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L35: Lower Power Voltage Scaling

L35: Lower Power Voltage Scaling. 1999. 8. 성균관대학교 조 준 동 http://vada.skku.ac.kr. Voltage Scaling.

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L35: Lower Power Voltage Scaling

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  1. L35: Lower Power Voltage Scaling 1999. 8. 성균관대학교 조 준 동 http://vada.skku.ac.kr SungKyunKwan Univ.

  2. Voltage Scaling • Merely changing a processor clock frequency is not an effective technique for reducing energy consumption. Reducing the clock frequency will reduce the power consumed by a processor, however, it does not reduce the energy required to perform a given task. • Lowering the voltage along with the clock actually alters the energy-per-operation of the microprocessor, reducing the energy required to perform a fixed amount of work. SungKyunKwan Univ.

  3. Dynamic Voltage Scaling(DVS) SungKyunKwan Univ.

  4. Processor Usage Model SungKyunKwan Univ.

  5. OS: Voltage Scaling SungKyunKwan Univ.

  6. Scale Supply Voltage with fCLK SungKyunKwan Univ.

  7. Adaptive Power Supply Voltages SungKyunKwan Univ.

  8. Variable Supply Voltage Block Diagram SungKyunKwan Univ.

  9. Typical MPEG IDCT Histogram SungKyunKwan Univ.

  10. Voltage scheduling under timing constraints • Energy consumption of a processor: • 10nJ/cycle at 2.5V • 25nJ/cycle at 4 V • 40nJ/cycle at 5V • maximum clock frequencies: • 50MHz at 5V, 40MHz at 4V, 25MHz at 2.5V • Given that an application needs 1000M cycles to finish and the timing constaint is 25sec. SungKyunKwan Univ.

  11. 40J 5.02 1000Mcycles 50MHz (A) 0 5 10 15 20 25 Time(sec) Different Voltage Schedules Timing constraint 32.5J 5.02 750Mcycles 50MHz 250Mcycles 25MHz (B) Energy consumption (µ Vdd2) 2.52 0 5 10 15 20 25 Time(sec) 5.02 25J (C) 4.02 1000Mcycles 40MHz Time(sec) 0 5 10 15 20 25 SungKyunKwan Univ.

  12. Example of Variable Supply SungKyunKwan Univ.

  13. DVS Implementation SungKyunKwan Univ.

  14. Computational work varies with time. An approach to reduce the energy consumption of such systems beyond shut down involves the dynamic adjustment of supply voltage based on computational workload. The basic idea is to lower power supply when the a fixed supply for some fraction of time. The supply voltage and clock rate are increased during high workload period. Variable Supply Voltage Block Diagram SungKyunKwan Univ.

  15. Data Driven Signal Processing The basic idea of averaging two samples are buffered and their work loads are averaged. The averaged workload is then used as the effective workload to drive the power supply. Using a pingpong buffering scheme, data samples In +2, In +3 are being buffered while In, In +1 are being processed. SungKyunKwan Univ.

  16. Example of Buffering SungKyunKwan Univ.

  17. Graphical Interpretation SungKyunKwan Univ.

  18. Buffering Example: MPEG Decoder SungKyunKwan Univ.

  19. DVS SungKyunKwan Univ.

  20. DVS Scheduling Framework • Use real-time framework toconstrain task voltage scheduling Energy ~ Work • Speed Start Deadline Start Deadline Lower speed,Lower voltage, Lower energy Idle time represents wasted energy µProc. Speed Work Work Time SungKyunKwan Univ.

  21. D3 S1 S2 S3 D1 D2 Speed Intercom Time DVS Simulation Interrupts Cache Behavior Task Variance Scheduling Overhead User Input Weather Implementation Theory Reality Simulate run-time scheduler tofully understand voltage-scaling behavior SungKyunKwan Univ.

  22. Simulation Infrastructure GUI MPEG { Frame_Start(deadline); Decode_MPEG_Frame(); Frame_Finish(); } Cryptography Windowing VoltageScheduler I/O Support MPEG  Priority 80 GUI  Priority 23 Applicationsupport libraries lpARM Run-time Scheduler Speed Priority Develop support environment tomodel complete software system SungKyunKwan Univ.

  23. Run-Time Voltage Scaling Normalized to 3.3V fixed-voltage processor Combination of independent benchmarks • Dynamic Voltage Scalingsignificantly reduces energy dissipation! SungKyunKwan Univ.

  24. Run-Time Performance Analysis 0 2x deadline Audio MPEG GUI Software can automatically recognize and adjust forbi-modal GUI distribution Normalized to deadline at max processor speed • Application characteristics strongly affectvoltage scaling performance SungKyunKwan Univ.

  25. Compute ASAP+ System Shutdown SungKyunKwan Univ.

  26. Another Approach: Reduce Clock Frequency SungKyunKwan Univ.

  27. Voltage Scheduling II SungKyunKwan Univ.

  28. Evaluation: Algorithms SungKyunKwan Univ.

  29. AVG<weight> • Computes an exponentially moving average of the previous intervals. At each interval the run-percent from the previous interval is combined with the previous running average, forming a long-term prediction of system behavior. <weight> is the relative weighting of past intervals relative of the current interval (larger value means a great weight on the past) using the equation (weight X old + new)/(weight+1). 3 can be used. SungKyunKwan Univ.

  30. OS: Voltage Scheduling SungKyunKwan Univ.

  31. Run-Time Scheduling Dynamics Run faster to make up lost time Thread accomplishing more than expected,reduce speed Deadline exceeded,increase speed Higher-priority task µProc. Speed Optimal schedule E(work) Initial speed estimate Time Workload calculated to be average of previous frames • Periodically re-evaluate schedule toadjust for unforeseen events SungKyunKwan Univ.

  32. Vertical Layering SungKyunKwan Univ.

  33. Optimal Scheduling • For a region spanned by a given task specification, each point in time will either be scheduled at the minimum speed spanned by that task or else the task will not be scheduled to run at that point. Algorithm • n tasks to schedule • O(n) speed settings to consider for each task • O(n) linked tasks requiring adjustment for each setting: Total complexity: O(n 3 ) time. SungKyunKwan Univ.

  34. Scheduling step0 SungKyunKwan Univ.

  35. Scheduling step1 SungKyunKwan Univ.

  36. Scheduling step2 SungKyunKwan Univ.

  37. Scheduling step3 SungKyunKwan Univ.

  38. Scheduling step4 SungKyunKwan Univ.

  39. Scheduling step5 SungKyunKwan Univ.

  40. References • [Lin97] Lin et al., "Scheduling Techniques for Variable Voltage Low Power Designs," ACM Transactions on Design Automation of Electronic Systems, vol. 2, no. 2, pp. 81-97, 1997. • [Govil95] - Extended simulation with practical algorithms on traces of UNIX workstations • [Kuroda98] - Implementation of DVS processor to mitigate effects of process variation • [Ishihara98] - Dynamic voltage scaling with non- constant capacitances • S. Gary, et. al., "The PowerPC 603 Microprocessor: A Low-Power Design for Portable Applications," Proceedings of the Thirty-Ninth IEEE Computer Society International Conference, Mar. 1994, pp. 307-15. • A. Chandrakasan, Low Power Digital CMOS Design, Boston: Kluwer Academic Publishers, 1995. • C. Nagendra, et.al., "A Comparison of the Power-Delay Characteristics of CMOS Adders,” Proceedings of the International Workshop on Low Power Design, Apr. 1994, pp. 231-6. • T. Callaway and E. Swartzlander, "Optimizing Arithmetic Elements for Signal Processing," VLSI Signal Processing, Vol. 5, New York: IEEE Special Publications, 1992, pp. 91-100. • T. Biggs, et. al., "A 1 Watt 68040-Compatible Microprocessor," Proceedings of the IEEE Symposium on Low Power Electronics, Oct. 1994, pp. 8-11. • J. Lorch, A Complete Picture of the Energy Consumption of a Portable Computer, M.S. Thesis, University of California, Berkeley, 1995 SungKyunKwan Univ.

  41. References • S. Kunii, "Means of Realizing Long Battery Life in Portable PCs," Proceedings of the IEEE Symposium on Low Power Electronics, Oct. 1995, pp. 12-3. • M. Culbert, "Low Power Hardware for a High Performance PDA," Proceedings of the Thirty-Ninth IEEE Computer Society International Conference, Mar. 1994, pp. 144-7. • T. Ikeda, "ThinkPad Low-Power Evolution," Proceedings of the IEEE Symposium on Low Power Electronics, Oct. 1995, pp. 6-7. • A. Chandrakasan, A. Burstein, and R.W. Brodersen, "A Low Power Chipset for Portable Multimedia Applications," IEEE Journal of Solid State Circuits, Vol. 29, Dec. 1994, pp. 1415-28. • M. Horowitz, T. Indermaur, and R. Gonzalez, "Low-Power Digital Design," Proceedings of the IEEE Symposium on Low Power Electronics, Oct. 1994, pp. 8-11. • D. Lidsky and J. Rabaey, "Early Power Exploration - A World Wide Web Application," Proceedings of the Thirty-Third Design Automation Conference, June 1996. • T. Burd, Low-Power CMOS Cell Library Design Methodology, M.S. Thesis, University of California, Berkeley, UCB/ERL M94/89, 1994. SungKyunKwan Univ.

  42. A. Chandrakasan, S. Sheng, and R.W. Brodersen, "Low-Power CMOS Digital Design," IEEE Journal of Solid State Circuits, Apr. 1992, pp. 473-84. • Advanced RISC Machines, Ltd., ARM710 Data Sheet, Technical Document, Dec. 1994. • Integrated Device Technology, Inc., Enhanced Orion 64-Bit RISC Microprocessor, Data Sheet, Sep. 1995. • Intel Corp., Embedded Ultra-Low Power Intel486TM GX Processor, SmartDieTM Product Specification, Dec. 1995. • A. Stratakos, S. Sanders, and R.W. Brodersen, "A Low-voltage CMOS DC-DC Converter for Portable Battery-operated Systems," Proceedings of the Twenty-Fifth IEEE Power Electronics Specialist Conference, June 1994, pp. 619-26. • J. Bunda, et. al., "16-Bit vs. 32-Bit Instructions for Pipelined Architectures," Proceedings of the 20th International Symposium on Computer Architecture, May 1993, pp. 237-46. • Advanced RISC Machines, Ltd., Introduction to Thumb, Developer Technical Document, Mar. 1995. SungKyunKwan Univ.

  43. J. Bunda, W.C. Athas, and D. Fussell, "Evaluating Power Implications of CMOS Microprocessor Design Decisions," Proceedings of the International Workshop on Low Power Design, Apr. 1994, pp. 147-52. • P. Freet, "The SH Microprocessor: 16-Bit Fixed Length Instruction Set Provides Better Power and Die Size," Proceedings of the Thirty-Ninth IEEE Computer Society International Conference, Mar. 1994, pp. 486-8. • T. Burd, B. Peters, A Power Analysis of a Microprocessor: A Study of an Implementation of the MIPS R3000 Architecture, ERL Technical Report, University of California, Berkeley, 1994. • J. Montanaro, et. al., "A 160MHz 32b 0.5W CMOS RISC Microprocessor," Proceedings of the Thirty-Ninth IEEE International Solid-State Circuits Conference - Slide Supplement, Feb. 1996, pp. 170-1. • J. Bunda, Instruction-Processing Optimization Techniques for VLSI Microprocessors, Ph.D. Thesis, The University of Texas at Austin, 1993. • R. Gonzalez and M. Horowitz, "Energy Dissipation in General Purpose Processors," Proceedings of the IEEE Symposium on Low Power Electronics, Oct. 1995, pp. 12-3. SungKyunKwan Univ.

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