Open Access System for Information Sharing

Login Library

 

Conference
Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

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
Files in This Item:
There are no files associated with this item.

qr_code

  • mendeley

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Views & Downloads

Browse