Vlsi Design Of Neural Networks

Author : Ulrich Ramacher,Ulrich Rückert
Publisher : Springer Science & Business Media
Page : 346 pages
File Size : 40,5 Mb
Release : 2012-12-06
Category : Technology & Engineering
ISBN : 9781461539940

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VLSI Design of Neural Networks by Ulrich Ramacher,Ulrich Rückert Pdf

The early era of neural network hardware design (starting at 1985) was mainly technology driven. Designers used almost exclusively analog signal processing concepts for the recall mode. Learning was deemed not to cause a problem because the number of implementable synapses was still so low that the determination of weights and thresholds could be left to conventional computers. Instead, designers tried to directly map neural parallelity into hardware. The architectural concepts were accordingly simple and produced the so called interconnection problem which, in turn, made many engineers believe it could be solved by optical implementation in adequate fashion only. Furthermore, the inherent fault-tolerance and limited computation accuracy of neural networks were claimed to justify that little effort is to be spend on careful design, but most effort be put on technology issues. As a result, it was almost impossible to predict whether an electronic neural network would function in the way it was simulated to do. This limited the use of the first neuro-chips for further experimentation, not to mention that real-world applications called for much more synapses than could be implemented on a single chip at that time. Meanwhile matters have matured. It is recognized that isolated definition of the effort of analog multiplication, for instance, would be just as inappropriate on the part ofthe chip designer as determination of the weights by simulation, without allowing for the computing accuracy that can be achieved, on the part of the user.

Author : Mohamed I. Elmasry
Publisher : Springer
Page : 360 pages
File Size : 48,6 Mb
Release : 1994-11-30
Category : Computers
ISBN : UOM:39015034023021

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VLSI Artificial Neural Networks Engineering by Mohamed I. Elmasry Pdf

Engineers have long been fascinated by how efficient and how fast biological neural networks are capable of performing such complex tasks as recognition. Such networks are capable of recognizing input data from any of the five senses with the necessary accuracy and speed to allow living creatures to survive. Machines which perform such complex tasks as recognition, with similar ac curacy and speed, were difficult to implement until the technological advances of VLSI circuits and systems in the late 1980's. Since then, the field of VLSI Artificial Neural Networks (ANNs) have witnessed an exponential growth and a new engineering discipline was born. Today, many engineering curriculums have included a course or more on the subject at the graduate or senior under graduate levels. Since the pioneering book by Carver Mead; "Analog VLSI and Neural Sys tems", Addison-Wesley, 1989; there were a number of excellent text and ref erence books on the subject, each dealing with one or two topics. This book attempts to present an integrated approach of a single research team to VLSI ANNs Engineering.

Author : Mohamed I. Elmasry
Publisher : Springer Science & Business Media
Page : 335 pages
File Size : 55,6 Mb
Release : 2012-12-06
Category : Technology & Engineering
ISBN : 9781461527664

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VLSI Artificial Neural Networks Engineering by Mohamed I. Elmasry Pdf

Engineers have long been fascinated by how efficient and how fast biological neural networks are capable of performing such complex tasks as recognition. Such networks are capable of recognizing input data from any of the five senses with the necessary accuracy and speed to allow living creatures to survive. Machines which perform such complex tasks as recognition, with similar ac curacy and speed, were difficult to implement until the technological advances of VLSI circuits and systems in the late 1980's. Since then, the field of VLSI Artificial Neural Networks (ANNs) have witnessed an exponential growth and a new engineering discipline was born. Today, many engineering curriculums have included a course or more on the subject at the graduate or senior under graduate levels. Since the pioneering book by Carver Mead; "Analog VLSI and Neural Sys tems", Addison-Wesley, 1989; there were a number of excellent text and ref erence books on the subject, each dealing with one or two topics. This book attempts to present an integrated approach of a single research team to VLSI ANNs Engineering.

Author : Jose G. Delgado-Frias,W.R. Moore
Publisher : Springer Science & Business Media
Page : 318 pages
File Size : 40,9 Mb
Release : 2013-06-29
Category : Computers
ISBN : 9781489913319

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VLSI for Neural Networks and Artificial Intelligence by Jose G. Delgado-Frias,W.R. Moore Pdf

Neural network and artificial intelligence algorithrns and computing have increased not only in complexity but also in the number of applications. This in turn has posed a tremendous need for a larger computational power that conventional scalar processors may not be able to deliver efficiently. These processors are oriented towards numeric and data manipulations. Due to the neurocomputing requirements (such as non-programming and learning) and the artificial intelligence requirements (such as symbolic manipulation and knowledge representation) a different set of constraints and demands are imposed on the computer architectures/organizations for these applications. Research and development of new computer architectures and VLSI circuits for neural networks and artificial intelligence have been increased in order to meet the new performance requirements. This book presents novel approaches and trends on VLSI implementations of machines for these applications. Papers have been drawn from a number of research communities; the subjects span analog and digital VLSI design, computer design, computer architectures, neurocomputing and artificial intelligence techniques. This book has been organized into four subject areas that cover the two major categories of this book; the areas are: analog circuits for neural networks, digital implementations of neural networks, neural networks on multiprocessor systems and applications, and VLSI machines for artificial intelligence. The topics that are covered in each area are briefly introduced below.

Author : Jose G. Delgado-Frias,W.R. Moore
Publisher : Springer Science & Business Media
Page : 411 pages
File Size : 48,9 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781461537526

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VLSI for Artificial Intelligence and Neural Networks by Jose G. Delgado-Frias,W.R. Moore Pdf

This book is an edited selection of the papers presented at the International Workshop on VLSI for Artifidal Intelligence and Neural Networks which was held at the University of Oxford in September 1990. Our thanks go to all the contributors and especially to the programme committee for all their hard work. Thanks are also due to the ACM-SIGARCH, the IEEE Computer Society, and the lEE for publicizing the event and to the University of Oxford and SUNY-Binghamton for their active support. We are particularly grateful to Anna Morris, Maureen Doherty and Laura Duffy for coping with the administrative problems. Jose Delgado-Frias Will Moore April 1991 vii PROLOGUE Artificial intelligence and neural network algorithms/computing have increased in complexity as well as in the number of applications. This in tum has posed a tremendous need for a larger computational power than can be provided by conventional scalar processors which are oriented towards numeric and data manipulations. Due to the artificial intelligence requirements (symbolic manipulation, knowledge representation, non-deterministic computations and dynamic resource allocation) and neural network computing approach (non-programming and learning), a different set of constraints and demands are imposed on the computer architectures for these applications.

Author : Bing J. Sheu,Joongho Choi
Publisher : Springer Science & Business Media
Page : 569 pages
File Size : 55,9 Mb
Release : 2012-12-06
Category : Technology & Engineering
ISBN : 9781461522478

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Neural Information Processing and VLSI by Bing J. Sheu,Joongho Choi Pdf

Neural Information Processing and VLSI provides a unified treatment of this important subject for use in classrooms, industry, and research laboratories, in order to develop advanced artificial and biologically-inspired neural networks using compact analog and digital VLSI parallel processing techniques. Neural Information Processing and VLSI systematically presents various neural network paradigms, computing architectures, and the associated electronic/optical implementations using efficient VLSI design methodologies. Conventional digital machines cannot perform computationally-intensive tasks with satisfactory performance in such areas as intelligent perception, including visual and auditory signal processing, recognition, understanding, and logical reasoning (where the human being and even a small living animal can do a superb job). Recent research advances in artificial and biological neural networks have established an important foundation for high-performance information processing with more efficient use of computing resources. The secret lies in the design optimization at various levels of computing and communication of intelligent machines. Each neural network system consists of massively paralleled and distributed signal processors with every processor performing very simple operations, thus consuming little power. Large computational capabilities of these systems in the range of some hundred giga to several tera operations per second are derived from collectively parallel processing and efficient data routing, through well-structured interconnection networks. Deep-submicron very large-scale integration (VLSI) technologies can integrate tens of millions of transistors in a single silicon chip for complex signal processing and information manipulation. The book is suitable for those interested in efficient neurocomputing as well as those curious about neural network system applications. It has been especially prepared for use as a text for advanced undergraduate and first year graduate students, and is an excellent reference book for researchers and scientists working in the fields covered.

Author : M. Jabri,R.J. Coggins,B.G. Flower
Publisher : Springer Science & Business Media
Page : 262 pages
File Size : 50,6 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9789401105255

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Adaptive Analog VLSI Neural Systems by M. Jabri,R.J. Coggins,B.G. Flower Pdf

Adaptive Analog VLSI Neural Systems is the first practical book on neural networks learning chips and systems. It covers the entire process of implementing neural networks in VLSI chips, beginning with the crucial issues of learning algorithms in an analog framework and limited precision effects, and giving actual case studies of working systems. The approach is systems and applications oriented throughout, demonstrating the attractiveness of such an approach for applications such as adaptive pattern recognition and optical character recognition. Dr Jabri and his co-authors from AT&T Bell Laboratories, Bellcore and the University of Sydney provide a comprehensive introduction to VLSI neural networks suitable for research and development staff and advanced students.

Author : Tamás Roska,Ángel Rodríguez-Vázquez
Publisher : John Wiley & Sons
Page : 438 pages
File Size : 45,6 Mb
Release : 2001-01-17
Category : Computers
ISBN : UOM:39015050503690

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Towards the Visual Microprocessor by Tamás Roska,Ángel Rodríguez-Vázquez Pdf

Written by a group of leading researchers in the field, this is a pioneering work, providing a concise analysis of the topic by the inventors of the CNN universal machine and the supercomputer chip. Opening with a foreword by the respected academic, Professor Leon Chua, the book progresses to explore circuit design, prototyping and analogical algorithms. Subjects covered include the VLSI design and implementation of CNNs, the testing of CNN chips and a detailed analysis of the new system for prototyping and interfacing the CNN universal chips ? Includes applications in: Neurocomputing, Machine Vision, Image Processing and VLSI Signal Processing ? Provides simple algorithms to design and synthesise complex circuits ? Written and edited by world authorities in this field, including Leon Chua who invented CNNs in the late 1980s. This text follows on from Roska's previous success - Cellular Neural Networks and D3 - with this groundbreaking work about a rapidly developing and increasingly influential field of circuit theory. This text would be of great interest to a broad audience including postgraduate and advanced students, researchers and professionals in electrical and electronic engineering, computer science, mathematics and neurobiology.

Author : George Chamberlain,Electronics Carleton University. Dissertation. Engineering,ProQuest Co
Publisher : Unknown
Page : 200 pages
File Size : 46,7 Mb
Release : 1994
Category : Electronic
ISBN : OCLC:290443189

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Training and Design of VLSI Neural Networks by George Chamberlain,Electronics Carleton University. Dissertation. Engineering,ProQuest Co Pdf

Author : Sandeep Saini,Kusum Lata,G.R. Sinha
Publisher : CRC Press
Page : 292 pages
File Size : 50,8 Mb
Release : 2021-12-31
Category : Technology & Engineering
ISBN : 9781000523843

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VLSI and Hardware Implementations using Modern Machine Learning Methods by Sandeep Saini,Kusum Lata,G.R. Sinha Pdf

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.

Author : Sankar K. Pal,David Zhang
Publisher : World Scientific
Page : 421 pages
File Size : 53,6 Mb
Release : 2002
Category : Computers
ISBN : 9789812778086

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Neural Networks and Systolic Array Design by Sankar K. Pal,David Zhang Pdf

Neural networks (NNs) and systolic arrays (SAs) have many similar features. This volume describes, in a unified way, the basic concepts, theories and characteristic features of integrating or formulating different facets of NNs and SAs, as well as presents recent developments and significant applications. The articles, written by experts from all over the world, demonstrate the various ways this integration can be made to efficiently design methodologies, algorithms and architectures, and also implementations, for NN applications. The book will be useful to graduate students and researchers in many related areas, not only as a reference book but also as a textbook for some parts of the curriculum. It will also benefit researchers and practitioners in industry and R&D laboratories who are working in the fields of system design, VLSI, parallel processing, neural networks, and vision.

Author : Sied Mehdi Fakhraie,Kenneth C Smith
Publisher : Unknown
Page : 228 pages
File Size : 50,5 Mb
Release : 1996-12-01
Category : Electronic
ISBN : 1461563127

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VLSI - Compatible Implementations for Artificial Neural Networks by Sied Mehdi Fakhraie,Kenneth C Smith Pdf

Author : Carver Mead
Publisher : Addison Wesley Publishing Company
Page : 416 pages
File Size : 55,8 Mb
Release : 1989
Category : Analog computers
ISBN : UOM:49015000947821

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Analog VLSI and Neural Systems by Carver Mead Pdf

A self-contained text, suitable for a broad audience. Presents basic concepts in electronics, transistor physics, and neurobiology for readers without backgrounds in those areas. Annotation copyrighted by Book News, Inc., Portland, OR

Author : Ibrahim (Abe) M. Elfadel,Duane S. Boning,Xin Li
Publisher : Springer
Page : 694 pages
File Size : 51,7 Mb
Release : 2019-03-15
Category : Technology & Engineering
ISBN : 9783030046668

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Machine Learning in VLSI Computer-Aided Design by Ibrahim (Abe) M. Elfadel,Duane S. Boning,Xin Li Pdf

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

Author : Gabriele Manganaro,Paolo Arena,Luigi Fortuna
Publisher : Springer Science & Business Media
Page : 280 pages
File Size : 45,5 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9783642600449

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Cellular Neural Networks by Gabriele Manganaro,Paolo Arena,Luigi Fortuna Pdf

The field of cellular neural networks (CNNs) is of growing importance in non linear circuits and systems and it is maturing to the point of becoming a new area of study in general nonlinear theory. CNNs emerged through two semi nal papers co-authored by Professor Leon O. Chua back in 1988. Since then, the attention that CNNs have attracted in the scientific community has been vast. For instance, there are international workshops dedicated to CNNs and their applications, special issues published in both the International Journal of Circuit Theory and in the IEEE Transactions on Circuits and Systems, and there are also Associate Editors appointed in the latter journal especially for the CNN field. All of this bears witness the importance that CNNs are gaining within the scientific community. Without doubt this book is a primer in the field. Its extensive coverage provides the reader with a very comprehensive view of aspects involved in the theory and applications of cellular neural networks. The authors have done an excellent job merging basic CNN theory, synchronization, spatio temporal phenomena and hardware implementation into eight exquisitely written chapters. Each chapter is thoroughly illustrated with examples and case studies. The result is a book that is not only excellent as a professional reference but also very appealing as a textbook. My view is that students as well professional engineers will find this volume extremely useful.