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One efficient approach to control chip-wide thermal distribution inmulti-core systems is the optimization of online assignments of tasks toprocessing cores. Online task assignment, however, faces several uncertaintiesin real-world Systems and does not show a deterministic nature. In this paper,we consider the operation of a thermal-aware task scheduler, dispatching tasksfrom an arrival queue as well as setting the voltage and frequency of theprocessing 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 thetask scheduler as a semi-Markov decision problem (SMDP). Then, to solve theformulated SMDP, we propose two reinforcement learning algorithms that arecapable of computing the optimal task assignment policy without requiring thestatistical knowledge of the stochastic dynamics underlying the system states.The proposed algorithms also rely on function approximation techniques tohandle the infinite length of the task queue as well as the continuous natureof temperature readings. Compared to related research, the simulation resultsshow a nearly 6 Kelvin reduction in system average peak temperature and 66milliseconds decrease in mean task service time.

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