An Artificial Intelligence Approach to VLSI Routing
An Artificial Intelligence Approach to VLSI Routing

Author: R. Joobbani

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 174

ISBN-13: 1461325552

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Routing of VLSI chips is an important, time consuming, and difficult problem. The difficulty of the problem is attributed to the large number of often conflicting factors that affect the routing quality. Traditional techniques have approached routing by ignoring some of these factors and imposing unnecessary constraints in order to make routing tractable. In addition to the imposition of these restrictions, which simplify the problems to a degree but at the same time reduce the routing quality, traditional approaches use brute force. They often transform the problem into mathematical or graph problems and completely ignore the specific knowledge about the routing task that can greatly help the solution. This thesis overcomes some of the above problems and presents a system that performs routing close to what human designers do. In other words it heavily capitalizes on the knowledge of human expertise in this area, it does not impose unnecessary constraints, it considers all the different factors that affect the routing quality, and most importantly it allows constant user interaction throughout the routing process. To achieve the above, this thesis presents background about some representative techniques for routing and summarizes their characteristics. It then studies in detail the different factors (such as minimum area, number of vias, wire length, etc.) that affect the routing quality, and the different criteria (such as vertical/horizontal constraint graph, merging, minimal rectilinear Steiner tree, etc.) that can be used to optimize these factors.

An Artificial Intelligence Approach to Test Generation
An Artificial Intelligence Approach to Test Generation

Author: Narinder Singh

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 202

ISBN-13: 146131979X

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I am indebted to my thesis advisor, Michael Genesereth, for his guidance, inspiration, and support which has made this research possible. As a teacher and a sounding board for new ideas, Mike was extremely helpful in pointing out Haws, and suggesting new directions to explore. I would also like to thank Harold Brown for introducing me to the application of artificial intelligence to reasoning about designs, and his many valuable comments as a reader of this thesis. Significant contribu tions by the other members of my reading committee, Mark Horowitz, and Allen Peterson have greatly improved the content and organization of this thesis by forcing me to communicate my ideas more clearly. I am extremely grateful to the other members of the Logic Group at the Heuristic Programming Project for being a sounding board for my ideas, and providing useful comments. In particular, I would like to thank Matt Ginsberg, Vineet Singh, Devika Subramanian, Richard Trietel, Dave Smith, Jock Mackinlay, and Glenn Kramer for their pointed criticisms. This research was supported by Schlumberger Palo Alto Research (previously Fairchild Laboratory for Artificial Intelligence). I am grateful to Peter Hart, the former head of the AI lab, and his successor Marty Tenenbaum for providing an excellent environment for performing this research.

VLSI Placement and Global Routing Using Simulated Annealing
VLSI Placement and Global Routing Using Simulated Annealing

Author: Carl Sechen

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 298

ISBN-13: 1461316979

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From my B.E.E degree at the University of Minnesota and right through my S.M. degree at M.I.T., I had specialized in solid state devices and microelectronics. I made the decision to switch to computer-aided design (CAD) in 1981, only a year or so prior to the introduction of the simulated annealing algorithm by Scott Kirkpatrick, Dan Gelatt, and Mario Vecchi of the IBM Thomas 1. Watson Research Center. Because Prof. Alberto Sangiovanni-Vincentelli, my UC Berkeley advisor, had been a consultant at IBM, I re ceived a copy of the original IBM internal report on simulated annealing approximately the day of its release. Given my background in statistical mechanics and solid state physics, I was immediately impressed by this new combinatorial optimization technique. As Prof. Sangiovanni-Vincentelli had suggested I work in the areas of placement and routing, it was in these realms that I sought to explore this new algorithm. My flJ'St implementation of simulated annealing was for an island-style gate array placement problem. This work is presented in the Appendix of this book. I was quite struck by the effect of a nonzero temperature on what otherwise appears to be a random in terchange algorithm.

Routing in the Third Dimension
Routing in the Third Dimension

Author: Naveed A. Sherwani

Publisher: John Wiley & Sons

Published: 1995-03

Total Pages: 386

ISBN-13: 9780780310896

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This key text addresses the complex computer chips of tomorrow which will consist of several layers of metal interconnect, making the interconnect within a chip or a multichip module a three dimensional problem. You'll find an insightful approach to the algorithmic, cell design issues in chip and MCM routing with an emphasis on techniques for eliminating routing area.

Coordination of Distributed Problem Solvers
Coordination of Distributed Problem Solvers

Author: Edmund H. Durfee

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 278

ISBN-13: 1461316995

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As artificial intelligence (AI) is applied to more complex problems and a wider set of applications, the ability to take advantage of the computational power of distributed and parallel hardware architectures and to match these architec tures with the inherent distributed aspects of applications (spatial, functional, or temporal) has become an important research issue. Out of these research concerns, an AI subdiscipline called distributed problem solving has emerged. Distributed problem-solving systems are broadly defined as loosely-coupled, distributed networks of semi-autonomous problem-solving agents that perform sophisticated problem solving and cooperatively interact to solve problems. N odes operate asynchronously and in parallel with limited internode commu nication. Limited internode communication stems from either inherent band width limitations of the communication medium or from the high computa tional cost of packaging and assimilating information to be sent and received among agents. Structuring network problem solving to deal with consequences oflimited communication-the lack of a global view and the possibility that the individual agents may not have all the information necessary to accurately and completely solve their subproblems-is one of the major focuses of distributed problem-solving research. It is this focus that also is one of the important dis tinguishing characteristics of distributed problem-solving research that sets it apart from previous research in AI.

Evolutionary Algorithms for VLSI CAD
Evolutionary Algorithms for VLSI CAD

Author: Rolf Drechsler

Publisher: Springer Science & Business Media

Published: 2013-03-14

Total Pages: 184

ISBN-13: 1475728662

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In VLSI CAD, difficult optimization problems have to be solved on a constant basis. Various optimization techniques have been proposed in the past. While some of these methods have been shown to work well in applications and have become somewhat established over the years, other techniques have been ignored. Recently, there has been a growing interest in optimization algorithms based on principles observed in nature, termed Evolutionary Algorithms (EAs). Evolutionary Algorithms in VLSI CAD presents the basic concepts of EAs, and considers the application of EAs in VLSI CAD. It is the first book to show how EAs could be used to improve IC design tools and processes. Several successful applications from different areas of circuit design, like logic synthesis, mapping and testing, are described in detail. Evolutionary Algorithms in VLSI CAD consists of two parts. The first part discusses basic principles of EAs and provides some easy-to-understand examples. Furthermore, a theoretical model for multi-objective optimization is presented. In the second part a software implementation of EAs is supplied together with detailed descriptions of several EA applications. These applications cover a wide range of VLSI CAD, and different methods for using EAs are described. Evolutionary Algorithms in VLSI CAD is intended for CAD developers and researchers as well as those working in evolutionary algorithms and techniques supporting modern design tools and processes.

Evolutionary Algorithms in Engineering Applications
Evolutionary Algorithms in Engineering Applications

Author: Dipankar Dasgupta

Publisher: Springer Science & Business Media

Published: 2013-06-29

Total Pages: 561

ISBN-13: 3662034239

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Evolutionary algorithms are general-purpose search procedures based on the mechanisms of natural selection and population genetics. They are appealing because they are simple, easy to interface, and easy to extend. This volume is concerned with applications of evolutionary algorithms and associated strategies in engineering. It will be useful for engineers, designers, developers, and researchers in any scientific discipline interested in the applications of evolutionary algorithms. The volume consists of five parts, each with four or five chapters. The topics are chosen to emphasize application areas in different fields of engineering. Each chapter can be used for self-study or as a reference by practitioners to help them apply evolutionary algorithms to problems in their engineering domains.

Configurable Intelligent Optimization Algorithm
Configurable Intelligent Optimization Algorithm

Author: Fei Tao

Publisher: Springer

Published: 2014-08-18

Total Pages: 364

ISBN-13: 3319088408

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Presenting the concept and design and implementation of configurable intelligent optimization algorithms in manufacturing systems, this book provides a new configuration method to optimize manufacturing processes. It provides a comprehensive elaboration of basic intelligent optimization algorithms, and demonstrates how their improvement, hybridization and parallelization can be applied to manufacturing. Furthermore, various applications of these intelligent optimization algorithms are exemplified in detail, chapter by chapter. The intelligent optimization algorithm is not just a single algorithm; instead it is a general advanced optimization mechanism which is highly scalable with robustness and randomness. Therefore, this book demonstrates the flexibility of these algorithms, as well as their robustness and reusability in order to solve mass complicated problems in manufacturing. Since the genetic algorithm was presented decades ago, a large number of intelligent optimization algorithms and their improvements have been developed. However, little work has been done to extend their applications and verify their competence in solving complicated problems in manufacturing. This book will provide an invaluable resource to students, researchers, consultants and industry professionals interested in engineering optimization. It will also be particularly useful to three groups of readers: algorithm beginners, optimization engineers and senior algorithm designers. It offers a detailed description of intelligent optimization algorithms to algorithm beginners; recommends new configurable design methods for optimization engineers, and provides future trends and challenges of the new configuration mechanism to senior algorithm designers.

Evolutionary Scheduling
Evolutionary Scheduling

Author: Keshav Dahal

Publisher: Springer Science & Business Media

Published: 2007-02-15

Total Pages: 631

ISBN-13: 3540485821

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Evolutionary scheduling is a vital research domain at the interface of artificial intelligence and operational research. This edited book gives an overview of many of the current developments in the large and growing field of evolutionary scheduling. It demonstrates the applicability of evolutionary computational techniques to solve scheduling problems, not only to small-scale test problems, but also fully-fledged real-world problems.