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This book provides readers with an up-to-date account of the use of machine learning frameworks, methodologies, algorithms and techniques in the context of computer-aided design (CAD) for very-large-scale integrated circuits (VLSI). Coverage includes the various machine learning methods used in lithography, physical design, yield prediction, post-silicon performance analysis, reliability and failure analysis, power and thermal analysis, analog design, logic synthesis, verification, and neuromorphic design. Provides up-to-date information on machine learning in VLSI CAD for device modeling, layout verifications, yield prediction, post-silicon validation, and reliability; Discusses the use of machine learning techniques in the context of analog and digital synthesis; Demonstrates how to formulate VLSI CAD objectives as machine learning problems and provides a comprehensive treatment of their efficient solutions; Discusses the tradeoff between the cost of collecting data and prediction accuracy and provides a methodology for using prior data to reduce cost of data collection in the design, testing and validation of both analog and digital VLSI designs. From the Foreword As the semiconductor industry embraces the rising swell of cognitive systems and edge intelligence, this book could serve as a harbinger and example of the osmosis that will exist between our cognitive structures and methods, on the one hand, and the hardware architectures and technologies that will support them, on the other....As we transition from the computing era to the cognitive one, it behooves us to remember the success story of VLSI CAD and to earnestly seek the help of the invisible hand so that our future cognitive systems are used to design more powerful cognitive systems. This book is very much aligned with this on-going transition from computing to cognition, and it is with deep pleasure that I recommend it to all those who are actively engaged in this exciting transformation. Dr. Ruchir Puri, IBM Fellow, IBM Watson CTO & Chief Architect, IBM T. J. Watson Research Center
This book constitutes the refereed proceedings of the 14th International Workshop on Power and Timing Optimization and Simulation, PATMOS 2004, held in Santorini, Greece in September 2004. The 85 revised papers presented together with abstracts of 6 invited presentations were carefully reviewed and selected from 152 papers submitted. The papers are organized in topical sections on buses and communication, circuits and devices, low power issues, architectures, asynchronous circuits, systems design, interconnect and physical design, security and safety, low-power processing, digital design, and modeling and simulation.
This book describes a new type of computer aided VLSI design tool, called a VLSI System Planning, that is meant to aid designers dur ing the early, or conceptual, state of design. During this stage of design, the objective is to define a general design plan, or approach, that is likely to result in an efficient implementation satisfying the initial specifications, or to determine that the initial specifications are not realizable. A design plan is a collection of high level design decisions. As an example, the conceptual design of digital filters involves choosing the type of algorithm to implement (e. g. , finite impulse response or infinite impulse response), the type of polyno mial approximation (e. g. , Equiripple or Chebyshev), the fabrication technology (e. g. , CMOS or BiCMOS), and so on. Once a particu lar design plan is chosen, the detailed design phase can begin. It is during this phase that various synthesis, simulation, layout, and test activities occur to refine the conceptual design, gradually filling more detail until the design is finally realized. The principal advantage of VLSI System Planning is that the increasingly expensive resources of the detailed design process are more efficiently managed. Costly redesigns are minimized because the detailed design process is guided by a more credible, consistent, and correct design plan.
The explosive growth and development of the integrated circuit market over the last few years have been mostly limited to the digital VLSI domain. The difficulty of automating the design process in the analog domain, the fact that a general analog design methodology remained undefined, and the poor performance of earlier tools have left the analog
The International Workshop on Power and Timing Modeling, Optimization, and Simulation PATMOS 2002, was the 12th in a series of international workshops 1 previously held in several places in Europe. PATMOS has over the years evolved into a well-established and outstanding series of open European events on power and timing aspects of integrated circuit design. The increased interest, espe- ally in low-power design, has added further momentum to the interest in this workshop. Despite its growth, the workshop can still be considered as a very - cused conference, featuring high-level scienti?c presentations together with open discussions in a free and easy environment. This year, the workshop has been opened to both regular papers and poster presentations. The increasing number of worldwide high-quality submissions is a measure of the global interest of the international scienti?c community in the topics covered by PATMOS. The objective of this workshop is to provide a forum to discuss and inves- gate the emerging problems in the design methodologies and CAD-tools for the new generation of IC technologies. A major emphasis of the technical program is on speed and low-power aspects with particular regard to modeling, char- terization, design, and architectures. The technical program of PATMOS 2002 included nine sessions dedicated to most important and current topics on power and timing modeling, optimization, and simulation. The three invited talks try to give a global overview of the issues in low-power and/or high-performance circuit design.
Machine learning is a potential solution to resolve bottleneck issues in VLSI via optimizing tasks in the design process. This book aims to provide the latest machine-learning–based methods, algorithms, architectures, and frameworks designed for VLSI design. The focus is on digital, analog, and mixed-signal design techniques, device modeling, physical design, hardware implementation, testability, reconfigurable design, synthesis and verification, and related areas. Chapters include case studies as well as novel research ideas in the given field. Overall, the book provides practical implementations of VLSI design, IC design, and hardware realization using machine learning techniques. Features: Provides the details of state-of-the-art machine learning methods used in VLSI design Discusses hardware implementation and device modeling pertaining to machine learning algorithms Explores machine learning for various VLSI architectures and reconfigurable computing Illustrates the latest techniques for device size and feature optimization Highlights the latest case studies and reviews of the methods used for hardware implementation This book is aimed at researchers, professionals, and graduate students in VLSI, machine learning, electrical and electronic engineering, computer engineering, and hardware systems.
This book constitutes the refereed proceedings of the 4th International Workshop on Distributed Computing, IWDC 2002, held in Calcutta, India, in December 2002. The 31 revised full papers and 3 student papers presented together with 3 keynote papers were carefully reviewed and selected from more than 90 submissions. The papers are organized in topical sections on Web caching, distributed computing, wireless networks, wireless mobile systems, VLSI and parallel systems, optical networks, and distributed systems.