Neural Networks And Analog Computation

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Neural Networks and Analog Computation

Author : Hava T. Siegelmann
Publisher : Springer Science & Business Media
ISBN 13 : 146120707X
Total Pages : 193 pages
Book Rating : 4.4/5 (612 download)

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Book Synopsis Neural Networks and Analog Computation by : Hava T. Siegelmann

Download or read book Neural Networks and Analog Computation written by Hava T. Siegelmann and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 193 pages. Available in PDF, EPUB and Kindle. Book excerpt: The theoretical foundations of Neural Networks and Analog Computation conceptualize neural networks as a particular type of computer consisting of multiple assemblies of basic processors interconnected in an intricate structure. Examining these networks under various resource constraints reveals a continuum of computational devices, several of which coincide with well-known classical models. On a mathematical level, the treatment of neural computations enriches the theory of computation but also explicated the computational complexity associated with biological networks, adaptive engineering tools, and related models from the fields of control theory and nonlinear dynamics. The material in this book will be of interest to researchers in a variety of engineering and applied sciences disciplines. In addition, the work may provide the base of a graduate-level seminar in neural networks for computer science students.

Analog Computing

Author : Bernd Ulmann
Publisher : Walter de Gruyter GmbH & Co KG
ISBN 13 : 3110787741
Total Pages : 460 pages
Book Rating : 4.1/5 (17 download)

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Book Synopsis Analog Computing by : Bernd Ulmann

Download or read book Analog Computing written by Bernd Ulmann and published by Walter de Gruyter GmbH & Co KG. This book was released on 2022-11-07 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analog computing is one of the main pillars of Unconventional Computing. Almost forgotten for decades, we now see an ever-increasing interest in electronic analog computing because it offers a path to high-performance and highly energy-efficient computing. These characteristics are of great importance in a world where vast amounts of electric energy are consumed by today’s computer systems. Analog computing can deliver efficient solutions to many computing problems, ranging from general purpose analog computation to specialised systems like analog artificial neural networks. The book “Analog Computing” has established itself over the past decade as the standard textbook on the subject and has been substantially extended in this second edition, which includes more than 300 additional bibliographical entries, and has been expanded in many areas to include much greater detail. These enhancements will confirm this book’s status as the leading work in the field. It covers the history of analog computing from the Antikythera Mechanism to recent electronic analog computers and uses a wide variety of worked examples to provide a comprehensive introduction to programming analog computers. It also describes hybrid computers, digital differential analysers, the simulation of analog computers, stochastic computers, and provides a comprehensive treatment of classic and current analog computer applications. The last chapter looks into the promising future of analog computing.

Analog VLSI and Neural Systems

Author : Carver Mead
Publisher : Addison Wesley Publishing Company
ISBN 13 :
Total Pages : 416 pages
Book Rating : 4.4/5 (91 download)

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Book Synopsis Analog VLSI and Neural Systems by : Carver Mead

Download or read book Analog VLSI and Neural Systems written by Carver Mead and published by Addison Wesley Publishing Company. This book was released on 1989 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Neural Networks: Computational Models and Applications

Author : Huajin Tang
Publisher : Springer Science & Business Media
ISBN 13 : 3540692258
Total Pages : 310 pages
Book Rating : 4.5/5 (46 download)

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Book Synopsis Neural Networks: Computational Models and Applications by : Huajin Tang

Download or read book Neural Networks: Computational Models and Applications written by Huajin Tang and published by Springer Science & Business Media. This book was released on 2007-03-12 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural Networks: Computational Models and Applications presents important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence: pattern recognition, uniform approximation, constrained optimization, NP-hard problems, and image segmentation. The book offers a compact, insightful understanding of the broad and rapidly growing neural networks domain.

Neural Networks

Author : S?ren Brunak
Publisher : World Scientific
ISBN 13 : 9789971509385
Total Pages : 200 pages
Book Rating : 4.5/5 (93 download)

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Book Synopsis Neural Networks by : S?ren Brunak

Download or read book Neural Networks written by S?ren Brunak and published by World Scientific. This book was released on 1990 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt: Both specialists and laymen will enjoy reading this book. Using a lively, non-technical style and images from everyday life, the authors present the basic principles behind computing and computers. The focus is on those aspects of computation that concern networks of numerous small computational units, whether biological neural networks or artificial electronic devices.

VLSI Design of Neural Networks

Author : Ulrich Ramacher
Publisher : Springer Science & Business Media
ISBN 13 : 1461539943
Total Pages : 346 pages
Book Rating : 4.4/5 (615 download)

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Book Synopsis VLSI Design of Neural Networks by : Ulrich Ramacher

Download or read book VLSI Design of Neural Networks written by Ulrich Ramacher and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Encyclopedia of Complexity and Systems Science

Author :
Publisher : Springer
ISBN 13 : 9780387758886
Total Pages : 10398 pages
Book Rating : 4.7/5 (588 download)

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Book Synopsis Encyclopedia of Complexity and Systems Science by :

Download or read book Encyclopedia of Complexity and Systems Science written by and published by Springer. This book was released on 2009-06-26 with total page 10398 pages. Available in PDF, EPUB and Kindle. Book excerpt: This encyclopedia provides an authoritative single source for understanding and applying the concepts of complexity theory together with the tools and measures for analyzing complex systems in all fields of science and engineering. It links fundamental concepts of mathematics and computational sciences to applications in the physical sciences, engineering, biomedicine, economics and the social sciences.

Analog VLSI Neural Networks

Author : Yoshiyasu Takefuji
Publisher : Springer Science & Business Media
ISBN 13 : 1461535824
Total Pages : 132 pages
Book Rating : 4.4/5 (615 download)

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Book Synopsis Analog VLSI Neural Networks by : Yoshiyasu Takefuji

Download or read book Analog VLSI Neural Networks written by Yoshiyasu Takefuji and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Efficient Processing of Deep Neural Networks

Author : Vivienne Sze
Publisher : Springer Nature
ISBN 13 : 3031017668
Total Pages : 254 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Efficient Processing of Deep Neural Networks by : Vivienne Sze

Download or read book Efficient Processing of Deep Neural Networks written by Vivienne Sze and published by Springer Nature. This book was released on 2022-05-31 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.

Analog VLSI Implementation of Neural Systems

Author : Carver Mead
Publisher : Springer Science & Business Media
ISBN 13 : 1461316391
Total Pages : 250 pages
Book Rating : 4.4/5 (613 download)

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Book Synopsis Analog VLSI Implementation of Neural Systems by : Carver Mead

Download or read book Analog VLSI Implementation of Neural Systems written by Carver Mead and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Analog Computation, Artificial Neural Networks and Convex Quadratic Optimization

Author : Sushil Kumar Verma
Publisher :
ISBN 13 :
Total Pages : 212 pages
Book Rating : 4.:/5 (1 download)

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Book Synopsis Analog Computation, Artificial Neural Networks and Convex Quadratic Optimization by : Sushil Kumar Verma

Download or read book Analog Computation, Artificial Neural Networks and Convex Quadratic Optimization written by Sushil Kumar Verma and published by . This book was released on 1994 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Analog and Hybrid Computer Programming

Author : Bernd Ulmann
Publisher : Walter de Gruyter GmbH & Co KG
ISBN 13 : 3110787881
Total Pages : 441 pages
Book Rating : 4.1/5 (17 download)

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Book Synopsis Analog and Hybrid Computer Programming by : Bernd Ulmann

Download or read book Analog and Hybrid Computer Programming written by Bernd Ulmann and published by Walter de Gruyter GmbH & Co KG. This book was released on 2023-05-22 with total page 441 pages. Available in PDF, EPUB and Kindle. Book excerpt: As classic digital computers are about to reach their physical and architectural boundaries, interest in unconventional approaches to computing, such as quantum and analog computers, is rapidly increasing. For a wide variety of practical applications, analog computers can outperform classic digital computers in terms of both raw computational speed and energy efficiency. This makes them ideally suited a co-processors to digital computers, thus forming hybrid computers. This second edition of "Analog and Hybrid Computer Programming" provides a thorough introduction to the programming of analog and hybrid computers. It contains a wealth of practical examples, ranging from simple problems such as radioactive decay, harmonic oscillators, and chemical reaction kinetics to advanced topics which include the simulation of neurons, chaotic systems such as a double-pendulum simulation and many more. In addition to these examples, it contains a chapter on special functions which can be used as "subroutines" in an analog computer setup.

Handbook of Neural Computing Applications

Author : Alianna J. Maren
Publisher : Academic Press
ISBN 13 : 148326484X
Total Pages : 472 pages
Book Rating : 4.4/5 (832 download)

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Book Synopsis Handbook of Neural Computing Applications by : Alianna J. Maren

Download or read book Handbook of Neural Computing Applications written by Alianna J. Maren and published by Academic Press. This book was released on 2014-05-10 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: Handbook of Neural Computing Applications is a collection of articles that deals with neural networks. Some papers review the biology of neural networks, their type and function (structure, dynamics, and learning) and compare a back-propagating perceptron with a Boltzmann machine, or a Hopfield network with a Brain-State-in-a-Box network. Other papers deal with specific neural network types, and also on selecting, configuring, and implementing neural networks. Other papers address specific applications including neurocontrol for the benefit of control engineers and for neural networks researchers. Other applications involve signal processing, spatio-temporal pattern recognition, medical diagnoses, fault diagnoses, robotics, business, data communications, data compression, and adaptive man-machine systems. One paper describes data compression and dimensionality reduction methods that have characteristics, such as high compression ratios to facilitate data storage, strong discrimination of novel data from baseline, rapid operation for software and hardware, as well as the ability to recognized loss of data during compression or reconstruction. The collection can prove helpful for programmers, computer engineers, computer technicians, and computer instructors dealing with many aspects of computers related to programming, hardware interface, networking, engineering or design.

Non-Linear Feedback Neural Networks

Author : Mohd. Samar Ansari
Publisher : Springer
ISBN 13 : 813221563X
Total Pages : 217 pages
Book Rating : 4.1/5 (322 download)

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Book Synopsis Non-Linear Feedback Neural Networks by : Mohd. Samar Ansari

Download or read book Non-Linear Feedback Neural Networks written by Mohd. Samar Ansari and published by Springer. This book was released on 2013-09-03 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

VLSI for Artificial Intelligence and Neural Networks

Author : Jose G. Delgado-Frias
Publisher : Springer Science & Business Media
ISBN 13 : 1461537525
Total Pages : 411 pages
Book Rating : 4.4/5 (615 download)

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Book Synopsis VLSI for Artificial Intelligence and Neural Networks by : Jose G. Delgado-Frias

Download or read book VLSI for Artificial Intelligence and Neural Networks written by Jose G. Delgado-Frias and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 411 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

A Fully Analog Computing-in-Memory Accelerator for Neural Network

Author :
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (141 download)

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Book Synopsis A Fully Analog Computing-in-Memory Accelerator for Neural Network by :

Download or read book A Fully Analog Computing-in-Memory Accelerator for Neural Network written by and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Using Artificial Neural Networks for Analog Integrated Circuit Design Automation

Author : João P. S. Rosa
Publisher : Springer Nature
ISBN 13 : 3030357430
Total Pages : 117 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Using Artificial Neural Networks for Analog Integrated Circuit Design Automation by : João P. S. Rosa

Download or read book Using Artificial Neural Networks for Analog Integrated Circuit Design Automation written by João P. S. Rosa and published by Springer Nature. This book was released on 2019-12-11 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses the automatic sizing and layout of analog integrated circuits (ICs) using deep learning (DL) and artificial neural networks (ANN). It explores an innovative approach to automatic circuit sizing where ANNs learn patterns from previously optimized design solutions. In opposition to classical optimization-based sizing strategies, where computational intelligence techniques are used to iterate over the map from devices’ sizes to circuits’ performances provided by design equations or circuit simulations, ANNs are shown to be capable of solving analog IC sizing as a direct map from specifications to the devices’ sizes. Two separate ANN architectures are proposed: a Regression-only model and a Classification and Regression model. The goal of the Regression-only model is to learn design patterns from the studied circuits, using circuit’s performances as input features and devices’ sizes as target outputs. This model can size a circuit given its specifications for a single topology. The Classification and Regression model has the same capabilities of the previous model, but it can also select the most appropriate circuit topology and its respective sizing given the target specification. The proposed methodology was implemented and tested on two analog circuit topologies.