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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, United Kingdom
Petru Eles
Linköping University, Sweden
Contents
•
Motivation & Introduction
• Dynamic Voltage Scaling
• Co-Synthesis with DVS Consideration
•
DVS optimised Scheduling
•
DVS optimised Mapping
•
Experimental Results
•
Conclusions
Marcus T. Schmitz
University of Southampton
2
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
Marcus T. Schmitz
University of Southampton
3
Introduction
Dynamic
Voltage Scaling
System Level
Co-Synthesis
Energy-Efficient
Co-Synthesis for
DVS Sytems
Marcus T. Schmitz
University of Southampton
4
Dynamic Voltage Scaling (DVS)
Energy vs. Speed
1.2
DVS
Processor
f Reg.
Energy
VR
1
Frequency
0.8
0.6
Voltage/Frequency
0.4
E  k V
2
dd
0.2
0
1
1.5
2
2.5
3
3.5
4
4.5
5
1/Speed
Available from: Transmeta, AMD, Intel
Marcus T. Schmitz
University of Southampton
5
Co-Synthesis for DVS Systems
System Specification, Technology Lib.
Mapping
Scheduling
EE-GLSA
EE-GMA
Designer driven
Allocation
Voltage Scaling
Evaluation
Marcus T. Schmitz
University of Southampton
6
DVS in Distributed Systems [23]
Input:
Output:
Scheduling (mapping)
Power profile
scaled voltage for
each DVS task
P
Emax
P
Slack
PE0
CL0
Esc < Emax
PE0
CL0
2.3V 3.3V 2.4V
PE1
PE1
d t
@ Vmax
Marcus T. Schmitz
University of Southampton
Voltage Scaling
d
t
@ dyn. V
7
Energy-Efficient Scheduling
Two objectives:
• Timing feasibility
• Garantee deadlines
• Low energy dissipation
• Optimisation DVS usability – Slack time
Traditional scheduling technique focus mainly on
timing feasibility!
Problem due to power variations:
• Simply increase deadline slack leads to
sub-optimal solutions!
Marcus T. Schmitz
University of Southampton
8
Energy-Efficient Scheduling
S1:
E=71J
P
PE0
PE1
4
P
E=65.6J
5
5
4
Slack 
Savings 
DVS
0
1
PE2
2
0
1
2
Slack

3 
6
3
6
t
S2:
E=71J
P
PE0
PE1
t
Slack
4
P
E=53.9J
5
4
Slack 
Savings 
5
DVS
0
2
1
PE2
Marcus T. Schmitz
University of Southampton
0

3 
6
2
1

3 
6
t
t
9
Energy-Efficient Scheduling
•
•
Based on Genetic List Scheduling Algorithm [6,10]
Task priorities are encoded into priorities strings
0
PS
4
1
2
3
4
3
9
7
List
Scheduler
2
0 1 2 3 4
Marcus T. Schmitz
University of Southampton
Schedule
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
10
3
3
7
2
8
1
1
3
2
2
No Hole Filling!
No Mapping!
List Scheduler
Insertion
Timing,
Energy Assign fitness
Mutation
Mating
GA
Marcus T. Schmitz
University of Southampton
DVS
Rank individuals
Selection
low
high
Optimised Population
Initial Population
EE-GLSA
11
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
Marcus T. Schmitz
University of Southampton
12
Hole Filling Problem
Hole filling
0 7
3 1
PE0
4
2
3
d3
1 4
d2
d3,4
4 6
2 4
d2
d4
PE1
0 
1
Therefore, priorities decide solely upon execution order!
Marcus T. Schmitz
University of Southampton
13
Task Mapping
Why seperation from the list scheduling?
• Regardless of priorties, greedy mapping
P
0 7
1 4
d1
2 5
PE0
LS
PE1
d1,2 t
d2
Marcus T. Schmitz
University of Southampton
14
Task Mapping
Make greedy mapping decision based on:
• Timing
• Energy
P
0 7
1 4
d1
2 5
PE0 ?
LS
PE1
?
d1,2 t
d2
Marcus T. Schmitz
University of Southampton
15
Task Mapping
Make mapping decision based on:
• Timing
• Energy
P
0 7
1 4
d1
2 5
PE0
0
LS
PE1
d1,2 t
d2
Marcus T. Schmitz
University of Southampton
16
Task Mapping
Make mapping decision based on:
• Timing
• Energy
P
0 7
1 4
d1
2 5
PE0
0
?
LS
PE1
?
d1,2 t
d2
Marcus T. Schmitz
University of Southampton
17
Task Mapping
Make mapping decision based on:
• Timing
• Energy
P
0 7
1 4
d1
2 5
PE0
0
LS
PE1
2
d1,2 t
d2
Marcus T. Schmitz
University of Southampton
18
Task Mapping
Make mapping decision based on:
• Timing
• Energy
P
0 7
1 4
d1
2 5
PE0
0
LS
PE1
2
d1,2 t
d2
Marcus T. Schmitz
University of Southampton
19
Task Mapping
Make mapping decision based on:
• Timing
• Energy
P
0 7
1 4
d1
2 5
PE0
0
LS
PE1
2
1
d1,2 t
d2
Marcus T. Schmitz
University of Southampton
20
Task Mapping
Make mapping decision based on:
• Timing
• Energy
P
0 7
1 4
d1
2 5
PE0
0
2
LS
PE1
1
d1,2 t
d2
Marcus T. Schmitz
University of Southampton
21
Genetic Mapping Algorithm [8]
Task mapping are encoded into mapping strings
0
1
3
2
4
6
5
d
d
task
PE
0
1
1
0
2
2
3
1
4
1
5
0
6
0
CPU
DVS-CPU
1
0
2
ASIC
Chromosome
Marcus T. Schmitz
University of Southampton
22
Including DVS
EE-GLSA
Insertion
Timing,
Energy + Assign fitness
Area
Mutation
Mating
GA
Marcus T. Schmitz
University of Southampton
Rank individuals
Selection
low
high
Optimised Population
Initial Population
EE-GMA
23
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
Marcus T. Schmitz
University of Southampton
24
Schedule Optimisation
80
EVEN-DVS[18]
GLSA+EVEN
EE-GLSA
Reduction (%)
70
60
50
40
30
20
10
0
Tgff1
Tgff2
Tgff3
Tgff4
Tgff5
Tgff6
Tgff7
Tgff8
Tgff9
Tgff10
Example
Marcus T. Schmitz
University of Southampton
25
Schedule Optimisation
40
LEneS [11]
EE-GLSA
Reduction (%)
35
30
25
20
15
10
5
0
TG1
TG2
Benchmark set
Marcus T. Schmitz
University of Southampton
26
Mapping Optimisation
90
EVEN-DVS
EE-GMA
80
Reduction (%)
70
60
50
40
30
20
10
0
Tgff1
Tgff2
Tgff3
Tgff4
Tgff5
Tgff6
Tgff7
Tgff8
Tgff9
Tgff10
Example
Marcus T. Schmitz
University of Southampton
27
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
Marcus T. Schmitz
University of Southampton
28