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Author: M. Jabri
Publisher: Springer Science & Business Media
ISBN: 9401105251
Category : Computers
Languages : en
Pages : 262
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Book Description
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: M. Jabri
Publisher: Springer Science & Business Media
ISBN: 9401105251
Category : Computers
Languages : en
Pages : 262
Get Book
Book Description
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: Carver Mead
Publisher: Springer Science & Business Media
ISBN: 1461316391
Category : Technology & Engineering
Languages : en
Pages : 250
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Book Description
This volume contains the proceedings of a workshop on Analog Integrated Neural Systems held May 8, 1989, in connection with the International Symposium on Circuits and Systems. The presentations were chosen to encompass the entire range of topics currently under study in this exciting new discipline. Stringent acceptance requirements were placed on contributions: (1) each description was required to include detailed characterization of a working chip, and (2) each design was not to have been published previously. In several cases, the status of the project was not known until a few weeks before the meeting date. As a result, some of the most recent innovative work in the field was presented. Because this discipline is evolving rapidly, each project is very much a work in progress. Authors were asked to devote considerable attention to the shortcomings of their designs, as well as to the notable successes they achieved. In this way, other workers can now avoid stumbling into the same traps, and evolution can proceed more rapidly (and less painfully). The chapters in this volume are presented in the same order as the corresponding presentations at the workshop. The first two chapters are concerned with fmding solutions to complex optimization problems under a predefmed set of constraints. The first chapter reports what is, to the best of our knowledge, the first neural-chip design. In each case, the physics of the underlying electronic medium is used to represent a cost function in a natural way, using only nearest-neighbor connectivity.
Author: G. Cauwenberghs
Publisher: Springer Science & Business Media
ISBN: 9780792385554
Category : Technology & Engineering
Languages : en
Pages : 444
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Book Description
Learning on Silicon combines models of adaptive information processing in the brain with advances in microelectronics technology and circuit design. The premise is to construct integrated systems not only loaded with sufficient computational power to handle demanding signal processing tasks in sensory perception and pattern recognition, but also capable of operating autonomously and robustly in unpredictable environments through mechanisms of adaptation and learning. This edited volume covers the spectrum of Learning on Silicon in five parts: adaptive sensory systems, neuromorphic learning, learning architectures, learning dynamics, and learning systems. The 18 chapters are documented with examples of fabricated systems, experimental results from silicon, and integrated applications ranging from adaptive optics to biomedical instrumentation. As the first comprehensive treatment on the subject, Learning on Silicon serves as a reference for beginners and experienced researchers alike. It provides excellent material for an advanced course, and a source of inspiration for continued research towards building intelligent adaptive machines.
Author: Yoshiyasu Takefuji
Publisher: Springer Science & Business Media
ISBN: 1461535824
Category : Technology & Engineering
Languages : en
Pages : 131
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Book Description
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: Carver Mead
Publisher:
ISBN:
Category : Integrated circuits
Languages : en
Pages : 371
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Book Description
Author: Mohamed I. Elmasry
Publisher: Springer Science & Business Media
ISBN: 146152766X
Category : Technology & Engineering
Languages : en
Pages : 335
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Book Description
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 R. Dorronsoro
Publisher: Springer
ISBN: 3540460845
Category : Computers
Languages : en
Pages : 1384
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Book Description
The International Conferences on Arti?cial Neural Networks, ICANN, have been held annually since 1991 and over the years have become the major European meeting in neural networks. This proceedings volume contains all the papers presented at ICANN 2002, the 12th ICANN conference, held in August 28– 30, 2002 at the Escuela T ́ecnica Superior de Inform ́atica of the Universidad Aut ́onoma de Madrid and organized by its Neural Networks group. ICANN 2002 received a very high number of contributions, more than 450. Almost all papers were revised by three independent reviewers, selected among the more than 240 serving at this year’s ICANN, and 221 papers were ?nally selected for publication in these proceedings (due to space considerations, quite a few good contributions had to be left out). I would like to thank the Program Committee and all the reviewers for the great collective e?ort and for helping us to have a high quality conference.
Author: Ulrich Ramacher
Publisher: Springer Science & Business Media
ISBN: 1461539943
Category : Technology & Engineering
Languages : en
Pages : 346
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Book Description
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: Tor Sverre Lande
Publisher: Springer
ISBN: 0585280010
Category : Technology & Engineering
Languages : en
Pages : 462
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Book Description
Neuromorphic Systems Engineering: Neural Networks in Silicon emphasizes three important aspects of this exciting new research field. The term neuromorphic expresses relations to computational models found in biological neural systems, which are used as inspiration for building large electronic systems in silicon. By adequate engineering, these silicon systems are made useful to mankind. Neuromorphic Systems Engineering: Neural Networks in Silicon provides the reader with a snapshot of neuromorphic engineering today. It is organized into five parts viewing state-of-the-art developments within neuromorphic engineering from different perspectives. Neuromorphic Systems Engineering: Neural Networks in Silicon provides the first collection of neuromorphic systems descriptions with firm foundations in silicon. Topics presented include: large scale analog systems in silicon neuromorphic silicon auditory (ear) and vision (eye) systems in silicon learning and adaptation in silicon merging biology and technology micropower analog circuit design analog memory analog interchipcommunication on digital buses £/LIST£ Neuromorphic Systems Engineering: Neural Networks in Silicon serves as an excellent resource for scientists, researchers and engineers in this emerging field, and may also be used as a text for advanced courses on the subject.
Author: Maurizio Valle
Publisher: Springer Science & Business Media
ISBN: 1402027826
Category : Science
Languages : en
Pages : 309
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Book Description
Intelligent/smart systems have become common practice in many engineering applications. On the other hand, current low cost standard CMOS technology (and future foreseeable developments) makes available enormous potentialities. The next breakthrough will be the design and development of "smart adaptive systems on silicon" i.e. very power and highly size efficient complete systems (i.e. sensing, computing and "actuating" actions) with intelligence on board on a single silicon die. Smart adaptive systems on silicon will be able to "adapt" autonomously to the changing environment and will be able to implement "intelligent" behaviour and both perceptual and cognitive tasks. At last, they will communicate through wireless channels, they will be battery supplied or remote powered (via inductive coupling) and they will be ubiquitous in our every day life. Although many books deal with research and engineering topics (i.e. algorithms, technology, implementations, etc.) few of them try to bridge the gap between them and to address the issues related to feasibility, reliability and applications. Smart Adaptive Systems on Silicon, though not exhaustive, tries to fill this gap and to give answers mainly to the feasibility and reliability issues. Smart Adaptive Systems on Silicon mainly focuses on the analog and mixed mode implementation on silicon because this approach is amenable of achieving impressive energy and size efficiency. Moreover, analog systems can be more easily interfaced with sensing and actuating devices.
