vada lab.sungkyunkwan univ. 1 dynamic voltage scaling

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SungKyunKwan Univ . 1 VADA Lab. Dynamic Voltage Scaling

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SungKyunKwan Univ.

1VADA Lab.

Dynamic Voltage Scaling

SungKyunKwan Univ.

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Energy Efficient Software for General Purpose Computing, Trevor Pering, UC-Berkeley

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Introduction: Research Spectrum

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Introduction: My Contributions

• Simulator: Instruction-level Energy Estimation

• Software: Energy Efficient Algorithms• OS: Voltage Scheduling Algorithms ***• OS: Multiprocessing for Energy• Microprocessor: Dynamic Caches

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Processor Systems:high Power

• Thinkpad (Pentium) 0.3 Hours/AA• InfoPad (ARM) 0.8 Hours/AA• Toshiba Portable (486) 0.9 Hours/AA• Newton (ARM) 2.0 Hours/AA• Device (Processor) Processor System Lif

etime

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Do We Just Optimize Power?

• Operations per Battery Life:• Minimize Energy Consumed per

Operation• Operations per Second:• Maximize Throughput

Operations/ second

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Ultra-low power Infopad Project

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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.

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OS: Voltage Scaling

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DVS

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Dynamic Voltage Scaling

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DVS Scheduling Frameworkµ

Pro

c. S

peed

Time

Start Deadline Start Deadline

Idle time represents

wasted energy

Lower speed,Lower voltage, Lower energy

Energy ~ Work • Speed

WorkWork

• Use real-time framework toconstrain task voltage scheduling

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DVS SimulationS

peed

Time

S1 S2 S3 D1 D3 D2 Task Variance

Weather

Interrupts

User Input

Cache Behavior

Scheduling Overhead

IntercomIntercom

RealityTheory ImplementationSimulate run-time scheduler to

fully understand voltage-scaling behavior

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Simulation InfrastructureGUI

Run-timeScheduler

VoltageScheduler

Applicationsupport libraries

MPEG Priority 80GUI Priority 23

MPEG Priority 80GUI Priority 23

Speed Priority

{ Frame_Start(deadline); Decode_MPEG_Frame(); Frame_Finish();}

{ Frame_Start(deadline); Decode_MPEG_Frame(); Frame_Finish();}Windowing

Cryptography

I/O Support

lpARM

MPEG

Develop support environment tomodel complete software system

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Results: Run-Time Voltage Scaling

73%

58%

25%16%

65%

46%

15%20%

0%

20%

40%

60%

80%

100%

Audio GUI MPEG Audio &MPEG

To

tal

Sy

ste

m E

ne

rgy

DVS SimulationPost-Trace Optimal

Normalized to 3.3V

fixed-voltage processor

Combination of independent

benchmarks

• Dynamic Voltage Scalingsignificantly reduces energy dissipation!

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Run-Time Performance AnalysisFrame Computation Histogram

0%

20%

40%

60%

80%

100%

Fixed-V Frame Execution Time

AudioGUIMPEG

DVS System Energy

0%

20%

40%

60%

80%

100%

To

tal S

ys

tem

En

erg

y

Basic AlgorithmAdjusted AlgorithmPost-Trace Optimal

Audio MPEG GUI

Software can automatically recognize and adjust for

bi-modal GUI distribution

0 2x deadline

Normalized to deadline at max processor speed

• Application characteristics strongly affectvoltage scaling performance

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Variable Supply Voltage Block Diagram

• 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.

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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.

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Simplest Approach: Compute ASAP

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Another Approach: Reduce Clock Frequency

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Voltage Scheduling II

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Evaluation: Algorithms

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OS: Voltage Scheduling

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Run-Time Scheduling Dynamicsµ

Pro

c. S

peed

Time

Thread accomplishing more than expected,

reduce speedDeadline exceeded,

increase speedHigher-priority

task

Run faster to make up lost time

Initial speed estimate

Optimal scheduleE(work)

Workload calculated to be average of previous frames

• Periodically re-evaluate schedule toadjust for unforeseen events

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Vertical Layering

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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.

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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

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• 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.

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• T. Burd, Low-Power CMOS Cell Library Design Methodology, M.S. Thesis, University of California, Berkeley, UCB/ERL M94/89, 1994.

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• A. Chandrakasan, S. Sheng, and R.W. Brodersen, "Low-Power CMOS Digital Design," IEEE Journal of Solid State Circuits, Apr. 1992, pp. 473-84.

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• 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.

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• 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.

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• 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.

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