Download Full eBooks in PDF
Vlsi Compatible Implementations For Artificial Neural Networks Microform Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Vlsi Compatible Implementations For Artificial Neural Networks Microform book. This book definitely worth reading, it is an incredibly well-written.
Author : Sied Mehdi Fakhraie
Publisher : National Library of Canada = Bibliothèque nationale du Canada
Page : 372 pages
File Size : 44,6 Mb
Release : 1995
Category : Electronic
ISBN : 0612027538
Author : Sied Mehdi Fakhraie,Kenneth C Smith
Publisher : Unknown
Page : 228 pages
File Size : 45,8 Mb
Release : 1996-12-01
Category : Electronic
ISBN : 1461563127
Author : Jose G. Delgado-Frias,W.R. Moore
Publisher : Springer Science & Business Media
Page : 318 pages
File Size : 46,8 Mb
Release : 2013-06-29
Category : Computers
ISBN : 9781489913319
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 : Mohamed I. Elmasry
Publisher : Springer Science & Business Media
Page : 335 pages
File Size : 50,6 Mb
Release : 2012-12-06
Category : Technology & Engineering
ISBN : 9781461527664
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 : 411 pages
File Size : 44,5 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781461537526
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 : Ulrich Ramacher,Ulrich Rückert
Publisher : Springer
Page : 343 pages
File Size : 53,7 Mb
Release : 2012-02-05
Category : Technology & Engineering
ISBN : 1461539951
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 : Alan F. Murray,Lionel Tarassenko
Publisher : Unknown
Page : 176 pages
File Size : 51,7 Mb
Release : 1994
Category : Computers
ISBN : UOM:39015032763529
Author : Mohd. Samar Ansari
Publisher : Springer
Page : 217 pages
File Size : 43,5 Mb
Release : 2013-09-03
Category : Technology & Engineering
ISBN : 9788132215639
This book aims to present a viable alternative to the Hopfield Neural Network (HNN) model for analog computation. It is well known the standard HNN suffers from problems of convergence to local minima, and requirement of a large number of neurons and synaptic weights. Therefore, improved solutions are needed. The non-linear synapse neural network (NoSyNN) is one such possibility and is discussed in detail in this book. This book also discusses the applications in computationally intensive tasks like graph coloring, ranking, and linear as well as quadratic programming. The material in the book is useful to students, researchers and academician working in the area of analog computation.
Author : K. Wojtek Przytula,V. K. Prasanna Kumar
Publisher : Prentice Hall
Page : 346 pages
File Size : 55,5 Mb
Release : 1993
Category : Computers
ISBN : UOM:39015032742614
Explores issues related to implementing artificial neural networks on programmable, massively parallel computers, and special purpose digital, programmable VLSI architectures. The nine contributions cover mapping methodologies and implementations, digital neurocomputers, and architectural building blocks. Annotation copyright by Book News, Inc., Portland, OR
Author : Yoshiyasu Takefuji
Publisher : Springer
Page : 148 pages
File Size : 41,5 Mb
Release : 1993
Category : Computers
ISBN : UOM:39015029950071
This book brings together in one place important contributions and state-of-the-art research in the rapidly advancing area of analog VLSI neural networks. The book serves as an excellent reference, providing insights into some of the most important issues in analog VLSI neural networks research efforts.
Author : Nelson Morgan
Publisher : Institute of Electrical & Electronics Engineers(IEEE)
Page : 152 pages
File Size : 45,8 Mb
Release : 1990
Category : Computers
ISBN : UOM:39015019859894
Author : Anonim
Publisher : Unknown
Page : 346 pages
File Size : 50,8 Mb
Release : 1991
Category : Neural networks (Computer science)
ISBN : UOM:39015021495901
The basic idea behind this series of books is to combine expertise and experience of contributing authors from a number of different scientific disciplines.
Author : Hendrik Carolus Arthur Maria Withagen
Publisher : Unknown
Page : 147 pages
File Size : 40,8 Mb
Release : 1997
Category : Electronic
ISBN : 9038603509
Author : Anonim
Publisher : Unknown
Page : 1216 pages
File Size : 49,5 Mb
Release : 1992
Category : Physics
ISBN : UOM:39015026186844
Author : Tamás Roska,J. Vandewalle
Publisher : Wiley
Page : 236 pages
File Size : 40,7 Mb
Release : 1994-01-25
Category : Computers
ISBN : 047193836X
Cellular Neural Networks Edited by T. Roska, Computer & Automation Institute, Hungarian Academy of Sciences J. Vandewalle, Katholieke Universiteit Leuven, Belgium Written by a team of well-known researchers, this unique book and software package covers all the recent developments in, and applications of, cellular neural networks. The text is divided into four sections, detailing the theory, VLSI realization, design tools and applications of CNNs. Features include: The design of high-speed, high-density CNNs in CMOS technology. Testability in analogue cellular neural networks. The practical applications of CNNs including the detection of moving and stationary objects, and the use of cellular neural networks for solving specific image-processing problems. A versatile CNN software simulator accompanies the book. Designed for standard PCs under MS DOS, this user-friendly program allows users to visualize and test their own ideas. The 3.5" disk and user’s manual form an essential part of this package. The wide-ranging applications of cellular neural networks and the ease of VLSI implementation have ensured that CNNs are now a major new research area. This comprehensive package will provide valuable guidance for advanced students and researchers in electrical and electronic engineering and computer science. Neurobilogists and mathematicians will also find the package appealing.