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This book presents novel circuit-level evolutionary partitioning algorithms to optimize VLSI physical design automation. It addresses limitations in traditional partitioning methods by introducing enhanced techniques such as Modified Genetic Algorithm (MGA), Modified Particle Swarm Optimization (MPSO), and a new Graph Cellular Automata (GCA) approach. These algorithms aim to minimize cut size, reduce interconnection delays, and improve overall circuit performance. Experimental validation using ISCAS'85 benchmark circuits confirms the efficiency and superiority of the proposed methods in terms of partition quality and computational time.…mehr

Produktbeschreibung
This book presents novel circuit-level evolutionary partitioning algorithms to optimize VLSI physical design automation. It addresses limitations in traditional partitioning methods by introducing enhanced techniques such as Modified Genetic Algorithm (MGA), Modified Particle Swarm Optimization (MPSO), and a new Graph Cellular Automata (GCA) approach. These algorithms aim to minimize cut size, reduce interconnection delays, and improve overall circuit performance. Experimental validation using ISCAS'85 benchmark circuits confirms the efficiency and superiority of the proposed methods in terms of partition quality and computational time.
Autorenporträt
Dr. R. Pavithra Guru is an Assistant Professor at SRMIST, Chennai, with expertise in VLSI design, machine learning, and intelligent systems. He holds a Ph.D. from Anna University, has published 15+ papers, holds multiple patents, and completed a postdoc at City, University of London. He is passionate about innovation and mentoring.