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Author : Paul Taylor Wildes
Publisher :
ISBN 13 :
Total Pages : 112 pages
Book Rating : 4.:/5 (89 download)
Download or read book Electronic Implementations of Neural Networks written by Paul Taylor Wildes and published by . This book was released on 1988 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Sun Yuan Kung
Publisher : Prentice Hall
ISBN 13 :
Total Pages : 472 pages
Book Rating : 4.3/5 (91 download)
Download or read book Digital Neural Networks written by Sun Yuan Kung and published by Prentice Hall. This book was released on 1993 with total page 472 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intended for engineers and researchers interested in the applications of neural networks to signal and image processing, this book is theoretically based with emphasis on application and implementation. Coverage includes neural networks for representation, unsupervised networks for association/classification, neural networks for generalization/restoration, neural net and conventional optimization techniques, and special purpose supercomputers for neural nets.
Author : Amos R. Omondi
Publisher : Springer Science & Business Media
ISBN 13 : 0387284877
Total Pages : 365 pages
Book Rating : 4.3/5 (872 download)
Download or read book FPGA Implementations of Neural Networks written by Amos R. Omondi and published by Springer Science & Business Media. This book was released on 2006-10-04 with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the 1980s and early 1990s there was signi?cant work in the design and implementation of hardware neurocomputers. Nevertheless, most of these efforts may be judged to have been unsuccessful: at no time have have ha- ware neurocomputers been in wide use. This lack of success may be largely attributed to the fact that earlier work was almost entirely aimed at developing custom neurocomputers, based on ASIC technology, but for such niche - eas this technology was never suf?ciently developed or competitive enough to justify large-scale adoption. On the other hand, gate-arrays of the period m- tioned were never large enough nor fast enough for serious arti?cial-neur- network (ANN) applications. But technology has now improved: the capacity and performance of current FPGAs are such that they present a much more realistic alternative. Consequently neurocomputers based on FPGAs are now a much more practical proposition than they have been in the past. This book summarizes some work towards this goal and consists of 12 papers that were selected, after review, from a number of submissions. The book is nominally divided into three parts: Chapters 1 through 4 deal with foundational issues; Chapters 5 through 11 deal with a variety of implementations; and Chapter 12 looks at the lessons learned from a large-scale project and also reconsiders design issues in light of current and future technology.
Author : Nelson Morgan
Publisher : Institute of Electrical & Electronics Engineers(IEEE)
ISBN 13 :
Total Pages : 152 pages
Book Rating : 4.3/5 (91 download)
Download or read book Artificial Neural Networks written by Nelson Morgan and published by Institute of Electrical & Electronics Engineers(IEEE). This book was released on 1990 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Bhupendra S. Bhakta
Publisher :
ISBN 13 :
Total Pages : 202 pages
Book Rating : 4.:/5 (244 download)
Download or read book Electronic Implementation of Neural Networks written by Bhupendra S. Bhakta and published by . This book was released on 1991 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Steven F. Zornetzer
Publisher :
ISBN 13 :
Total Pages : 544 pages
Book Rating : 4.3/5 (91 download)
Download or read book An Introduction to Neural and Electronic Networks written by Steven F. Zornetzer and published by . This book was released on 1995 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: This presentation of the foremost research and theory from disciplines that provide the foundations of neural network research--neurobiology, physics, computer science, electrical engineering, mathematics, and psychology--shows how neural networks and neurocomputing represent radical departures from conventional approaches to digital computers, in terms of algorithms and architecture.
Author : Milan Curcic
Publisher : Simon and Schuster
ISBN 13 : 1638350051
Total Pages : 414 pages
Book Rating : 4.6/5 (383 download)
Download or read book Modern Fortran written by Milan Curcic and published by Simon and Schuster. This book was released on 2020-10-07 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern Fortran teaches you to develop fast, efficient parallel applications using twenty-first-century Fortran. In this guide, you’ll dive into Fortran by creating fun apps, including a tsunami simulator and a stock price analyzer. Filled with real-world use cases, insightful illustrations, and hands-on exercises, Modern Fortran helps you see this classic language in a whole new light. Summary Using Fortran, early and accurate forecasts for hurricanes and other major storms have saved thousands of lives. Better designs for ships, planes, and automobiles have made travel safer, more efficient, and less expensive than ever before. Using Fortran, low-level machine learning and deep learning libraries provide incredibly easy, fast, and insightful analysis of massive data. Fortran is an amazingly powerful and flexible programming language that forms the foundation of high performance computing for research, science, and industry. And it's come a long, long way since starting life on IBM mainframes in 1956. Modern Fortran is natively parallel, so it's uniquely suited for efficiently handling problems like complex simulations, long-range predictions, and ultra-precise designs. If you're working on tasks where speed, accuracy, and efficiency matter, it's time to discover—or re-discover—Fortran.. About the technology For over 60 years Fortran has been powering mission-critical scientific applications, and it isn't slowing down yet! Rock-solid reliability and new support for parallel programming make Fortran an essential language for next-generation high-performance computing. Simply put, the future is in parallel, and Fortran is already there. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the book Modern Fortran teaches you to develop fast, efficient parallel applications using twenty-first-century Fortran. In this guide, you'll dive into Fortran by creating fun apps, including a tsunami simulator and a stock price analyzer. Filled with real-world use cases, insightful illustrations, and hands-on exercises, Modern Fortran helps you see this classic language in a whole new light. What's inside Fortran's place in the modern world Working with variables, arrays, and functions Module development Parallelism with coarrays, teams, and events Interoperating Fortran with C About the reader For developers and computational scientists. No experience with Fortran required. About the author Milan Curcic is a meteorologist, oceanographer, and author of several general-purpose Fortran libraries and applications. Table of Contents PART 1 - GETTING STARTED WITH MODERN FORTRAN 1 Introducing Fortran 2 Getting started: Minimal working app PART 2 - CORE ELEMENTS OF FORTRAN 3 Writing reusable code with functions and subroutines 4 Organizing your Fortran code using modules 5 Analyzing time series data with arrays 6 Reading, writing, and formatting your data PART 3 - ADVANCED FORTRAN USE 7 Going parallel with Fortan coarrays 8 Working with abstract data using derived types 9 Generic procedures and operators for any data type 10 User-defined operators for derived types PART 4 - THE FINAL STRETCH 11 Interoperability with C: Exposing your app to the web 12 Advanced parallelism with teams, events, and collectives
Author : Mona E. Zaghloul
Publisher : Springer Science & Business Media
ISBN 13 : 1461526809
Total Pages : 293 pages
Book Rating : 4.4/5 (615 download)
Download or read book Silicon Implementation of Pulse Coded Neural Networks written by Mona E. Zaghloul and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: When confronted with the hows and whys of nature's computational engines, some prefer to focus upon neural function: addressing issues of neural system behavior and its relation to natural intelligence. Then there are those who prefer the study of the "mechanics" of neural systems: the nuts and bolts of the "wetware": the neurons and synapses. Those who investigate pulse coded implementations ofartificial neural networks know what it means to stand at the boundary which lies between these two worlds: not just asking why natural neural systems behave as they do, but also how they achieve their marvelous feats. The research results presented in this book not only address more conventional abstract notions of neural-like processing, but also the more specific details ofneural-like processors. It has been established for some time that natural neural systems perform a great deal of information processing via electrochemical pulses. Accordingly, pulse coded neural network concepts are receiving increased attention in artificial neural network research. This increased interest is compounded by continuing advances in the field of VLSI circuit design. This is the first time in history in which it is practical to construct networks of neuron-like circuits of reasonable complexity that can be applied to real problems. We believe that the pioneering work in artificial neural systems presented in this book will lead to further advances that will not only be useful in some practical sense, but may also provide some additional insight into the operation of their natural counterparts.
Author : Ulrich Ramacher
Publisher : Springer Science & Business Media
ISBN 13 : 1461539943
Total Pages : 346 pages
Book Rating : 4.4/5 (615 download)
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.
Author : Bert Moons
Publisher : Springer
ISBN 13 : 3319992236
Total Pages : 216 pages
Book Rating : 4.3/5 (199 download)
Download or read book Embedded Deep Learning written by Bert Moons and published by Springer. This book was released on 2018-10-23 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers algorithmic and hardware implementation techniques to enable embedded deep learning. The authors describe synergetic design approaches on the application-, algorithmic-, computer architecture-, and circuit-level that will help in achieving the goal of reducing the computational cost of deep learning algorithms. The impact of these techniques is displayed in four silicon prototypes for embedded deep learning. Gives a wide overview of a series of effective solutions for energy-efficient neural networks on battery constrained wearable devices; Discusses the optimization of neural networks for embedded deployment on all levels of the design hierarchy – applications, algorithms, hardware architectures, and circuits – supported by real silicon prototypes; Elaborates on how to design efficient Convolutional Neural Network processors, exploiting parallelism and data-reuse, sparse operations, and low-precision computations; Supports the introduced theory and design concepts by four real silicon prototypes. The physical realization’s implementation and achieved performances are discussed elaborately to illustrated and highlight the introduced cross-layer design concepts.
Author : Fathi M. Salem
Publisher : Springer Nature
ISBN 13 : 3030899292
Total Pages : 130 pages
Book Rating : 4.0/5 (38 download)
Download or read book Recurrent Neural Networks written by Fathi M. Salem and published by Springer Nature. This book was released on 2022-01-03 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides a compact but comprehensive treatment that provides analytical and design steps to recurrent neural networks from scratch. It provides a treatment of the general recurrent neural networks with principled methods for training that render the (generalized) backpropagation through time (BPTT). This author focuses on the basics and nuances of recurrent neural networks, providing technical and principled treatment of the subject, with a view toward using coding and deep learning computational frameworks, e.g., Python and Tensorflow-Keras. Recurrent neural networks are treated holistically from simple to gated architectures, adopting the technical machinery of adaptive non-convex optimization with dynamic constraints to leverage its systematic power in organizing the learning and training processes. This permits the flow of concepts and techniques that provide grounded support for design and training choices. The author’s approach enables strategic co-training of output layers, using supervised learning, and hidden layers, using unsupervised learning, to generate more efficient internal representations and accuracy performance. As a result, readers will be enabled to create designs tailoring proficient procedures for recurrent neural networks in their targeted applications.
Author : Zhang, Ming
Publisher : IGI Global
ISBN 13 : 1615207120
Total Pages : 660 pages
Book Rating : 4.6/5 (152 download)
Download or read book Artificial Higher Order Neural Networks for Computer Science and Engineering: Trends for Emerging Applications written by Zhang, Ming and published by IGI Global. This book was released on 2010-02-28 with total page 660 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book introduces and explains Higher Order Neural Networks (HONNs) to people working in the fields of computer science and computer engineering, and how to use HONNS in these areas"--Provided by publisher.
Author : David A. Durfee
Publisher :
ISBN 13 :
Total Pages : 208 pages
Book Rating : 4.:/5 (295 download)
Download or read book Design and Implementation of Electronic Artificial Neural Networks written by David A. Durfee and published by . This book was released on 1992 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Bing J. Sheu
Publisher : Springer Science & Business Media
ISBN 13 : 1461522471
Total Pages : 569 pages
Book Rating : 4.4/5 (615 download)
Download or read book Neural Information Processing and VLSI written by Bing J. Sheu and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 569 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 : International Computer Science Institute
Publisher :
ISBN 13 :
Total Pages : 11 pages
Book Rating : 4.:/5 (287 download)
Download or read book Considerations for the Electronic Implementation of Artificial Neural Networks written by International Computer Science Institute and published by . This book was released on 1990 with total page 11 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : George William Irwin
Publisher : IET
ISBN 13 : 9780852968529
Total Pages : 320 pages
Book Rating : 4.9/5 (685 download)
Download or read book Neural Network Applications in Control written by George William Irwin and published by IET. This book was released on 1995 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: The aim is to present an introduction to, and an overview of, the present state of neural network research and development, with an emphasis on control systems application studies. The book is useful to a range of levels of reader. The earlier chapters introduce the more popular networks and the fundamental control principles, these are followed by a series of application studies, most of which are industrially based, and the book concludes with a consideration of some recent research.
Author : Nan Zheng
Publisher : John Wiley & Sons
ISBN 13 : 1119507405
Total Pages : 389 pages
Book Rating : 4.1/5 (195 download)
Download or read book Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design written by Nan Zheng and published by John Wiley & Sons. This book was released on 2019-10-18 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explains current co-design and co-optimization methodologies for building hardware neural networks and algorithms for machine learning applications This book focuses on how to build energy-efficient hardware for neural networks with learning capabilities—and provides co-design and co-optimization methodologies for building hardware neural networks that can learn. Presenting a complete picture from high-level algorithm to low-level implementation details, Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design also covers many fundamentals and essentials in neural networks (e.g., deep learning), as well as hardware implementation of neural networks. The book begins with an overview of neural networks. It then discusses algorithms for utilizing and training rate-based artificial neural networks. Next comes an introduction to various options for executing neural networks, ranging from general-purpose processors to specialized hardware, from digital accelerator to analog accelerator. A design example on building energy-efficient accelerator for adaptive dynamic programming with neural networks is also presented. An examination of fundamental concepts and popular learning algorithms for spiking neural networks follows that, along with a look at the hardware for spiking neural networks. Then comes a chapter offering readers three design examples (two of which are based on conventional CMOS, and one on emerging nanotechnology) to implement the learning algorithm found in the previous chapter. The book concludes with an outlook on the future of neural network hardware. Includes cross-layer survey of hardware accelerators for neuromorphic algorithms Covers the co-design of architecture and algorithms with emerging devices for much-improved computing efficiency Focuses on the co-design of algorithms and hardware, which is especially critical for using emerging devices, such as traditional memristors or diffusive memristors, for neuromorphic computing Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design is an ideal resource for researchers, scientists, software engineers, and hardware engineers dealing with the ever-increasing requirement on power consumption and response time. It is also excellent for teaching and training undergraduate and graduate students about the latest generation neural networks with powerful learning capabilities.
