Bhuvaneswari, M.C.

Application of Evolutionary Algorithms for Multi-objective Optimization in VLSI and Embedded Systems. - Dordrecht : Springer, 2014. - 1 online resource (181 p.) - eBooks on Demand .

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); Nondominated Sorting; Crowding Distance; 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); Learning Factors; 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; Error Ratio (ER); Generational Distance (GD); Maximum Pareto-Optimal Front Error (MFE); 2.7.2 Metrics Evaluating Diversity among Nondominated Solutions; Spacing (S); Spread (Delta); 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; Computation of Objectives; 4.2.2 Folded Cascode Amplifier Architecture; 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; Fitness Function; 4.3.4 Experimental Results of WSGA Method; 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

9788132219583 129 (NL)

Electronic digital computers -- Circuits -- Congresses.
Integrated circuits -- Very large scale integration -- Design and construction -- Congresses.
Signal processing -- Digital techniques -- Congresses.
Soft computing -- Congresses.

Electronic books.


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