Application of Evolutionary Algorithms for Multi-objective Optimization in VLSI and Embedded Systems.Material type: TextSeries: eBooks on DemandPublisher: Dordrecht : Springer, 2014Description: 1 online resource (181 p.)ISBN: 9788132219583Subject(s): Electronic digital computers -- Circuits -- Congresses | Integrated circuits -- Very large scale integration -- Design and construction -- Congresses | Signal processing -- Digital techniques -- Congresses | Soft computing -- CongressesGenre/Form: Electronic books.Additional physical formats: Print version:: Application of Evolutionary Algorithms for Multi-objective Optimization in VLSI and Embedded SystemsDDC classification: 006.3 | 621.39/5 LOC classification: TK7874.75Online resources: Click here to view this ebook.
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|TK7874.66 .D47 2016 Design and modeling of low power VLSI systems /||TK7874.7 Z436 2015 High Speed Digital Design :||TK7874.75 Closing the achievement gap from an international perspective :||TK7874.75 Application of Evolutionary Algorithms for Multi-objective Optimization in VLSI and Embedded Systems.||TK7874.75 -- .V575 1983 Materials and Process Characterization :||TK7874.75 -- .V575 1983 Vlsi Electronics Microstructure Science :||TK7874.75 -- .V575 1987 Vlsi Metallization :|
Preface; Organization of the Book; Acknowledgements; Contents; About the Editor; Chapter 1: Introduction to Multi-objective Evolutionary Algorithms; 1.1 Introduction; 1.2 Multi-objective Optimization Problem; 1.3 Why Evolutionary Algorithms?; 1.4 Multi-objective Evolutionary Algorithms; 1.5 Genetic Algorithm; 1.6 Multi-objective Genetic Algorithm; 1.6.1 Weighted Sum Genetic Algorithm (WSGA); 1.6.2 Nondominated Sorting Genetic Algorithm II (NSGA-II); 220.127.116.11 Nondominated Sorting; 18.104.22.168 Crowding Distance; 22.214.171.124 Crowded Tournament Selection; 1.6.3 NSGA-II with Controlled Elitism (NSGA-II-CE)
1.6.4 Hybrid NSGA-II with Pareto Hill Climbing (NSGA-II-PHC)1.7 Particle Swarm Optimization; 1.8 Multi-objective Particle Swarm Optimization; 1.8.1 Weighted Sum Particle Swarm Optimization; 1.8.2 Nondominated Sorting Particle Swarm Optimization (NSPSO); 1.8.3 Adaptive NSPSO (ANSPSO); 126.96.36.199 Learning Factors; 188.8.131.52 Inertia Weight; 1.8.4 Hybrid NSPSO with Pareto Hill Climbing (NSPSO-PHC); References; Chapter 2: Hardware/Software Partitioning for Embedded Systems; 2.1 Introduction; 2.2 Prior Work on HW/SW Partitioning; 2.3 Target Architecture; 2.4 Input Model; 2.5 Objective Function
2.6 Encoding Procedure2.7 Performance Metric Evaluation; 2.7.1 Metrics Evaluating Closeness to True Pareto-Optimal Front; 184.108.40.206 Error Ratio (ER); 220.127.116.11 Generational Distance (GD); 18.104.22.168 Maximum Pareto-Optimal Front Error (MFE); 2.7.2 Metrics Evaluating Diversity among Nondominated Solutions; 22.214.171.124 Spacing (S); 126.96.36.199 Spread (Delta); 188.8.131.52 Weighted Metric (W); 2.8 Experimental Results; 2.9 Summary; References; Chapter 3: Circuit Partitioning for VLSI Layout; 3.1 Introduction; 3.2 Prior Work on Circuit Partitioning; 3.3 Illustration of Circuit Bipartitioning Problem
3.4 Circuit Bipartitioning Using Multi-objective Optimization Algorithms3.4.1 Encoding Procedure; 3.4.2 Fitness Function Formulation; 3.5 Experimental Results; 3.6 Summary; References; Chapter 4: Design of Operational Amplifier; 4.1 Problem Definition; 4.2 Operational Amplifier Design; 4.2.1 Miller OTA Architecture; 184.108.40.206 Computation of Objectives; 4.2.2 Folded Cascode Amplifier Architecture; 220.127.116.11 Computation of Objectives; 4.3 Multi-objective Genetic Algorithm for Operational Amplifier Design; 4.3.1 Circuit Representation for Miller OTA
4.3.2 Circuit Representation for Folded Cascode OpAmp4.3.3 WSGA-Based OpAmp Design; 18.104.22.168 Fitness Function; 4.3.4 Experimental Results of WSGA Method; 22.214.171.124 Experimental Results for Folded Cascode OpAmp; 4.4 Operational Amplifier Design Using NSGA-II; 4.4.1 Multi-objective Fitness Function; 4.4.2 Simulation Results Obtained Using NSGA-II Algorithm; 4.5 Summary; References; Chapter 5: Design Space Exploration for Scheduling and Allocation in High Level Synthesis of Datapaths; 5.1 Introduction; 5.2 Datapath Synthesis; 5.3 Related Work
5.4 Multi-objective Evolutionary Approaches to Datapath Scheduling and Allocation
This book describes how evolutionary algorithms (EA), including genetic algorithms (GA) and particle swarm optimization (PSO) can be utilized for solving multi-objective optimization problems in the area of embedded and VLSI system design. Many complex engineering optimization problems can be modelled as multi-objective formulations. This book provides an introduction to multi-objective optimization using meta-heuristic algorithms, GA and PSO and how they can be applied to problems like hardware/software partitioning in embedded systems, circuit partitioning in VLSI, design of operational ampl
Description based upon print version of record.