Neural schedulers based on deep reinforcement learning (DRL) have shown
considerable potential for solving real-world resource allocation problems, as
they have demonstrated significant performance gain in the domain of cluster
computing. In this paper, we investigate the feasibility of neural schedulers
for the domain of System-on-Chip (SoC) resource allocation through extensive
experiments and comparison with non-neural, heuristic schedulers. The key
finding is three-fold. First, neural schedulers designed for cluster computing
domain do not work well for SoC due to i) heterogeneity of SoC computing
resources and ii) variable action set caused by randomness in incoming jobs.
Second, our novel neural scheduler technique, Eclectic Interaction Matching
(EIM), overcomes the above challenges, thus significantly improving the
existing neural schedulers. Specifically, we rationalize the underlying reasons
behind the performance gain by the EIM-based neural scheduler. Third, we
discover that the ratio of the average processing elements (PE) switching delay
and the average PE computation time significantly impacts the performance of
neural SoC schedulers even with EIM. Consequently, future neural SoC scheduler
design must consider this metric as well as its implementation overhead for
practical utility.

通过与非神经、启发式任务调度器的比较，本文通过广泛实验研究和深度强化学习探究了基于 DRL 的神经调度器在 SoC 资源分配领域的实现可能性，并提出了 Eclectic Interaction Matching 技术以改进现有的神经调度器