NPU-Accelerated Imitation Learning for Thermal Optimization of QoS-Constrained Heterogeneous Multi-Cores
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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.
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