Energy-Efficient Mapping and Scheduling for DVS Enabled Distributed Embedded Systems

transcript

Energy-Efficient Mapping and Scheduling for DVS Enabled

Distributed Embedded Systems

Marcus T. Schmitz and Bashir M. Al-HashimiUniversity of Southampton, United Kingdom

Petru ElesLinköping University, Sweden

2Marcus T. SchmitzUniversity of Southampton

Contents• Motivation & Introduction

• Dynamic Voltage Scaling

• Co-Synthesis with DVS Consideration

• DVS optimised Scheduling

• DVS optimised Mapping

• Experimental Results

• Conclusions

MotivationLow 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

Introduction

Dynamic Voltage Scaling

System Level Co-Synthesis

Energy-Efficient Co-Synthesis for

DVS Sytems

Dynamic Voltage Scaling (DVS)

f Reg.

DVS Processor

1 1.5 2 2.5 3 3.5 4 4.5 5

Energy vs. Speed

Voltage/Frequency

Frequency

Available from: Transmeta, AMD, Intel

1/Speed

2ddVkE

Co-Synthesis for DVS Systems

Allocation

Mapping

Scheduling

Voltage Scaling

Evaluation

System Specification, Technology Lib.

DVS in Distributed Systems [23]

@ Vmax @ dyn. V

Input:Scheduling (mapping)Power profile

Output:scaled voltage for each DVS task

Emax Esc < Emax

2.3V 2.4V3.3V

Voltage Scaling

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!

Energy-Efficient Scheduling

E=53.9J

E=65.6J

Slack Savings

Energy-Efficient Scheduling• Based on Genetic List Scheduling Algorithm [6,10]

• Task priorities are encoded into priorities strings

List Scheduler

4 3 9 7 2

Duties of the Scheduler:1. Select ready task with highest

priority2. Schedule selected task3. Update schedule and ready list4. Repeat until no un-scheduled

task is left

Schedule

EE-GLSA

List Scheduler DVS

Assign fitness

Rank individuals

Selection

Mutation

Mating

InsertionIniti

low high

Timing, Energy

No Hole Filling!No Mapping!

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

Hole Filling Problem

d2 d3,4

Hole filling

Therefore, priorities decide solely upon execution order!

Task Mapping

Why seperation from the list scheduling?• Regardless of priorties, greedy mapping

Task Mapping

Make greedy mapping decision based on:• Timing• Energy

Task Mapping

Make mapping decision based on:• Timing• Energy

Task Mapping

Genetic Mapping Algorithm [8]

CPU DVS-CPU

task PE

Chromosome

Task mapping are encoded into mapping strings

EE-GMA

EE-GLSA

Assign fitness

Rank individuals

Selection

Mutation

Mating

Insertion

low high

Timing, Energy + Area

Including DVS

Experimental Results• 4 Benchmark Sets:

• 27 generated by TGFF [7]

– 8 to 100 tasks: Power variations 2.6

• 2 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

Schedule Optimisation

Tgff1 Tgff2 Tgff3 Tgff4 Tgff5 Tgff6 Tgff7 Tgff8 Tgff9 Tgff10

Example

EVEN-DVS[18]

GLSA+EVEN

EE-GLSA

Schedule Optimisation

TG1 TG2

Benchmark set

LEneS [11]

EE-GLSA

Mapping Optimisation

Tgff1 Tgff2 Tgff3 Tgff4 Tgff5 Tgff6 Tgff7 Tgff8 Tgff9 Tgff10

Example

EVEN-DVS

EE-GMA

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

Energy-Efficient Mapping and Scheduling for DVS Enabled Distributed Embedded Systems

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