mode selection and mode dependency modeling for power-aware embedded systems dexin li, pai h. chou,...
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![Page 1: Mode Selection and Mode Dependency Modeling for Power-Aware Embedded Systems Dexin Li, Pai H. Chou, Nader Bagherzadeh University of California, Irvine](https://reader030.vdocuments.site/reader030/viewer/2022032800/56649d385503460f94a1190b/html5/thumbnails/1.jpg)
Mode Selection andMode Dependency Modeling for
Power-Aware Embedded Systems
Mode Selection andMode Dependency Modeling for
Power-Aware Embedded Systems
Dexin Li, Pai H. Chou, Nader Bagherzadeh
University of California, Irvine5th International Conference on VLSI Design and
7th Asia and South Pacific Design Automation Conference
Bangalore, India, January 11, 2002
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OutlineOutline
Introduction
Related work
Mode dependency modeling
Mode selection
Experimental results
Conclusions
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IntroductionIntroduction
Power-aware systems Low power High performance as required
Use components with multiple power modes Power modes: full-on, doze, nap, sleep, deep sleep,
shutdown, … voltage and frequency settings Peripheral devices with diff. bus transfer rate, encodings
Mode selection Choose combinations of power modes over time Satisfy power performance constraints
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Mode SelectionMode Selection
Choosing mode combinations Many power modes are available Few modes are considered
Current limitations OS/API limitations Difficult to track the details
Select modes in isolation e.g., processor only Lack of system-level view
Satisfying constraints Power Timing
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Related WorkRelated Work
Dynamic Power Management (DPM) Low power, low-latency as a design goal
(rather than a constraint) [Benini99], [Hwang97], [Qiu00], … Lack of real time guarantees Not considering inter-component dependency
Dynamic voltage scaling (DVS) Slow down processor greedily [Yao95], [Hong98], [Shin99], [Quan01] Limited to single processor Can have counter-intuitive effects at system-level
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Example: tasks on multiple componentsExample: tasks on
multiple components Component R1 is voltage scalable
R2, R3 are NOT voltage scalable
R3 co-active with R1 (R1 on => R3 on)
original schedule
original power profile
R1
R2R3
Pmax
AB
C
C
A B
Tim
e vi
ewP
ower
vie
w
deadline
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Example -- applying DVSExample -- applying DVS
Greedily slow down R1, stretch A all the way to deadline
Co-activation => stretch C by same amount as A
Problems: Max violation (overlap B), plus higher energy!
DVSschedule
DVSpower profile
R1
R2R3
Pmax
C'
A'
B
B
Tim
e vi
ewP
ower
vie
w
deadline
C'A'
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Example: optimal solutionExample: optimal solution
Slow down R1 partially to eliminate power spike
Reduces co-active energy due to task C
No overlap with B => eliminates max power violation
Feasibletime schedule
Optimalpower profile
R1
R2R3
Pmax
A''
A''
B
B
C''
C''
Tim
e vi
ewP
ower
vie
w
deadline
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Our approachOur approach
Constraint-driven Power, performance as hard constraints
Mode change overhead Timing, power Will not change mode if overhead cannot be amortized
System-level modeling Co-activation Selection dependency across components
Exponential search space Pruning required
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OutlineOutline
Introduction
Related work
Mode dependency modeling Mode dependency graph (MDG) Legal configurations
Mode selection
Experimental results
Conclusions
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System modelingSystem modeling
Resource R M = {m1, m2, … } mutually exclusive modes
notation: R.m1, R.m2, … H = subset of M x M set of overhead functions
(power, time) on changing mode
Component = container of one or more resources
System Set of resources { R1, R2, R3, …} Set of dependencies between modes Configuration = member of MR1 x MR2 x MR3 x … Workload Constraints
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Mode dependency across resourcesMode dependency across resources
Necessary condition: m1 is necessary for m2 !m2 is sufficient for !m1
Operators AND, OR, XOR
Mode dependency graph Vertices: set of modes
set of operators Edges: direction of dependency
Purpose: generate set of “legal” configurations Assignments that satisfy all mode dependencies Independent of workload and timing/power constraints
m1 m2
m1
AND OR
m2
R1 R2
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Example: Sensor Node (MIT)Example: Sensor Node (MIT)
Components:Modes processor A active, idle, sleep memory M on, off radio R rx_tx, rx, tx, off sensor S on, off
# of possible configurations 3 x 2 x 4 x 2 = 48
(grows multiplicatively)
# of legal configurations 5 as given by MIT group
(closer to linearly) Manually generated Want to automate this
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Mode dependencies in sensor nodeMode dependencies in sensor node
Betw. CPU and {sensor, radio} CPU may sleep if
both radio and sensor are off
CPU may sleep or idle if either sensor or radio is “working”
Betw. Memory and CPU CPU co-activation with memory
(asymmetric)
Betw. sensor and radio Radio ON only if sensor ON
S.off
R.offAND A.sleep
S.on
R.rx_txR.rx
XORA.sleepA.idle
A.active M.on
A.idleA.sleep
M.off
R.on S.on
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Generating legal configurationsGenerating legal configurations
Idea: one “counter” per resource Binary encoding for modes in a resource
Interresource dependency graph Dependency between counters An edge (R1.m1, R2.m2) in MDG
=> add edge (R1, R2)
Linearize the counters Topological sort break cycle if necessary
Run the counters Count next digit only if there
is an edge in original MDG
radiosensor CPU mem
memCPUradio
sensor
off off sleep off
on off sleep off
idle off
on idle off
active on
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OutlineOutline
Introduction
Related work
Mode dependency modeling
Mode selection Legal vs. feasible configurations Mode schedule
Experimental results
Conclusions
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Mode Selection: inputMode Selection: input
Mode dependency graph
A schedule of workload Independent of power/energy, timing
Component library Maps workload into power, timing Maps mode change into power/timing overhead
Constraints Timing: min/max separation Power: min/max power levels
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Output: feasible mode scheduleOutput: feasible mode schedule
Feasibility All configurations are “legal” (satisfy MDG) All timing & power constraints are satisfied
Mode schedule Original workload mapped to configuration
(selected modes) over time Overhead tasks inserted between changed modes Idle power also considered
Solutions Dynamic programming Backtracking
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Example – Mars Pathfinder RoverExample – Mars Pathfinder Rover
Component-based system Power sources: solar panel, non-rechargeable batteries Power consumers
Digital: computer, wireless, camera, hazard detector Mechanical: driving motors, steering motors Thermal: heaters
Design constraints: Power consumption
Max/min power constraints Timing
Hard real-time Pair-wise precedence
Mode dependency Co-activation Enabling/disabling
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Application Example: Mars RoverApplication Example: Mars Rover
Behaviors and tasks Moving around on Mars surface Communicating with the Lander Taking pictures (IMP) Performing scientific experiments (APXS, ASI/MET)
Components in the entire system Hazard detector (HAZ), Driving motor (DRV) Steer motor (STR) Radio frequency modem (RF) Camera (CAM) Microprocessor (PPC), Micro-controller (MC)
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Example: Mars RoverExample: Mars Rover
Components: hazard detector, driving
motor, steering motor
Constraints: hazard detector and
motors should not be working at the same time
Legal configurations
str.on
drv.on
OR str.off
str: steering motor
drv: driving motor
haz: hazard detector
hazard detector driving motor steering motoroff off offoff on offoff off onoff on onon off off
m0m1m2m3m4
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ResultsResults
Base line Only: on/off modes Relaxed constraints Mode change overhead No max power constraint
Mode selection Energy saving:
3.7% to 11.9%8.7% average
Meets max power More general timing 1300
1350
1400
1450
1500
1550
1600
1650
1700
1750
5 6 7 8 9 10
Mode SelectionOn&Off
Pmin(W)
Energy(J)
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Results: Min powerResults: Min power
Free energy source e.g., solar panel Thermal battery
Cost: above min power
Saving: From 6.9% to 49.3% average 26.5%
0
100
200
300
400
500
600
700
800
900
1000
5 6 7 8 9 10
Mode Selection
On&Off
Pmin (W)
Energy (J)
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Exploring Different Working ScenariosExploring Different Working Scenarios
Explore different working scenarios and compare results Three mission tasks
Moving around (MOV) Taking picture (CAM) Scientific experiment
(SCI) Three scenarios
A: MOV, CAM, SCI B: CAM, MOV, SCI C: CAM, SCI, MOV
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Result IIIResult III
Different working scenarios have different energy consumption Scenario C consumes
12% more energy than scenario A (by mode selection)
Mode selection always does better than simply on/off modes
0
5000
10000
15000
20000
25000
A B C
Mode SelectionOn&off
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ConclusionsConclusions
Mode selection problem Choosing modes of component Minimize energy at system level, satisfy timing constraints Handles mode-change overhead
Mode dependency modeling Enabling relationship between modes of diff resources Helps prune out illegal configurations
Mode selection algorithm Iterate over legal config’s to find feasible mode schedule Satisfies power & timing constraints Improve existing solution by 8-25% in energy cost Give designers confidence in power-aware systems
Tool available from http://www.ece.uci.edu/~impacct/
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Mode Selection Algorithm (sketch)Mode Selection Algorithm (sketch)
Exploration techniques Backtracking Cutting exploration space with multi-dimensional
constraints
Two steps in design exploration: Find feasible mode selection for operating tasks
Timing constraints Dependency between tasks
Find feasible mode selections for idle intervals System power/energy constraints: min, max, or power profile Mode change overhead, both time and power overheads
Speedup techniques Sorting component modes with power numbers
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Legal configurationsLegal configurations
Sensor Node example Mode combinations given
by MIT group: 5 combinations
Mode combination generated from dependency graph
Add constraint When sensor is off, all
other component should be off (proactive)
Automatically obtain same results as MIT group
conf S R A Ms0 on tx_rx on ons1 on rx idle offs2 on rx sleep offs3 on off sleep offs4 off tx_rx on ons5 off rx idle offs6 off rx sleep offs7 off off sleep off
Not given by MIT group
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BibliographyBibliography [Benini99] L. Benini, G. Paleologo, A. Bogliolo, and G. De Micheli. Policy
optimization for dynamic power management. IEEE Trans. Computer-Aided Design, 18:813–833, June 1999.
[Hwang97] C.-H. Hwang and A. Wu. A predictive system shutdown method for energy saving of event-driven computation. In Proc. of DAC, November 1997.
[Qiu00] Q. Qiu, Q.Wu, and M. Pedram. Dynamic power management of complex systems using Generalized Stochastic Petri Nets. In Proc. of DAC, pages 352–356, 2000.
[Hong98] I. Hong, D. Kirovski, G. Qi, M. Potkonjak, and M. B. Srivastava. Power optimization of variable voltage core-based systems. In Proc. of DAC, pages 176–181, 1998.
[Quan01] G. Quan and X. S. Hu. Energy efficient fixed-priority scheduling for real-time systems on variable voltage processors. In Proc. of DAC, pages 828–833, 2001.
[Shin99], Y. Shin and K. Choi. Power conscious fixed priority scheduling for hard real-time systems. In Proc. of DAC, pages 134–139, 1999.
[Yao95] F. Yao, A. Demers, and S. Shenker. A scheduling model for reduced CPU energy. IEEE Annual Foundation of Computer Science, pages 374–382, 1995.
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AcknowledgementsAcknowledgements
DARPA PACC F33615-00-1-1719
NASA/Cal-Tech/JPL: N. Aranki M. Mojarradi J. U. Patel N. Toomarian
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Tool PrototypeTool Prototype
Language Python, Tkinter
Graphical User Interface Time window and power window View tasks, overheads, power profile Interactive command line Incorporate with the scheduler (to be completed) On-line help (to be completed)
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Scenario AScenario A
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Scenario BScenario B
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Scenario CScenario C
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System Power RepresentationSystem Power Representation
Schedule Gantt Chart
Time view Power view
Mode selection Gantt chart
Tasks marked with mode settings
Added non-operating tasks
Idle intervals mode change
overheads Power profile view
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Mode Selection: Problem StatementMode Selection: Problem Statement
Input A schedule MDG Constraints (power, timing)
Output a mode schedule
Problem statement Find feasible mode selection
for the tasks/idle intervals for the components that meets system power and timing constraints
Mode selector
scheduleComponent library
System constraints
Mode selections
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Comparison of three scenariosComparison of three scenarios
Original DVS Optimal
No No NoTiming Violations
No Yes NoPower Violations
300 320 288Energy Cost
Original Does not apply dynamic voltage scaling technique
DVS Ignores mode dependency between R1 and R3
Optimal solution Must consider mode dependency Energy saving due to dynamic voltage scaling