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Energy-Efficient Mapping and Scheduling for DVS Enabled Distributed Embedded Systems Marcus T. Schmitz and Bashir M. Al-Hashimi University of Southampton,

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Presentation on theme: "Energy-Efficient Mapping and Scheduling for DVS Enabled Distributed Embedded Systems Marcus T. Schmitz and Bashir M. Al-Hashimi University of Southampton,"— Presentation transcript:

1 Energy-Efficient Mapping and Scheduling for DVS Enabled Distributed Embedded Systems Marcus T. Schmitz and Bashir M. Al-Hashimi University of Southampton, United Kingdom Petru Eles Linköping University, Sweden

2 2 Marcus T. Schmitz University of Southampton Contents Motivation & Introduction Dynamic Voltage Scaling Co-Synthesis with DVS Consideration DVS optimised Scheduling DVS optimised Mapping Experimental Results Conclusions

3 3 Marcus T. Schmitz University of Southampton Motivation Low Energy: Portable Applications Autonomous Systems Feasibilty Issues (SoC - heat) Operational Cost and Environmental Reasons System Level Co-Design: Shrinking Time-To-Market Windows Reducing Production Cost High Degree of Optimisation Freedom

4 4 Marcus T. Schmitz University of Southampton Introduction Dynamic Voltage Scaling System Level Co-Synthesis Energy-Efficient Co-Synthesis for DVS Sytems

5 5 Marcus T. Schmitz University of Southampton Dynamic Voltage Scaling (DVS) f Reg. DVS Processor Energy vs. Speed Voltage/Frequency Frequency VR Available from: Transmeta, AMD, Intel 1/Speed Energy

6 6 Marcus T. Schmitz University of Southampton Co-Synthesis for DVS Systems Allocation Mapping Scheduling Voltage Scaling Evaluation EE-GLSA EE-GMA Designer driven System Specification, Technology Lib.

7 7 Marcus T. Schmitz University of Southampton DVS in Distributed Systems [23] PE0 PE1 CL0 P t d PE0 PE1 CL0 P t V dyn. V Input: Scheduling (mapping) Power profile Output: scaled voltage for each DVS task E max E sc < E max Slack 2.3V2.4V3.3V Voltage Scaling

8 8 Marcus T. Schmitz University of Southampton Energy-Efficient Scheduling Two objectives: Timing feasibility Garantee deadlines Low energy dissipation Optimisation DVS usability – Slack time Problem due to power variations: Simply increase deadline slack leads to sub-optimal solutions! Traditional scheduling technique focus mainly on timing feasibility!

9 9 Marcus T. Schmitz University of Southampton Energy-Efficient Scheduling E=71 J E=71 J E=53.9 J E=65.6 J Slack Savings Slack Savings S1: S2: DVS Slack PE0 PE1 PE2 PE0 PE1 PE2 P tt t t P P P

10 10 Marcus T. Schmitz University of Southampton Energy-Efficient Scheduling Based on Genetic List Scheduling Algorithm [6,10] Task priorities are encoded into priorities strings List Scheduler PS Duties of the Scheduler: 1.Select ready task with highest priority 2.Schedule selected task 3.Update schedule and ready list 4.Repeat until no un-scheduled task is left Schedule

11 11 Marcus T. Schmitz University of Southampton EE-GLSA List SchedulerDVSAssign fitnessRank individualsSelection Mutation Mating Insertion Initial Population Optimised Population GA low high Timing, Energy No Hole Filling! No Mapping!

12 12 Marcus T. Schmitz University of Southampton Advantages Optimisation can be based on an arbitrary complex fitness function, including: Timing Energy (DVS technique) Enlarged search space (|T+C|! different schedules) Trade-off freedom: Synthesis time quality Easily adaptable to computing clusters Multiple populations with immigration scheme

13 13 Marcus T. Schmitz University of Southampton Hole Filling Problem d2d2 d4d4 d3d d2d2 d 3,4 Hole filling Therefore, priorities decide solely upon execution order! PE0 PE1

14 14 Marcus T. Schmitz University of Southampton Task Mapping Why seperation from the list scheduling? Regardless of priorties, greedy mapping LS d2d d1d1 d 1,2 PE0 PE1 P t

15 15 Marcus T. Schmitz University of Southampton Task Mapping Make greedy mapping decision based on: Timing Energy LS d2d d1d1 d 1,2 ? ? PE0 PE1 P t

16 16 Marcus T. Schmitz University of Southampton Task Mapping Make mapping decision based on: Timing Energy LS d2d d1d1 d 1,2 PE0 PE1 P t

17 17 Marcus T. Schmitz University of Southampton Task Mapping Make mapping decision based on: Timing Energy LS d2d d1d1 d 1,2 ? ? PE0 PE1 P t

18 18 Marcus T. Schmitz University of Southampton Task Mapping Make mapping decision based on: Timing Energy LS d2d d1d1 d 1,2 PE0 PE1 P t

19 19 Marcus T. Schmitz University of Southampton Task Mapping Make mapping decision based on: Timing Energy LS d2d d1d1 d 1,2 PE0 PE1 P t

20 20 Marcus T. Schmitz University of Southampton Task Mapping Make mapping decision based on: Timing Energy LS d2d d1d1 d 1,2 PE0 PE1 P t

21 21 Marcus T. Schmitz University of Southampton Task Mapping Make mapping decision based on: Timing Energy LS d2d d1d1 d 1,2 PE0 PE1 P t

22 22 Marcus T. Schmitz University of Southampton Genetic Mapping Algorithm [8] CPUDVS-CPU ASIC d d taskPE Chromosome Task mapping are encoded into mapping strings

23 23 Marcus T. Schmitz University of Southampton EE-GMA EE-GLSA Assign fitnessRank individualsSelection Mutation Mating Insertion Initial Population Optimised Population GA low high Timing, Energy + Area Including DVS

24 24 Marcus T. Schmitz University of Southampton Experimental Results 4 Benchmark Sets: 27 generated by TGFF [7] – 8 to 100 tasks: Power variations Hou examples taken from [13] – 8 to 20 tasks: Power variations 11 TG1 and TG2 taken from [11] – 60 examples with 30 tasks, each: No power variations Measurement application taken from [3] – 12 tasks: No power profile is provided Power and time overhead for DVS is neglected Average results of 5 optimisation runs

25 25 Marcus T. Schmitz University of Southampton Schedule Optimisation

26 26 Marcus T. Schmitz University of Southampton Schedule Optimisation

27 27 Marcus T. Schmitz University of Southampton Mapping Optimisation

28 28 Marcus T. Schmitz University of Southampton Conclusions DVS capability can achieve high energy savings in distributed embedded systems Proposed a new energy-efficient two-step mapping and scheduling approach Iterative improvement provides high savings / ad hoc constructive techniques are not suitable Optimisation times are reasonable Additional objectives can be easily included Consideration of power profile information leads to further energy reductions


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