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Produktbild: Variation-Aware Analog Structural Synthesis

Variation-Aware Analog Structural Synthesis A Computational Intelligence Approach

Aus der Reihe Analog Circuits and Signal Processing

138,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

29.11.2011

Verlag

Springer Netherland

Seitenzahl

305

Maße (L/B/H)

23,5/15,5/1,8 cm

Gewicht

499 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-94-007-2608-6

Beschreibung

Rezension

From the reviews:

“This book is squarely aimed at analog circuit designers who are searching for new approaches to analog structural design and optimization. … Those most likely to benefit from this book … are experts in the field of industrial circuit design seeking insight into new design tools. … for the non-expert, with only a cursory understanding of the background material, the processes and results described in this book are an inspiring example of real-world applications of evolutionary design.” (John Rieffel, Genetic Programming and Evolvable Machines, Vol. 12, 2011)

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

29.11.2011

Verlag

Springer Netherland

Seitenzahl

305

Maße (L/B/H)

23,5/15,5/1,8 cm

Gewicht

499 g

Auflage

1. Auflage

Sprache

Englisch

ISBN

978-94-007-2608-6

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

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  • Produktbild: Variation-Aware Analog Structural Synthesis
  • Preface. Acronyms and Notation.
    1. INTRODUCTION. 1.1 Motivation. 1.2 Background and Contributions to Analog CAD. 1.3 Background and Contributions to AI. 1.4 Analog CAD Is a Fruitfly for AI. 1.5 Conclusion.
    2. VARIATION-AWARE SIZING: BACKGROUND. 2.1 Introduction and Problem Formulation. 2.2 Review of Yield Optimization Approaches. 2.3 Conclusion.
    3. GLOBALLY RELIABLE, VARIATION-AWARE SIZING: SANGRIA. 3.1 Introduction. 3.2 Foundations: Model-Building Optimization (MBO). 3.3 Foundations: Stochastic Gradient Boosting. 3.4 Foundations: Homotopy. 3.5 SANGRIA Algorithm. 3.6 SANGRIA Experimental Results. 3.7 On Scaling to Larger Circuits. 3.8 Conclusion.
    4. KNOWLEDGE EXTRACTION IN SIZING: CAFFEINE. 4.1 Introduction and Problem Formulation. 4.2 Background: GP and Symbolic Regression. 4.3 CAFFEINE Canonical Form Functions. 4.4 CAFFEINE Search Algorithm. 4.5 CAFFEINE Results. 4.6 Scaling Up CAFFEINE: Algorithm. 4.7 Scaling Up CAFFEINE: Results. 4.8 Application: Behaviorial Modeling. 4.9 Application: Process-Variable Robustness Modeling. 4.10 Application: Design-Variable Robustness Modeling. 4.11 Application: Automated Sizing. 4.12 Application: Analytical Performance Tradeoffs. 4.13 Sensitivity To Search Algorithm. 4.14 Conclusion.
    5. CIRCUIT TOPOLOGY SYNTHESIS: BACKGROUND. 5.1 Introduction. 5.2 Topology-Centric Flows. 5.3 Reconciling System-Level Design. 5.4 Requirements for a Topology Selection / Design Tool. 5.5 Open-Ended Synthesis and the Analog Problem Domain. 5.6 Conclusion.
    6. TRUSTWORTHY TOPOLOGY SYNTHESIS: MOJITO SEARCH SPACE. 6.1 Introduction. 6.2 Search Space Framework. 6.3 A Highly Searchable Op Amp Library. 6.4 Operating-Point Driven Formulation. 6.5 Worked Example. 6.6 Size of Search Space. 6.7 Conclusion.
    7. TRUSTWORTHY TOPOLOGY SYNTHESIS: MOJITO ALGORITHM. 7.1 Introduction. 7.2 High-Level Algorithm. 7.3 Search Operators. 7.4 Handling Multiple Objectives. 7.5 Generation of Initial Individuals. 7.6 Experimental Setup. 7.7 Experiment: Hit Target Topologies? 7.8 Experiment: Diversity? 7.9 Experiment: Human-Competitive Results? 7.10 Discussion: Comparison to Open-Ended Structural Synthesis. 7.11 Conclusion.
    8. KNOWLEDGE EXTRACTION IN TOPOLOGY SYNTHESIS. 8.1 Introduction. 8.2 Generation of Database. 8.3 Extraction of Specs-To-Topology Decision Tree. 8.4 Global Nonlinear Sensitivity Analysis. 8.5 Extraction of Analytical Performance Tradeoffs. 8.6 Conclusion.
    9. VARIATION-AWARE TOPOLOGY SYNTHESIS & KNOWLEDGE EXTRACTION. 9.1 Introduction. 9.2 Problem Specification. 9.3 Background. 9.4 Towards a Solution. 9.5 Proposed Approach: MOJITO-R. 9.6 MOJITO-R Experimental Validation. 9.7 Conclusion.
    10. NOVEL VARIATION-AWARE TOPOLOGY SYNTHESIS. 10.1 Introduction. 10.2 Background. 10.3 MOJITO-N Algorithm and Results. 10.4 ISCLEs Algorithm And Results. 10.5 Conclusion.
    11. CONCLUSION. 11.1 General Contributions. 11.2 Specific Contributions. 11.3 Future Work. 11.4 Final Remarks.

    References. Index.