基于SMDP的NoC-based MPSoC平台热感知任务调度方法
An SMDP-Based Approach to Thermal-Aware Task Scheduling in NoC-based MPSoC platforms
摘要：
控制多核系统中芯片范围的热分布的一种有效方法是优化任务到处理核的在线分配。然而，在线任务分配面临着现实世界中的一些不确定性，并没有表现出确定性的性质。在本文中，我们考虑热感知任务调度器的操作，从到达队列中分派任务以及设置处理核心的电压和频率以优化整个芯片的平均温度裕度（即，核心以及NoC路由器）。我们的任务调度器的决策过程建模为半马尔可夫决策问题（SMDP）。然后，为了解决制定SMDP，我们提出了两个强化学习算法，能够计算最优的任务分配策略，而不需要统计知识的随机动态系统状态。所提出的算法还依赖于函数逼近技术来处理无限长的任务队列以及温度读数的连续性。仿真结果表明，与相关研究相比，系统平均峰值温度降低了近6 K，平均任务服务时间减少了66 ms。
英文摘要：
One efficient approach to control chip-wide thermal distribution in multi-core systems is the optimization of online assignments of tasks to processing cores. Online task assignment, however, faces several uncertainties in real-world Systems and does not show a deterministic nature. In this paper, we consider the operation of a thermal-aware task scheduler, dispatching tasks from an arrival queue as well as setting the voltage and frequency of the processing cores to optimize the mean temperature margin of the entire chip (i.e., cores as well as the NoC routers). We model the decision process of the task scheduler as a semi-Markov decision problem (SMDP). Then, to solve the formulated SMDP, we propose two reinforcement learning algorithms that are capable of computing the optimal task assignment policy without requiring the statistical knowledge of the stochastic dynamics underlying the system states. The proposed algorithms also rely on function approximation techniques to handle the infinite length of the task queue as well as the continuous nature of temperature readings. Compared to related research, the simulation results show a nearly 6 Kelvin reduction in system average peak temperature and 66 milliseconds decrease in mean task service time.
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