Neural Networks And Analog Computation
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Author |
: Hava T. Siegelmann |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 193 |
Release |
: 2012-12-06 |
ISBN-10 |
: 9781461207078 |
ISBN-13 |
: 146120707X |
Rating |
: 4/5 (78 Downloads) |
Synopsis Neural Networks and Analog Computation by : Hava T. Siegelmann
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.
Author |
: Hava Siegelmann |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 208 |
Release |
: 1998-12-01 |
ISBN-10 |
: 0817639497 |
ISBN-13 |
: 9780817639495 |
Rating |
: 4/5 (97 Downloads) |
Synopsis Neural Networks and Analog Computation by : Hava Siegelmann
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.
Author |
: Bernd Ulmann |
Publisher |
: Walter de Gruyter GmbH & Co KG |
Total Pages |
: 460 |
Release |
: 2022-11-07 |
ISBN-10 |
: 9783110787740 |
ISBN-13 |
: 3110787741 |
Rating |
: 4/5 (40 Downloads) |
Synopsis Analog Computing by : Bernd Ulmann
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.
Author |
: Carver Mead |
Publisher |
: Addison Wesley Publishing Company |
Total Pages |
: 416 |
Release |
: 1989 |
ISBN-10 |
: UOM:49015000947821 |
ISBN-13 |
: |
Rating |
: 4/5 (21 Downloads) |
Synopsis Analog VLSI and Neural Systems by : Carver Mead
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 |
: Huajin Tang |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 310 |
Release |
: 2007-03-12 |
ISBN-10 |
: 9783540692256 |
ISBN-13 |
: 3540692258 |
Rating |
: 4/5 (56 Downloads) |
Synopsis Neural Networks: Computational Models and Applications by : Huajin Tang
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.
Author |
: |
Publisher |
: Springer |
Total Pages |
: 10398 |
Release |
: 2009-06-26 |
ISBN-10 |
: 0387758887 |
ISBN-13 |
: 9780387758886 |
Rating |
: 4/5 (87 Downloads) |
Synopsis Encyclopedia of Complexity and Systems Science by :
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.
Author |
: Ulrich Ramacher |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 346 |
Release |
: 2012-12-06 |
ISBN-10 |
: 9781461539940 |
ISBN-13 |
: 1461539943 |
Rating |
: 4/5 (40 Downloads) |
Synopsis VLSI Design of Neural Networks by : Ulrich Ramacher
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 |
: Yoshiyasu Takefuji |
Publisher |
: Springer Science & Business Media |
Total Pages |
: 132 |
Release |
: 2012-12-06 |
ISBN-10 |
: 9781461535829 |
ISBN-13 |
: 1461535824 |
Rating |
: 4/5 (29 Downloads) |
Synopsis Analog VLSI Neural Networks by : Yoshiyasu Takefuji
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 |
: Vivienne Sze |
Publisher |
: Springer Nature |
Total Pages |
: 254 |
Release |
: 2022-05-31 |
ISBN-10 |
: 9783031017667 |
ISBN-13 |
: 3031017668 |
Rating |
: 4/5 (67 Downloads) |
Synopsis Efficient Processing of Deep Neural Networks by : Vivienne Sze
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.
Author |
: S?ren Brunak |
Publisher |
: World Scientific |
Total Pages |
: 200 |
Release |
: 1990 |
ISBN-10 |
: 9971509385 |
ISBN-13 |
: 9789971509385 |
Rating |
: 4/5 (85 Downloads) |
Synopsis Neural Networks by : S?ren Brunak
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.