  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.
