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Model-based Fuzzy Logic Classifier Synthesis for
Optimization of Data-Adaptable Embedded Systems
Adrian Lizarraga, Roman Lysecky, Jonathan SprinkleElectrical and Computer Engineering
University of Arizona, Tucson, [email protected]
This work was supported by the AFOSR DDDAS program under grant #FA9550-15-1-0143.
R. Lysecky, August 11, 2016 InfoSymbiotics/DDDAS 2016, Hartford, CT
Data-adaptable System Model (DASM)
• Objective: • Develop a data-adaptable modeling framework for DDDAS applications to
enable an efficient runtime framework that continually adapts, re-composes, and re-optimizes the system implementation
• Approach:• Model-based design capturing functional and non-functional application
requirements, as well as application structure• Understands how a system can be optimally composed as the availability of
sensing, computing, communication, and applications requirements change• Quantify the optimality of system compositions under various dynamic
execution scenarios
VideoCapture
Background Subtraction
Downsampling Morphological Filter
Feature Detection&Tracking
Inverse Perspective
Mapping
Position Estimation
Min. Braking Distance Calc.
Downsampling
Velocity Estimation
Acceleration Estimation
Physical Application
Attribute Constraint Calculator
Optimization Evaluator
Runtime DSE and Optimizer
VehicleControl
StateOptimize? Constraints
System Configuration
2
R. Lysecky, August 11, 2016 InfoSymbiotics/DDDAS 2016, Hartford, CT
A B D
C Latency: 100msAccuracy: 75%
Increased data size leadsto congestion in Task B
Video
GPS
A B’ D
C Latency: 35msAccuracy: 70%
Adapt implementation of TaskB to improve latency
GPS
Video
A B D
C Latency: 30msAccuracy: 75%
µP
µP
µP
µP
Video
GPS
Constraint: Latency < 38ms
Change in Data
• Task implementations should automatically adapt to changes in data quality, data availability, application requirements, and availability of computing resources
• Complexities of optimization necessitate new modeling approaches and supporting tools to facilitate design and enable runtime task adaptability
3
Data-adaptable System Modeling and Runtime Adaptation
R. Lysecky, August 11, 2016 InfoSymbiotics/DDDAS 2016, Hartford, CT
µP
µP
µP
µP
A B D
C Latency: 100msAccuracy: 75%
Increased data size leadsto congestion in Task B
Video
GPS
A B’ D
C Latency: 35msAccuracy: 70%
Adapt implementation of TaskB to improve latency
GPS
Video
A B D
C Latency: 30msAccuracy: 75%
µP
µP
µP
µP
Video
GPS
Constraint: Latency < 38ms
A B’ D’
C Latency: 37msAccuracy: 65%
Adapt implementation of Tasks Band D to improve latency
GPS
Video
Change in Data
Change in Resources
Processor core becomes unavailable
• Task implementations should automatically adapt to changes in data quality, data availability, application requirements, and availability of computing resources
• Complexities of optimization necessitate new modeling approaches and supporting tools to facilitate design and enable runtime task adaptability
4
Data-adaptable System Modeling and Runtime Adaptation
R. Lysecky, August 11, 2016 InfoSymbiotics/DDDAS 2016, Hartford, CT
µP
µP
µP
µP
A B D
C Latency: 100msAccuracy: 75%
Increased data size leadsto congestion in Task B
Video
GPS
A B’ D
C Latency: 35msAccuracy: 70%
Adapt implementation of TaskB to improve latency
GPS
Video
A B D
C Latency: 30msAccuracy: 75%
µP
µP
µP
µP
Video
GPS
Constraint: Latency < 38ms
A B’ D’
C Latency: 37msAccuracy: 65%
Adapt implementation of Tasks Band D to improve latency
GPS
Video
A B’’ D
C’’ Latency: 65msAccuracy: 80%
Adapt implementation of Task B and C to improve accuracy at the expense of latency
GPS
VideoConstraint: Latency < 70ms
Change in Data
Change in Resources
Change in Requirements
Processor core becomes unavailable
• Task implementations should automatically adapt to changes in data quality, data availability, application requirements, and availability of computing resources
• Complexities of optimization necessitate new modeling approaches and supporting tools to facilitate design and enable runtime task adaptability
5
Data-adaptable System Modeling and Runtime Adaptation
R. Lysecky, August 11, 2016 InfoSymbiotics/DDDAS 2016, Hartford, CT
Video-based Vehicle Tracking and Collision Avoidance (VBVTCA)
6
• Analyze video to detect vehicles in real time
• Calculate necessary minimal travel distance (MTD) to slow down and match lead vehicle’s speed
• Implementation must adapt to:• Data: Video resolution, SNR, • Environmental conditions• Dynamic execution
requirements (e.g., residential or highway)
𝑫𝑫𝟎𝟎
Vehicle Detected
Speed Matched
R. Lysecky, August 11, 2016 InfoSymbiotics/DDDAS 2016, Hartford, CT
DASM: Modeling Application Task Flow
7
Video
Done
BSDS1 MF FDT
DownsampledVideo
Frame width Frame height
SNRLatency
DA
EADA
EA
Foreground Mask
Frame width Frame height
SNRLatency
Accuracy
DA
EADA
EAEA
DS2 IPM
PVMTDC A
• Task: Any executable computation (e.g., algorithm, filter, simulation) that consumes data and produces output. Ex: Background Subtraction (BS)
• Data types: Atomic units, or tokens, transferred between tasks. • Data attributes: Properties that describe a data type. Data attributes model the
assumptions made on the data inputs and outputs of a task. • Evaluation attributes: Models formalizing or estimating metrics to evaluate
composability of tasks. Ex: Accuracy, Latency, Uncertainty
R. Lysecky, August 11, 2016 InfoSymbiotics/DDDAS 2016, Hartford, CT
DASM: Modeling Task Options
8
• Task option: Specific implementation of a task. • For a computational task, task options represent different implementations for the
same high-level task.• For a simulation task, task options may represent different models
Video DoneBS
Gaussian Mix
Adaptive
1 Gaussian
DS1 MF FDTDS2 IPM P V MTDCA
1x
2x
4x
1x
2x
4x
720x480 1280x720
1280x960 1280x1024
1600x1200 1920x1080
704x480 None 2 pixel radius
4 pixel radius
6 pixel radius
7 pixel radius
18 pixel radius
Surf/Surf
Surf/ORB
Surf/Sift
R. Lysecky, August 11, 2016 InfoSymbiotics/DDDAS 2016, Hartford, CT
DASM: Modeling Attribute Transforms
9
Task Option 1
Frame Width DAT
SNR EAT
. . .
Input Data
Frame width Frame height
SNRLatency
DA
EADA
EA
Output Data
Frame widthFrame heightSNRLatency
DA
EADA
EA
• Attribute Transforms: Model or estimate of evaluation attributes for outputs based on input data/evaluation attributes.
• Distinguish between data attribute transforms (DAT) and evaluation attribute transforms (EAT)
• Enable end-to-end evaluation of attribute values• Used during design space exploration to evaluate system compositions
𝑆𝑆𝑆𝑆𝑆𝑆𝑜𝑜𝑜𝑜𝑜𝑜 = 𝑆𝑆𝑆𝑆𝑆𝑆𝐼𝐼𝐼𝐼 + 20 log 𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑𝑑
R. Lysecky, August 11, 2016 InfoSymbiotics/DDDAS 2016, Hartford, CT
DASM: Modeling Attribute Guards
10
Foreground Mask
Frame width Frame height
SNRLatency
DA
EADA
EA
Task Option 1
Gaussian Mixtures Background Subtraction
Adaptive Background Subtraction
Task Option N
Downsampled Video
Frame width Frame height
SNRLatency
DA
EADA
EA
…
Frame width: [320, 1080]Frame height: [640, 1920]SNR: [5 dB, 22dB]
Attribute Guard 1DADAEA
• Attribute Guards: Define composability of each Task Option
• Semantic Composability: Captures the composability of specific task options• Prevent use of a task option that is invalid/incompatible for some data attribute
values
• Programmatic Composability: Capture designer knowledge of suitability of task compositions
• Can formalize (or estimate) the propagation of programmatic composability
R. Lysecky, August 11, 2016 InfoSymbiotics/DDDAS 2016, Hartford, CT
Model-guided Genetic Optimization Algorithm
11
• Model-guided (MG) Genetic Optimization:• Uses model information (attribute guards) to determine compatibility between task
implementations, based on the task-flow model composed by the designer• MG Population Generation: generates semantically valid yet diverse
configurations• MG Crossover: ensures semantically valid offspring configurations• MG Mutation: generates mutated offspring maintaining validity
VideoCapture
Background Subtraction
Downsampling Morphological Filter
Feature Detection&Tracking
Inverse Perspective
MappingPosition
EstimationMin. Braking
Distance Calc.
Downsampling
Velocity Estimation
Acceleration Estimation
Physical Application
Attribute Constraint Calculator
Optimization Evaluator
Runtime DSE and Optimizer
VehicleControl
StateOptimize? Constraints
System Configuration
R. Lysecky, August 11, 2016 InfoSymbiotics/DDDAS 2016, Hartford, CT
Initial Model-guided Genetic Optimization Algorithm
60
70
80
90
100
0 20 40 60 80 100 120 140 160 180 200
Acc
urac
y (%
)
Generation
Optimize Accuracy [Latency < 847 ms]
model-guided standard
0
50
100
150
0
2
4
6
8
1% 2% 3% 4% 5%
Gen
erat
ions
Spee
dup
Percent of Optimal
Optimize Accuracy [Latency < 847 ms]MG gen. STD gen. Speedup
• Model-guided genetic algorithm finds optimal solution faster• Initial fitness improvements of 18.5% (up to 23.3% in different scenario)• 6.5X speedup, up to 26X in different scenario, and up to 544X compared to exhaustive
search• Standard genetic algorithm may fail to find optimal with quality constraints
• Challenges• Dynamically changing requirements necessitate adapting optimization goals• Multi-objective optimization will yield Pareto optimal compositions
12
R. Lysecky, August 11, 2016 InfoSymbiotics/DDDAS 2016, Hartford, CT
• Fuzzy Design Metric Classifications:• Define the acceptable or unacceptable values for each high-level metric
• Fuzzy Design Fitness Rules:• Define the relative importance of each metric within a particular
application• IF L IS F OR G OR S, AND A IS S, THE DESIGN IS S• IF L IS F OR G OR S, AND A IS G, THE DESIGN IS G• IF L IS F OR G OR S, AND A IS F, THE DESIGN IS F• IF L IS U, AND A IS F OR G OR S, THE DESIGN IS U
Specifying Dynamic Fitness for Optimization
• Weighted Functions:
• Difficult to determine appropriate weights.
100%80%60%40%20%
0%
Latency (sec)847 200 32 0
100%80%60%40%20%
0%
Accuracy (%)0 60 70 90 100
GoodFair
Superior
Legend
Unacceptable
13
R. Lysecky, August 11, 2016 InfoSymbiotics/DDDAS 2016, Hartford, CT
DASM: Modeling Fuzzy Logic Classification Synthesizer
• Fuzzy classification functions should change dynamically• Meaning of a “Good” latency changes as vehicle speeds change• Ex: A latency of 750ms may be Fair in a residential area, but may be Unacceptable
in a highway scenario (750ms equates to more than 20 meters of uninformed, i.e., “blind”, driving)
• Fuzzy Logic Classification Synthesizer model • Classification Attribute Transform: Designer-specified transform that can
estimate/produce a classification function based on EAs.• Ex: Latency CAT determines latency classification boundaries by calculating the
maximum tolerable latency, LatencyMAX
• Then, Unacceptable classification is any latency > LatencyMAX14
Attributes
PositionP
VelocityV
AccelA
EAEA
EA
Fuzzy Logic Classification Synthesizer
Latency CAT
Accuracy CAT
. . .
Latency Fuzzy Classification
Accuracy Fuzzy Classification
U/F F/G G/S S
U/F F/G G/S S
R. Lysecky, August 11, 2016 InfoSymbiotics/DDDAS 2016, Hartford, CT
Runtime Optimization Framework
Using designer-specified models, runtime framework generates fuzzy classification functions at runtime
Fuzzy classification functions make “constraints” redundant Ex: A latency constraint can be converted to a range of “unacceptable”
values.
15
Video BSDS1 MF
FDTIPMPMTDC
DS2
VA
Application Model
Fuzzy Logic Classification
Synthesis
StateFuzzy Metric
Classification Functions
System Configuration
Runtime DSE and Optimizer
Fuzzy Fitness EvaluationDSE
• IF L IS F OR G OR S, AND A IS S, THE DESIGN IS S• IF L IS F OR G OR S, AND A IS G, THE DESIGN IS G• IF L IS F OR G OR S, AND A IS F, THE DESIGN IS F• IF L IS U, AND A IS U OR F OR G OR S, THE DESIGN IS U
100%
80%
60%
40%
20%
0%
Latency (sec)
847 200
32 0
100%
80%
60%
40%
20%
0%
Accuracy (%)
0 60 70 90 100
R. Lysecky, August 11, 2016 InfoSymbiotics/DDDAS 2016, Hartford, CT
Experimental Results: Fuzzy vs Linear Weighted Function
Scenario ΔSpeed (mph)
Latency Fuzzy Classification Boundaries Accuracy Fuzzy Classification Boundaries
U/F F/G G/S S U/F F/G G/S S1 4 847 200 32 0 60% 70% 90% 100%2 8 749 200 32 0 50% 60% 85% 100%3 14 619 200 32 0 50% 60% 80% 100%4 16 508 200 32 0 50% 60% 75% 100%5 23 235 200 32 0 50% 60% 70% 100%
16
-50%
-25%
0%
25%
50%
75%
100%
Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5
Piecewise Linear VS Fuzzy Logic Based Optimization
Latency Accuracy Fitness
• Fuzzy based approach produced configurations that sacrificed latency by 8.4%,
• BUT, accuracy and total system fitness are both improved by 25.7% and 67.3%, respectively
R. Lysecky, August 11, 2016 InfoSymbiotics/DDDAS 2016, Hartford, CT
-80.0%
-40.0%
0.0%
40.0%
80.0%
Config 1 Config 2 Config 4 Config 5
Scenario 3 Latency Accuracy Fitness
-100%-80%-60%-40%-20%
0%20%40%
Config 2 Config 3 Config 4 Config 5
Scenario 1 Latency Accuracy Fitness
Experimental Results: Dynamic Composition and Optimization
• Determined the optimal configuration for each execution scenario (e.g., configuration 1 is optimal in scenario 1)
• Evaluated the performance of each configuration in the other scenarios
Improvement in latency come at the expense of lower accuracy and overall system fitness
Configurations 4 and 5 use an Unacceptable (unsafe) resolution to meet the stricter latency constraints in the respective scenarios.
17
X X
X X
Configurations 1 and 2 have Unacceptable latency
R. Lysecky, August 11, 2016 InfoSymbiotics/DDDAS 2016, Hartford, CT
Conclusions and Future Work
Conclusions Presented modeling and optimization extensions enabling intuitive tradeoff
specifications for competing design requirements and optimization goals Fuzzy Logic Classification Synthesizer model enables runtime synthesis of
fuzzy metric classification functions DASM modeling environment supports the specification of fuzzy design
fitness rules to define the relative importance of competing metrics when determining overall system fitness
Fuzzy logic based optimization achieves compositions that tradeoff unneededlatency reduction for improvements in accuracy and overall system fitness
Future Work Formalize opportunity cost of system reconfigurations
Investigate methods to understand when dynamic adaption is advantageous/disadvantageous subject to system performance constraints
• Runtime adaptive instrumentation and performance models• Online, low overhead profiling to measure actual data/evaluation attributes
and refine/train evaluation models• Validation and Verification with Physical Hardware
• Sensing and video in-the-loop with Robotic Ford Escape
18
R. Lysecky, August 11, 2016 InfoSymbiotics/DDDAS 2016, Hartford, CT19
Thank You
This work was supported by the AFOSR DDDAS program under grant #FA9550-15-1-0143.