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System level power management must consider the uncertainty and variability that come from the environment, the application and the hardware. A robust power management technique must be able to learn the optimal decision from past events and improve itself as the environment changes. This article presents a novel on-line power management technique based on model-free constrained reinforcement learning (Q-learning). The proposed learning algorithm requires no prior information of the workload and dynamically adapts to the environment to achieve autonomous power management. We focus on the power management of the peripheral device and the microprocessor, two of the basic components of a computer. Due to their different operating behaviors and performance considerations, these two types of devices require different designs of Q-learning agent. The article discusses system modeling and cost function construction for both types of Q-learning agent. Enhancement techniques are also proposed to speed up the convergence and better maintain the required performance (or power) constraint in a dynamic system with large variations. Compared with the existing machine learning based power management techniques, the Q-learning based power management is more flexible in adapting to different workload and hardware and provides a wider range of power-performance tradeoff.

Without bells and whistles, VisTR achieves the highest speed among all existing VIS models, and achieves the best result among methods using single model on the YouTube-VIS dataset. For the first time, we demonstrate a much simpler and faster video instance segmentation framework built upon Transformers, achieving competitive accuracy. We hope that VisTR can motivate future research for more video understanding tasks.