Late breaking results: Reinforcement learning-based power management policy for mobile device systems
- Title
- Late breaking results: Reinforcement learning-based power management policy for mobile device systems
- Authors
- Kwon, E.; Han, S.; Park, Y.; Kim, Y.H.; Kang, Seokhyeong
- Date Issued
- 2020-07
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Abstract
- This paper presents a power management policy that exploits reinforcement learning to increase power efficiency of mobile device systems. Our Q-learning-based policy predicts a system's characteristics and learns power management controls to adapt to the system's variations. Therefore, we can flexibly manage the system power regardless of the application scenario and can achieve lower energy per QoS compared to previous dynamic voltage/frequency scaling governors. To minimize the process overhead, we implemented our power management policy as hardware; the hardware-implemented policy reduced the average latency up to 40× compared to the software-implemented policy.
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/106208
- ISSN
- 0738-100X
- Article Type
- Conference
- Citation
- 57th ACM/IEEE Design Automation Conference, DAC 2020, 2020-07
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- There are no files associated with this item.
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