Work-in-Progress: Reinforcement Learning-Based DAG Scheduling Algorithm in Clustered Many-Core Platform

Abstract

Embedded systems have become extensive, complex, and automated; thus, increasingly, computing platforms for such systems are being transformed into multi-/many-core platforms. Typically, self-driving systems, involve various applications that run simultaneously, and such systems require low power consumption and large-scale computation. A many-core processor with instructions, multiple data architecture can satisfy these requirements. Shortening the time required to execute all tasks (i.e., makespan) is an important objective in task scheduling for parallel real-time systems, such as self-driving system. Machine learning algorithms have been introduced to solve this kind of problem. This paper proposes a reinforcement learning-based scheduling algorithm for parallel real-time systems represented by a directed acyclic graph (DAG), and Kalray's MPPA3-80 is used as a target many-core processor.