  Application migration and dynamic voltage and frequency scaling (DVFS) are
indispensable means for fully exploiting the available potential in thermal
optimization of a heterogeneous clustered multi-core processor under
user-defined quality of service (QoS) targets. However, selecting the core to
execute each application and the voltage/frequency (V/f) levels of each cluster
is a complex problem because 1) the diverse characteristics and QoS targets of
applications require different optimizations, and 2) per-cluster DVFS requires
a global optimization considering all running applications. State-of-the-art
resource management techniques for power or temperature minimization either
rely on measurements that are often not available (such as power) or fail to
consider all the dimensions of the problem (e.g., by using simplified
analytical models). Imitation learning (IL) enables to use the optimality of an
oracle policy, yet at low run-time overhead, by training a model from oracle
demonstrations. We are the first to employ IL for temperature minimization
under QoS targets. We tackle the complexity by training a neural network (NN)
and accelerate the NN inference using a neural processing unit (NPU). While
such NN accelerators are becoming increasingly widespread on end devices, they
are so far only used to accelerate user applications. In contrast, we use an
existing accelerator on a real platform to accelerate NN-based resource
management. Our evaluation on a HiKey 970 board with an Arm big.LITTLE CPU and
an NPU shows significant temperature reductions at a negligible run-time
overhead, with unseen applications and different cooling than used for
training.
