Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/306375
Title: Study and Analysis of Minimization Algorithms for VLSI Circuit Synthesis
Researcher: Bansal, Manu
Guide(s): Agarwal, Alpana
Keywords: Genetic Algorithm
LGSynth93 Benchmark Circuits
Variable ordering
University: Thapar Institute of Engineering and Technology
Completed Date: 2018
Abstract: Low power consumption has emerged as a key design parameter for digital VLSI systems. Therefore, accurate methods are required to estimate the switching activity at the internal nodes of the logic circuits to determine the average power dissipation. Since, manipulation of Boolean functions is an important element of most logic synthesis algorithms, including logic optimization and logic verification of sequential and combinational circuits, therefore, it is important to have efficient methods to represent and manipulate such functions. A major problem with binary decision diagrams (BDD) based manipulation is the need for application-specific heuristic algorithms to order the input variables before processing. Therefore, finding a good variable order for ordered binary decision diagrams (OBDDs) is an essential part of OBDD-based CAD tools. The three techniques i.e. Genetic Algorithm, Hybridized Genetic Algorithm and Modified Memetic Algorithm (MMA) have been proposed for the variable reordering problem and determining and minimizing signal activity in BDDs. Ordering of variables in BDDs play a major role in reduction of nodes and hence the area. The performance of the genetic algorithms depends, to a great extent, on the performance of the crossover operator used. Three new versions of the crossover operator which overcome these problems are order crossover, cycle crossover and partially mapped crossover (PMX). All the three proposed algorithms based on the crossover techniques are implemented and validated on multi-input multi-output (MIMO) LGSynth93 Benchmark Circuits in order to find an optimal input variable order to reduce node count hence area, simultaneously reducing the signal activity hence reducing power dissipation using BDD-based probabilistic technique. The proposed Modified Memetic algorithm shows better results as compared to Genetic and Hybridized Genetic algorithms for all MIMO circuits.
Pagination: 106p.
URI: http://hdl.handle.net/10603/306375
Appears in Departments:Department of Electronics and Communication Engineering

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02_certificate.pdf231.18 kBAdobe PDFView/Open
03_acknowledgement.pdf350.41 kBAdobe PDFView/Open
04_abstract.pdf15.52 kBAdobe PDFView/Open
05_contents.pdf68.98 kBAdobe PDFView/Open
06_list of figures.pdf154.55 kBAdobe PDFView/Open
07_list of tables.pdf24.1 kBAdobe PDFView/Open
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09_chapter 1.pdf793.84 kBAdobe PDFView/Open
10_chapter 2.pdf518.34 kBAdobe PDFView/Open
11_chapter 3.pdf242.71 kBAdobe PDFView/Open
12_chapter 4.pdf1.79 MBAdobe PDFView/Open
13_chapter 5.pdf1.2 MBAdobe PDFView/Open
14_chapter 6.pdf929.22 kBAdobe PDFView/Open
15_chapter 7.pdf1.63 MBAdobe PDFView/Open
16_references.pdf1.4 MBAdobe PDFView/Open
17_list of publications.pdf635.8 kBAdobe PDFView/Open
80_recommendation.pdf693.5 kBAdobe PDFView/Open
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