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 )
Outline Introduction Application model Work and slack Policy Conservativeness and Granularity Experimental Results Conclusions 2
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
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) :
Outline 5 Introduction Application model Work and slack Policy Conservativeness and Granularity Experimental Results Conclusions
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
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
Outline 8 Introduction Application model Work and slack Policy Conservativeness and Granularity Experimental Results Conclusions
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
Experimental Results (2/5) Energy savings w.r.t. operating at are around 30% for frames 2% cost for the power management Above 128 frames the proven-slack policy energy linearly raise 10
Experimental Results (3/5) 11 The proven-slack policy cannot always exploit the accumulated slack Average slack : Worst-case slack :
Experimental Results (4/5) 12 Perfect predictor policy : 95% quality improvement costs only 3% additional energy Optimum is mJ
Experimental Results (5/5) 13 Many frames can be processed in the range of MHz.
Outline 14 Introduction Application model Work and slack Policy Conservativeness and Granularity Experimental Results Conclusions
Conclusions 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.
Conclusions Non-conservation Conservation Tardiness (sum of frame delay time / frame number)/deadline 16
Comparison 17
Progress report 18 Advisor: Shiann-Rong Kuang Speaker: Hao-Yi Jheng
Outline Adaptive Inter-compensation How to choose voltage/frequency level Adaptive Experimental Result Future Work 19
How to choose voltage/frequency level
Why need inter-compensation 21
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 =
Inter-compensation 23
Experimental Result 24 Energy(e+08)No-inter adaptive API_ API_ API_ API_ API_ API_ FRVNo-inter adaptive API_ API_ API_ API_ API_ API_
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