2011 ISLPED: Backlight scaling service

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2011 ISLPED: Backlight scaling service

  1. 1. Dynamic Backlight Scaling Optimization  for Mobile Streaming Applications Pi‐Cheng Hsui, Chun‐Han Lin, and Cheng‐Kang HsiehResearch Center for Information Technology Innovation  Academia Sinica, Taiwan International Symposium on Low Power Electronics and Design
  2. 2. Outline• Introduction• Dynamic Backlight Scaling Optimization – An optimal algorithm – Demonstration• Performance Evaluation – System deployment – Experimental results• Conclusion 2
  3. 3. Outline• Introduction• Dynamic Backlight Scaling Optimization – An optimal algorithm – Demonstration• Performance Evaluation – System deployment – Experimental results• Conclusion 3
  4. 4. Research Motivation• Mobile applications and services are having a profound effect on peoples lifestyles• The energy consumption of mobile devices is a major challenge in sustaining the applications and services. 4
  5. 5. Possible Solutions• Battery extenders • Cloud‐based Energy‐Saving  Services 5
  6. 6. What is the use? • With the service is applied, the service provider help reduce  the energy consumption of mobile devices when they access  Internet applications. What application to start? What hardware to target? CPU,  WIFI,  16.87% LCD Screen,  24.62% 24.38% Backlight,  34.12%Cisco Forecast:Video streams will account for 66% of  Power distribution on HTC Desire mobile data traffic by 2014 when browsing videos on YouTube 6
  7. 7. Outline• Introduction• Dynamic Backlight Scaling Optimization – An optimal algorithm – Demonstration• Performance Evaluation – System deployment – Experimental results• Conclusion 7
  8. 8. Dynamic Backlight Scaling Optimization for  Mobile Streaming Applications• Dynamic backlight scaling – Dynamically adjust backlight  levels for video frames• A technical problem – Determine appropriate levels  for a video subject to scaling  constraints • Video distortion • Hardware/software limitation • User perception • Etc.• Contributions – Problem formulation – An optimal algorithm – System implementation 8
  9. 9. A Dynamic‐Programming Algorithm Image frames in video Video distortion Hardware/software limitation Backlight assignment 9
  10. 10. Demonstration – HTC DesireApproach validation Performance evaluation 10
  11. 11. Outline• Introduction• Dynamic Backlight Scaling Optimization – An optimal algorithm – Demonstration• Performance Evaluation – System deployment – Experimental results• Conclusion 11
  12. 12. System DeploymentSystem architectureCase studies 12
  13. 13. Experimental Results• Impact of video distortion on  • Impact of duration on energy  energy savings savings • 18‐31% when the distortion  • 18‐31% when the duration  threshold is set at 0.9 threshold is set at 10 13
  14. 14. Outline• Introduction• Dynamic Backlight Scaling Optimization – An optimal algorithm – Demonstration• Performance Evaluation – System deployment – Experimental results• Conclusion 14
  15. 15. Conclusion• We raise the concept of cloud‐based energy‐saving services  and have developed the dynamic backlight scaling service as  an example service. – With the service is applied, an HTC Desire mobile phone can save 18‐ 31% backlight energy when browsing videos on YouTube.• We will release the mobile application program in the Android  Market and seek feedback to identify more issues in this  research direction. 15
  16. 16. We are the Research Center for IT Innovation 16

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