy-efficient Deadline-aware scheduler for heterogeneous SoCs

Abstract

Heterogeneous systems-on-chip (SoCs) integrate diverse cores with different performance and energy tradeoffs. Scheduling applications with soft deadline constraints is highly complex in such heterogeneous platforms, and the complexity is further exacerbated by the streaming jobs generated by applications from domains such as communication and radar systems. Existing deadline-aware schedulers typically first translate the job deadlines to task-level slacks before scheduling, which is the time available for a processing element (PE) to execute a specific task. Task-level slacks are critically dependent on the task-to-PE allocation of all other tasks from the same job (intra-job) or concurrent jobs (inter-job). However, this allocation is usually unknown before the start of the scheduling process. To address the problem, we propose PED, a probabilistic energy-efficient deadline-aware scheduler for heterogeneous SoCs. PED minimizes the average tardiness of streaming jobs with the least energy consumption by accurately predicting the task-to-PE allocation using Neural Network and considering intra- and inter-job contentions when scheduling tasks. Our extensive experimental results in a domain-specific SoC (DSSoC) designed for radar and communication domains show that PED can reduce tardiness by 6.9× with comparable energy consumption; and reduce energy consumption by 14% without any loss in tardiness, when compared with state-of-the-art schedulers.