NDLI: Achieving autonomous power management using reinforcement learning

Content Provider	ACM Digital Library
Author	Qiu, Qinru Shen, Hao Tan, Ying Lu, Jun Wu, Qing
Copyright Year	2013
Abstract	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.
Starting Page	1
Ending Page	32
Page Count	32
File Format	PDF
ISSN	10844309
e-ISSN	15577309
DOI	10.1145/2442087.2442095
Volume Number	18
Issue Number	2
Journal	ACM Transactions on Design Automation of Electronic Systems (TODAES)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-04-11
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Power management Computer Machine learning Thermal management
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Computer Science Applications Electrical and Electronic Engineering

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