An SMDP-Based Approach to Thermal-Aware Task Scheduling in NoC-based MPSoC platforms

TL;DR

This paper introduces an SMDP-based thermal-aware task scheduling method for NoC-based MPSoC platforms, utilizing reinforcement learning to optimize temperature distribution and reduce peak temperatures without prior system knowledge.

Contribution

It presents a novel SMDP model for thermal-aware scheduling and proposes two reinforcement learning algorithms that operate without system dynamics knowledge.

Findings

6 Kelvin reduction in average peak temperature

66 milliseconds decrease in mean task service time

Effective temperature management in multi-core systems

Abstract

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.