research-article

RAVEN: Perception-aware Optimization of Power Consumption for Mobile Games

Authors:

Changyoung Koh,

Junehwa SongAuthors Info & Claims

MobiCom '17: Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking

Pages 422 - 434

https://doi.org/10.1145/3117811.3117841

Published: 04 October 2017 Publication History

Abstract

High-end mobile GPUs are now becoming an integral part of mobile devices. However, a mobile GPU constitutes a major portion of power consumption on the devices, and mobile games top as the most popular class of graphics applications. This paper presents the design and implementation of RAVEN, a novel, on-the-fly frame rate scaling system for mobile gaming applications. RAVEN utilizes human visual perception of graphics change to opportunistically achieve power saving without degrading user experiences. The system develops a light-weight frame comparison technique to measure and predict perception-aware frame similarity. It also builds a low resolution virtual display which clones the device screen for performing similarity measurement at a low-power cost. It is able to work on an existing commercial smartphone and support applications from app stores without any modifications. It has been implemented on Nexus 5X, and its performance has been measured with 13 games. The system effectively reduces the overall power consumption of mobile devices while maintaining satisfactory user experiences. The power consumption is reduced by 21.78% on aver-age and up to 34.74%.

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Cited By

Liu TLucas JHe SLiu TWang XWang W(2024)Improving Resource and Energy Efficiency for Cloud 3D through Excessive Rendering ReductionProceedings of the Nineteenth European Conference on Computer Systems10.1145/3627703.3650064(317-332)Online publication date: 22-Apr-2024
https://dl.acm.org/doi/10.1145/3627703.3650064
Li XChen JLiu YWu KGallagher J(2023)Combatting Energy Issues for Mobile ApplicationsACM Transactions on Software Engineering and Methodology10.1145/352785132:1(1-44)Online publication date: 13-Feb-2023
https://dl.acm.org/doi/10.1145/3527851
Masola ACapodieci N(2023)Optimization strategies for GPUs: an overview of architectural approachesInternational Journal of Parallel, Emergent and Distributed Systems10.1080/17445760.2023.217375238:2(140-154)Online publication date: 5-Feb-2023
https://doi.org/10.1080/17445760.2023.2173752
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Index Terms

RAVEN: Perception-aware Optimization of Power Consumption for Mobile Games
1. Computing methodologies
  1. Computer graphics
    1. Graphics systems and interfaces
      1. Perception
2. Human-centered computing
  1. Ubiquitous and mobile computing
    1. Ubiquitous and mobile computing systems and tools

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Published In

cover image ACM Conferences

MobiCom '17: Proceedings of the 23rd Annual International Conference on Mobile Computing and Networking

October 2017

628 pages

ISBN:9781450349161

DOI:10.1145/3117811

General Chair:
Kobus Van der Merwe
University of Utah
,
Program Chairs:
Ben Greenstein
Google, USA
,
Kannan Srinivasan
Ohio State University, USA

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing

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Published: 04 October 2017

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MobiCom '17: The 23rd Annual International Conference on Mobile Computing and Networking

October 16 - 20, 2017

Utah, Snowbird, USA

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MobiCom '17 Paper Acceptance Rate 35 of 186 submissions, 19%;

Overall Acceptance Rate 440 of 2,972 submissions, 15%

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Cited By

Liu TLucas JHe SLiu TWang XWang W(2024)Improving Resource and Energy Efficiency for Cloud 3D through Excessive Rendering ReductionProceedings of the Nineteenth European Conference on Computer Systems10.1145/3627703.3650064(317-332)Online publication date: 22-Apr-2024
https://dl.acm.org/doi/10.1145/3627703.3650064
Li XChen JLiu YWu KGallagher J(2023)Combatting Energy Issues for Mobile ApplicationsACM Transactions on Software Engineering and Methodology10.1145/352785132:1(1-44)Online publication date: 13-Feb-2023
https://dl.acm.org/doi/10.1145/3527851
Masola ACapodieci N(2023)Optimization strategies for GPUs: an overview of architectural approachesInternational Journal of Parallel, Emergent and Distributed Systems10.1080/17445760.2023.217375238:2(140-154)Online publication date: 5-Feb-2023
https://doi.org/10.1080/17445760.2023.2173752
Zhang YWang RHuo YHua WBao H(2022)PowerNet: Learning-Based Real-Time Power-Budget RenderingIEEE Transactions on Visualization and Computer Graphics10.1109/TVCG.2021.306436728:10(3486-3498)Online publication date: 1-Oct-2022
https://doi.org/10.1109/TVCG.2021.3064367
Park KKim MPark L(2022)NeuSaver: Neural Adaptive Power Consumption Optimization for Mobile Video StreamingIEEE Transactions on Mobile Computing10.1109/TMC.2022.3195961(1-14)Online publication date: 2022
https://doi.org/10.1109/TMC.2022.3195961
Li XShi ZChen JLiu YHardavellas NCampanoni SGrot BKarpuzcu U(2022)Realizing Emotional Interactions to Learn User Experience and Guide Energy Optimization for Mobile ArchitecturesProceedings of the 55th Annual IEEE/ACM International Symposium on Microarchitecture10.1109/MICRO56248.2022.00064(868-884)Online publication date: 1-Oct-2022
https://dl.acm.org/doi/10.1109/MICRO56248.2022.00064
Liao HTang GGuo DWu KWu Y(2022)EdgeSaver: Edge-Assisted Energy-Aware Mobile Video Streaming for User Retention EnhancementIEEE Internet of Things Journal10.1109/JIOT.2021.31116459:9(6550-6562)Online publication date: 1-May-2022
https://doi.org/10.1109/JIOT.2021.3111645
Li XHong SChen JYan GWu K(2022)Using Psychophysics to Guide Power Adaptation for Input Methods on Mobile Architectures2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA)10.1109/HPCA53966.2022.00045(514-527)Online publication date: Apr-2022
https://doi.org/10.1109/HPCA53966.2022.00045
Varvello MKatevas KPlesa MHaddadi HBustamante FLivshits B(2022)BatteryLab: A Collaborative Platform for Power MonitoringPassive and Active Measurement10.1007/978-3-030-98785-5_5(97-121)Online publication date: 22-Mar-2022
https://doi.org/10.1007/978-3-030-98785-5_5
Ahn WHong CHan KChoi SOh JLim S(2021)Scrolling-Aware Rendering to Reduce Frame Rates on SmartphonesElectronics10.3390/electronics1017217710:17(2177)Online publication date: 6-Sep-2021
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