1. Impact of Power-Management Granularity on The Energy-Quality Trade-off for Soft And Hard Real-Time Applications International Symposium on System-on-Chip, 2008 A. Milutinovic, K. Goossens, and G.J.M. Smit Advisor: Shiann-Rong Kuang Speaker: Hao-Yi Jheng ( 鄭浩逸 ) 2009.2.26 1

2. Outline  Introduction  Application model  Work and slack  Policy Conservativeness and Granularity  Experimental Results  Conclusions 2

3. Application model 3  In this paper they evaluate two power-management policies for a number of different granularities on an MPEG4 application, on energy and quality (deadline misses).  Granularity (N) : frequency of operating point changes  Hard real-time applications  Don’t allow any frame miss deadline  Use conservative power-management  Soft real-time applications  Allow a limited number of frame miss deadline  Use non-conservative power-management

4. Work and slack 4  Work : the number of processor cycles  Relative deadline :  Relative deadline miss means this frame over deadline  Relative slack (r) :  Absolute deadline :  Absolute deadline miss means that the accumulative execution time frame 0 to i is over the total deadline  Absolute slack(s) :

5. Outline 5  Introduction  Application model  Work and slack  Policy Conservativeness and Granularity  Experimental Results  Conclusions

6. Conservative Policy  Conservative power-management policy :  Does not introduce any deadline misses compared to operating at.  Non-conservative power-management policy :  Some frames maybe miss it’s deadline. 6

7. Policy 7  Perfect predictor policy (non-conservative) :  Accurately predicts the next N frames workload and scaled the average frequency for those frame   Proven slack policy (conservative) :  Proven slack : the cumulative slack of the frames before it  Assume that the next N frames all require the worst-case work, but use all the proven slack of previous group to reduce the frequency of the processor 

8. Outline 8  Introduction  Application model  Work and slack  Policy Conservativeness and Granularity  Experimental Results  Conclusions

9. Experimental Results (1/5)  An MPEG4 decoder running on an ARM946 at 86 MHz  25 frames per second (fps), and a resolution of 176*144 pixel 9

10. Experimental Results (2/5)  Energy savings w.r.t. operating at are around 30% for 1-128 frames  2% cost for the power management  Above 128 frames the proven-slack policy energy linearly raise 10

11. Experimental Results (3/5) 11  The proven-slack policy cannot always exploit the accumulated slack Average slack : Worst-case slack :

12. Experimental Results (4/5) 12  Perfect predictor policy :  95% quality improvement costs only 3% additional energy  Optimum is 13000 mJ

13. Experimental Results (5/5) 13  Many frames can be processed in the range of 240-250 MHz.

14. Outline 14  Introduction  Application model  Work and slack  Policy Conservativeness and Granularity  Experimental Results  Conclusions

15. Conclusions 15 1. A long tail in the work distribution results in a steep quality improvement : from almost 0% to almost 100% at an additional energy cost of only 3%. 2. The proven-slack policy offers 100% quality at only 0.3% more energy than the perfect-predictor policy, which is theoretical upper bound and hard to achieve in practice. 3. The energy of the policies increases by only 2% when increasing the granularity to 128 frames.

16. Conclusions  Non-conservation  Conservation  Tardiness  (sum of frame delay time / frame number)/deadline 16

17. Comparison 17

18. Progress report 18 Advisor: Shiann-Rong Kuang Speaker: Hao-Yi Jheng 2009.2.23

19. Outline  Adaptive Inter-compensation  How to choose voltage/frequency level  Adaptive  Experimental Result  Future Work 19

20. How to choose voltage/frequency level 20 5.83 3.57 1.16 1.52 1.30 0.08 0.97

21. Why need inter-compensation 21

22. Inter-compensation  PID  Adaptive inter-compensation  If (previous frame predictive cycle number is more  cycles)  current frame predictive voltage level decreases one  else  current frame predictive voltage doesn’t change  If( )   = 2000  else   = 27000 22

23. Inter-compensation 23

24. Experimental Result 24 Energy(e+08)No-inter200027000adaptive API_00 2.133891.896942.107781.98991 API_01 1.414211.182321.251121.23007 API_02 2.579392.204972.342322.29719 API_03 1.655721.41081.491391.45527 API_04 2.203791.881782.067921.99084 API_05 1.243531.046721.161251.11097 FRVNo-inter200027000adaptive API_0066.263632.000876.911639.8287 API_0135.96658.86423 0.5415340.281196 API_02 24.90816.538281.008311.28403 API_03 41.996812.20530.3416971.0757 API_04 18.35237.357523.915221.03591 API_05 25.467326.35451.56183.66423

25. Future Work  We need Hardware GM and RM cycle numbers to verify the experimental Result  Driver is needed to support the GM and RM dump cycle number for prediction 25