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

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%.

[1]  C. V. Jawahar,et al.  The truth about cats and dogs , 2011, 2011 International Conference on Computer Vision.

[2]  Mun Choon Chan,et al.  Adaptive display power management for mobile games , 2011, MobiSys '11.

[3]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[4]  Alec Wolman,et al.  dJay: enabling high-density multi-tenancy for cloud gaming servers with dynamic cost-benefit GPU load balancing , 2015, SoCC.

[5]  Nam Sung Kim,et al.  Improving Throughput of Power-Constrained GPUs Using Dynamic Voltage/Frequency and Core Scaling , 2011, 2011 International Conference on Parallel Architectures and Compilation Techniques.

[6]  Sugato Chakravarty,et al.  Methodology for the subjective assessment of the quality of television pictures , 1995 .

[7]  Yu Yan,et al.  Optimizing power consumption of mobile games , 2015, HotPower '15.

[8]  M. Barris.  Vision and Art: The Biology of Seeing , 2005 .

[9]  Alec Wolman,et al.  Demo: Kahawai: high-quality mobile gaming using GPU offload , 2015, MobiSys.

[10]  E. Marg.  A VISION OF THE BRAIN , 1994 .

[11]  Nam Sung Kim,et al.  GPUWattch: enabling energy optimizations in GPGPUs , 2013, ISCA.

[12]  Yiran Chen,et al.  FingerShadow: An OLED Power Optimization Based on Smartphone Touch Interactions , 2014, HotPower.

[13]  Jingwen Leng,et al.  Exploiting Webpage Characteristics for Energy-Efficient Mobile Web Browsing , 2014, IEEE Computer Architecture Letters.

[14]  Scott A. Mahlke,et al.  APOGEE: Adaptive prefetching on GPUs for energy efficiency , 2013, Proceedings of the 22nd International Conference on Parallel Architectures and Compilation Techniques.

[15]  Lin Zhong,et al.  Chameleon: A Color-Adaptive Web Browser for Mobile OLED Displays , 2012, IEEE Transactions on Mobile Computing.

[16]  Hojung Cha,et al.  Content-centric display energy management for mobile devices , 2014, 2014 51st ACM/EDAC/IEEE Design Automation Conference (DAC).

[17]  Jaeha Kim,et al.  Memory-centric system interconnect design with Hybrid Memory Cubes , 2013, Proceedings of the 22nd International Conference on Parallel Architectures and Compilation Techniques.

[18]  Samarjit Chakraborty,et al.  Forget the battery, let's play games! , 2014, 2014 IEEE 12th Symposium on Embedded Systems for Real-time Multimedia (ESTIMedia).

[19]  Hojung Cha,et al.  Content-Centric Energy Management of Mobile Displays , 2016, IEEE Transactions on Mobile Computing.

[20]  Timo Koskela,et al.  Power Consumption Model of a Mobile GPU Based on Rendering Complexity , 2013, 2013 Seventh International Conference on Next Generation Mobile Apps, Services and Technologies.