beiträge zum optimierten entwurf fehler-und …...©2006 andreas könig, institute of integrated...
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© 2006 Andreas König, Institute of Integrated Sensor Systems
Institute of Integrated Sensor Systems
Dept. of Electrical Engineering and Information Technology
Beiträge zum optimierten Entwurf fehler-und störungs-toleranter Sensorsysteme für Mess- und Erkennungsaufgaben
Andreas KönigITG-Fachgruppensitzung, 17.11.2006
Übersicht:GruppenvorstellungÜbersicht früherer Arbeiten Selbstüberwachende und –reparierende SensorsystemeDynamisch rekonfigurierbare Sensorelektronik Zusammenfassung und Ausblick
© 2006 Andreas König, Institute of Integrated Sensor Systems
Institute of Integrated Sensor Systems
Dept. of Electrical Engineering and Information Technology
Research:Adaptive Sensor (µ-)ElectronicsSensor Fusion & Feature MappingLearning/Adaptation TechniquesDesign AutomationSensor System Integration
Labs:Computational Int. LabCAD-Lab (IC/MEMS)Test & Measurement LabSensor Technology Lab
Survey:Founded: April 2003Head: Prof. Andreas KönigStaff: 3 internal & 2 externaldoctoral students, 1 HIWI
Teaching:ElectronicsMeasurement TechnologySensor ElectronicsSensor Signal ProcessingNeurocomputing
© 2006 Andreas König, Institute of Integrated Sensor Systems
Prior WorkSample Applications from former Projects
Bio-Inspired Sensors, Circuits
& Systems
Bio-Inspired Sensors, Circuits
& SystemsBiometry
Automotive
Inspection Man-MachineInterface
Robot
Vision
© 2006 Andreas König, Institute of Integrated Sensor Systems
Prior Work Survey of Sample Sensor Chips from former Projects
Selection of Sensorchips &
Systems
Selection of Sensorchips &
SystemsRobot
Vision
Meter Readng
DOG-Chip
LAPIS HDR
LUCOS HDRELAC Chip
LOC
Low-Power Classifier
Electronic Fovea
© 2006 Andreas König, Institute of Integrated Sensor Systems
Prior WorkHolistic Physical Design of Recognition Systems (First Design-Flow)
Behavioral
Geometrical
FunctionalQuickCog
Modelling of Reference System
Stimuli, Parameters (C/C++, HDLs)
Simulation ResultsModels, Constraints
SpectreSimulation
Design: A/D-Partitioning
Assessment &Optimization,DimensionalityReduction
© 2006 Andreas König, Institute of Integrated Sensor Systems
Prior WorkHolistic Modelling and Design Methodology for IES/IMEMS
QuickCog IES DF Top-Level:
Fast & consistent designAssessment and optimization Intra/inter level optimizationHolistic modelling and simulationOpportunistic & parsimoniousAFS salience: physical savings !
AFS
Cla
ssifi
er
© 2006 Andreas König, Institute of Integrated Sensor Systems
Prior WorkOrigins of Adaptive Electronics
I II
III
Mixed-Signal
OC
© 2006 Andreas König, Institute of Integrated Sensor Systems
Self-Monitoring and –Repairing Sensor SystemsApplication for (Adaptive) Sensor Electronics
Em
bedd
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ting
Ubi
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Perv
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Des
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Com
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Disa
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Com
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Reconfigurable Computing Organic Computing (http.www.organic-computing.com)
Static and dynamic (re)configuration based on programmable (logic) device
Mimicking capabilities Mimicking capabilities of of living living beingsbeings//organismsorganisms: : selfself--xx--propertiesproperties
Mai
n Fr
ame C
ompu
ting
© 2006 Andreas König, Institute of Integrated Sensor Systems
Self-Monitoring and –Repairing Sensor SystemsPrincipal Sensor System
Sensor(s) AnalogElectronics
DigitalElectronics
BUS/RF-Ifc.
ObservedQuantity
Noise/perturbation:• static (manufacturing/assembly tolerances, ...)•dynamic(T, P, moisture, vibrations, depositions, ...)
electrical signal
energy
processed signal
measurement,decision
hardwiredprocessing
software/algorithms
Sensor systems are susceptible to drift & manufacturing problemsSophisticated design and self-monitoring & -repair features required
© 2006 Andreas König, Institute of Integrated Sensor Systems
Self-Monitoring and –Repairing Sensor SystemsTowards (Intelligent) Sensor System Design Automation
Sensor(s) AnalogElectronics
DigitalElectronics
BUS/RF-Ifc.
ObservedQuantity
electrical signal
energy
processed signal
measurement,decision
hardwiredprocessing
software/algorithms
Design phase: Assembly of suitable sensors & methods for IES/IMEMS
SensorArray
Signal proc.feature comp.
ClassifierTrain/Test
Classificationresult
Dimensionality Reduction
© 2006 Andreas König, Institute of Integrated Sensor Systems
Self-Monitoring and –Repairing Sensor SystemsTowards (Intelligent) Sensor System Design Automation
SensorArray
Signal proc.feature comp.
ClassifierTrain/Test
Dimensionality Reduction
Emerging alternative: Automated design by suitable optimzation
State of the art: expert driven design, manual, costly, slow, & tedious
observations(re)configuration
SensorArray
Signal proc.feature comp.
ClassifierTrain/Test
Classificationresult
Dimensionality Reduction
Observation & Optimization
Classificationresult
© 2006 Andreas König, Institute of Integrated Sensor Systems
Self-Monitoring and –Repairing Sensor SystemsTowards (Intelligent) Sensor System Design Automation
Reduction to the following subproblems:Selection/combinationParameter optimizationStructural optimization Function generation (e.g., breeding of texture filters [M. Köppen])
Automation of the design process currently requires limitationsCommonly only feed-forward architecture are consideredSimple graph structures with nodes of fixed, established methodblocks representing quasi ´´standard cells´´
SensorArray
Signal proc.feature comp.
ClassifierTrain/Test
Classificationresult
Dimensionality Reduction
Observation & Optimization
© 2006 Andreas König, Institute of Integrated Sensor Systems
Self-Monitoring and –Repairing Sensor SystemsDesign Methodology for IES/IMEMS
Size, weight,power
Size, weight,power
Real-timeReal-timeTime-to-marketTime-to-market
Reliability,Safety, (FT)Reliability,Safety, (FT)
MultiobjectiveDesign Optimization
MultiobjectiveDesign Optimization
Cost(low/high vol.)
Cost(low/high vol.)
Flexibility,Adaptivity
Flexibility,Adaptivity
Performance(Recognition)Performance(Recognition)
Appropriate methodology and flow for viable & feasible design mandatoryApproach: Bio-inspired adaptive circuits & systems (integrated HW/SW)
© 2006 Andreas König, Institute of Integrated Sensor Systems
Self-Monitoring and –Repairing Sensor SystemsDesign Methodology for IES/IMEMS
Intelligent System Design Framework
Intelligent System Design Framework
ES/MEMS Design Framework
ES/MEMS Design Framework
Feedforward of feasible behavioral IS
Feedback of Constraints
Linking the Frameworks of IS & ES design using multiobjective optimizationMerging SW & HW development by chosen information processing paradigmLong term: Migrate from design-time to run-time optimization/adaptation
© 2006 Andreas König, Institute of Integrated Sensor Systems
Self-Monitoring and –Repairing Sensor SystemsDesign Methodology for IES/IMEMS: Phases of System Life Cycle
Intelligent System Development
Intelligent System Development
Design time
Design time solution development commonly bases on single prototypeDeployment to multiple instances demands for static deviation compensationVarious dynamic perturbation influences demand for dynamic compensation
Operation time Deployment time
IES 1IES 1
IES 2IES 2
IES NIES N
IES 1IES 1
IES 2IES 2
IES NIES N
staticdeviations
dynamicperturbations
© 2006 Andreas König, Institute of Integrated Sensor Systems
Self-Monitoring and –Repairing Sensor SystemsFrom Design to Deployment Time: Instance-Specific Compensation
Machine-In-the-Loop-LearningGeneral system developmentInstance training for compensationof static non-idealities & deviations
Instance OptimizationInstance Optimization
Parameter,Structure
Design Phase
-
Results
TargetvaluesTargetvalues
Sensordata,
Features
DatabaseDatabase
•Synthesis•Compensation
Medical Laboratory Robot DAVID:Task: Tubes sorting & decappingMultiple installation sites in Europe
© 2006 Andreas König, Institute of Integrated Sensor Systems
Self-Monitoring and –Repairing Sensor SystemsDesirable Properties for Autonomous Sensor System
Sensor(s) AnalogElectronics
DigitalElectronics
BUS/RF-Ifc.
ObservedQuantity
Noise/perturbation
electrical signal
energy
processed signal
measurement,decision
Need: reliability, availability, robustness, predictive maintenance
Self-monitoring: disturbance/defect detection & diagnosis (cause)Self-repairing: self-monitoring & reconfiguration capability
Constant/repeated monitoring of measurement signal validity/integrityMethods: Sensor redundancy, actuator induced reference signal, measurement signal analysis, ... (TUD, EMK; TU Delft, ....)First research step: dynamically reconfigurable sensor electronics
© 2006 Andreas König, Institute of Integrated Sensor Systems
Dynamically Reconfigurable Sensor ElectronicsAvailable Reconfigurable Sensor Electronics
Recent approaches and products support dynamic self-calibration of analog systems/components
ALD2724x(EPAD)ALD2724x(EPAD) AD8555 (DigiTrim)AD8555 (DigiTrim)
Trimming by EEPROM (finite correction cycles)Trimming by DAC (volatile memory/switches, infinite correction cycles)
© 2006 Andreas König, Institute of Integrated Sensor Systems
Dynamically Reconfigurable Sensor ElectronicsAvailable Reconfigurable Sensor Electronics
Zetex TRACZetex TRAC
AnadigmVortexAnadigmVortex
Cypress PSoCCypress PSoC
Lattice ispPAC30Lattice ispPAC30
IMTEK GmC-FiltersIMTEK GmC-Filters
Collection of Reconfigurable Analog Electronics and Evolvable Hardware – Field-Programmable Analog/Transistor Arrays
KIP/JPL FPTAKIP/JPL FPTA
Approaches differ in granularityCommercial versions: Building block level
© 2006 Andreas König, Institute of Integrated Sensor Systems
Dynamically Reconfigurable Sensor ElectronicsAdvanced Architecture of Adaptive/Reconfigurable Mixed-Signal Systems
ProcessingUnit (Rec.)
ProcessingUnit (Rec.) ActuatorsActuatorsDACDAC
FPGA/ASIC
FPGA/ASIC MemoryMemory
Human IfcHuman Ifc DiagnosticsDiagnostics Aux. SystemsWireless
Aux. SystemsWireless
FPMAAdaptive
Sensor Signal Conditioning& ConversionSensorsSensors
Dynamically reconfigurable FPMA meeting industry requirementsRapid-prototyping and flexibility for sensor front-end (freq. limits !)Overall reconfigurable embedded sensor system architecture (SoC) including metrics and learning/optimization featuresInherently, fault-tolerance and self-x-features of OC are provided
© 2006 Andreas König, Institute of Integrated Sensor Systems
Dynamically Reconfigurable Sensor ElectronicsEvolvable Electronics:CAS Optimization in Design Phase & Post Fabrication
Intrinsic EvolutionIntrinsic Evolution
Parameter,Structure
Post Fabrication
-
Results
TargetvaluesTargetvalues
Sensordata,
Features
DatabaseDatabase
•Compensation•Yield increase
Evolving parameters with thereconfigurable hardware in the loop CHILL (Intel 89, ETANN)Multiobjective CAS Optimization
Extrinsic EvolutionExtrinsic Evolution
Parameter,Structure
Design Phase
-
Results,Modells
TargetvaluesTargetvalues
Sensordata,
Features
DatabaseDatabase
•Compensation•Centering
Evolution on circuit-levelEvolving new circuits CHILL in design phaseMultiobjective CAS Optimization
© 2006 Andreas König, Institute of Integrated Sensor Systems
Conclusions and Outlook
Activities & interest in sensor signal processing and intelligent system design (Sensor fusion, non linear methods, optimization, …)
Autoconfiguration and design automation appealing under scientificand industrial/commercial aspects (Talks 1 and 2)
Concepts of holistic design can be pursued down to hardware design and physical system integration (Talks 3 and 4)
Incorporating multiobjective optimization and reconfiguration/adap-tation will return viable, robust, and cost effective systems
Automation & automotive seem to be excellents field for application !