Variation-Aware Analog Structural Synthesis

327 Pages·2009·6.71 MB·English

by  Trent McConaghy

Variation-Aware Analog Structural Synthesis
ANALOGCIRCUITSANDSIGNALPROCESSINGSERIES
ConsultingEditor:MohammedIsmail.OhioStateUniversity
Forothertitlespublishedinthisseries,goto
www.springer.com/series/7381
Trent McConaghy (cid:129) Pieter Palmers (cid:129) Peng Gao
Michiel Steyaert (cid:129) Georges Gielen
Variation-Aware Analog
Structural Synthesis
A Computational Intelligence Approach
123
Dr.TrentMcConaghy Prof.MichielSteyaert
SolidoDesignAutomation,Inc. KatholiekeUniversiteitLeuven
102-116ResearchDrive DepartmentofElectricalEngineering
SaskatoonSKS7N3R3 (ESAT)
Canada KasteelparkArenberg10
[email protected] 3001Leuven
Belgium
[email protected]
Dr.PieterPalmers
MephistoDesignAutomation
NV(MDA) Prof.GeorgesGielen
Romeinsestraat18 KatholiekeUniversiteitLeuven
3001Heverlee DepartmentofElectrotechnicalEngineering
Belgium Div.Microelectronics&Sensors(MICAS)
KasteelparkArenberg10
3001Leuven
PengGao Belgium
KatholiekeUniversiteitLeuven [email protected]
DepartmentofElectricalEngineering
(ESAT)
KasteelparkArenberg10
3001Leuven
Belgium
ISSN
ISBN978-90-481-2905-8 e-ISBN978-90-481-2906-5
DOI10.1007/978-90-481-2906-5
SpringerDordrechtHeidelbergLondonNewYork
LibraryofCongressControlNumber:2009927593
(cid:2)c SpringerScience+BusinessMediaB.V.2009
Nopartofthisworkmaybereproduced,storedinaretrievalsystem,ortransmittedinanyformorby
anymeans,electronic,mechanical,photocopying,microfilming,recordingorotherwise,withoutwritten
permissionfromthePublisher,withtheexceptionofanymaterialsuppliedspecificallyforthepurpose
ofbeingenteredandexecutedonacomputersystem,forexclusiveusebythepurchaserofthework.
Printedonacid-freepaper
SpringerispartofSpringerScience+BusinessMedia(www.springer.com)
Summary of Contents
“This page left intentionally blank.”
Contents
Preface xi
AcronymsandNotation xv
1. Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Background andContributions toAnalogCAD . . . . . . . . 4
1.3 Background andContributions toAI . . . . . . . . . . . . . . 17
1.4 AnalogCADIsaFruitflyforAI . . . . . . . . . . . . . . . . 24
1.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
2. Variation-AwareSizing:Background 27
2.1 Introduction andProblemFormulation . . . . . . . . . . . . 27
2.2 ReviewofYieldOptimization Approaches . . . . . . . . . . 32
2.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3. GloballyReliable,Variation-AwareSizing:SANGRIA 47
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.2 Foundations: Model-Building Optimization (MBO) . . . . . . 48
3.3 Foundations: Stochastic GradientBoosting . . . . . . . . . . 53
3.4 Foundations: Homotopy . . . . . . . . . . . . . . . . . . . . 59
3.5 SANGRIAAlgorithm . . . . . . . . . . . . . . . . . . . . . 59
3.6 SANGRIAExperimentalResults . . . . . . . . . . . . . . . 70
3.7 OnScalingtoLargerCircuits . . . . . . . . . . . . . . . . . 82
3.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
4. KnowledgeExtraction inSizing:CAFFEINE 85
4.1 Introduction andProblemFormulation . . . . . . . . . . . . 85
4.2 Background: GPandSymbolicRegression . . . . . . . . . . 90
viii Contents
4.3 CAFFEINECanonical FormFunctions . . . . . . . . . . . . 94
4.4 CAFFEINESearchAlgorithm . . . . . . . . . . . . . . . . . 96
4.5 CAFFEINEResults . . . . . . . . . . . . . . . . . . . . . . 102
4.6 ScalingUpCAFFEINE:Algorithm . . . . . . . . . . . . . . 113
4.7 ScalingUpCAFFEINE:Results . . . . . . . . . . . . . . . . 117
4.8 Application: Behaviorial Modeling . . . . . . . . . . . . . . 121
4.9 Application: Process-Variable Robustness Modeling . . . . . 125
4.10 Application: Design-Variable Robustness Modeling . . . . . . 138
4.11 Application: AutomatedSizing . . . . . . . . . . . . . . . . 139
4.12 Application: AnalyticalPerformanceTradeoffs . . . . . . . . 139
4.13 Sensitivity ToSearchAlgorithm . . . . . . . . . . . . . . . . 139
4.14 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
5. CircuitTopologySynthesis:Background 143
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
5.2 Topology-Centric Flows . . . . . . . . . . . . . . . . . . . . 145
5.3 Reconciling System-LevelDesign . . . . . . . . . . . . . . . 153
5.4 Requirements foraTopologySelection/Design Tool . . . . . 156
5.5 Open-Ended SynthesisandtheAnalogProblemDomain . . . 157
5.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
6. TrustworthyTopologySynthesis:MOJITOSearchSpace 169
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
6.2 SearchSpaceFramework . . . . . . . . . . . . . . . . . . . . 173
6.3 AHighlySearchable OpAmpLibrary . . . . . . . . . . . . . 180
6.4 Operating-Point DrivenFormulation . . . . . . . . . . . . . . 181
6.5 WorkedExample . . . . . . . . . . . . . . . . . . . . . . . . 182
6.6 SizeofSearchSpace . . . . . . . . . . . . . . . . . . . . . . 186
6.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 190
7. TrustworthyTopologySynthesis:MOJITOAlgorithm 191
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 191
7.2 High-LevelAlgorithm . . . . . . . . . . . . . . . . . . . . . 193
7.3 SearchOperators . . . . . . . . . . . . . . . . . . . . . . . . 196
7.4 HandlingMultipleObjectives . . . . . . . . . . . . . . . . . 199
7.5 Generation ofInitialIndividuals . . . . . . . . . . . . . . . . 202
7.6 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . 207
7.7 Experiment: HitTargetTopologies? . . . . . . . . . . . . . . 208
7.8 Experiment: Diversity? . . . . . . . . . . . . . . . . . . . . . 209
7.9 Experiment: Human-CompetitiveResults? . . . . . . . . . . 209
Contents ix
7.10 Discussion: ComparisontoOpen-EndedStructuralSynthesis . 212
7.11 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 213
8. KnowledgeExtraction inTopologySynthesis 215
8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
8.2 Generation ofDatabase . . . . . . . . . . . . . . . . . . . . . 218
8.3 Extraction ofSpecs-To-Topology DecisionTree . . . . . . . . 219
8.4 GlobalNonlinear SensitivityAnalysis . . . . . . . . . . . . . 223
8.5 Extraction ofAnalytical PerformanceTradeoffs . . . . . . . . 227
8.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 229
9. Variation-AwareTopologySynthesis&KnowledgeExtraction 231
9.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 231
9.2 ProblemSpecification . . . . . . . . . . . . . . . . . . . . . 231
9.3 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . 232
9.4 TowardsaSolution . . . . . . . . . . . . . . . . . . . . . . . 234
9.5 ProposedApproach: MOJITO-R . . . . . . . . . . . . . . . . 234
9.6 MOJITO-RExperimental Validation . . . . . . . . . . . . . . 237
9.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 244
10. NovelVariation-AwareTopologySynthesis 247
10.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . 247
10.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . 248
10.3 MOJITO-NAlgorithm andResults . . . . . . . . . . . . . . 249
10.4 ISCLEsAlgorithmAndResults . . . . . . . . . . . . . . . . 253
10.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . 266
11. Conclusion 267
11.1 GeneralContributions . . . . . . . . . . . . . . . . . . . . . 267
11.2 SpecificContributions . . . . . . . . . . . . . . . . . . . . . 267
11.3 FutureWork . . . . . . . . . . . . . . . . . . . . . . . . . . 270
11.4 FinalRemarks . . . . . . . . . . . . . . . . . . . . . . . . . 275
References 277
Index 301