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Author: K. Wojtek Przytula Publisher: Prentice Hall ISBN: Category : Computers Languages : en Pages : 346
Book Description
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: K. Wojtek Przytula Publisher: Prentice Hall ISBN: Category : Computers Languages : en Pages : 346
Book Description
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 Science & Business Media ISBN: 1461536421 Category : Technology & Engineering Languages : en Pages : 237
Book Description
Neural Network Parallel Computing is the first book available to the professional market on neural network computing for optimization problems. This introductory book is not only for the novice reader, but for experts in a variety of areas including parallel computing, neural network computing, computer science, communications, graph theory, computer aided design for VLSI circuits, molecular biology, management science, and operations research. The goal of the book is to facilitate an understanding as to the uses of neural network models in real-world applications. Neural Network Parallel Computing presents a major breakthrough in science and a variety of engineering fields. The computational power of neural network computing is demonstrated by solving numerous problems such as N-queen, crossbar switch scheduling, four-coloring and k-colorability, graph planarization and channel routing, RNA secondary structure prediction, knight's tour, spare allocation, sorting and searching, and tiling. Neural Network Parallel Computing is an excellent reference for researchers in all areas covered by the book. Furthermore, the text may be used in a senior or graduate level course on the topic.
Author: N. Sundararajan Publisher: Wiley-IEEE Computer Society Press ISBN: Category : Computers Languages : en Pages : 424
Book Description
An excellent reference for neural networks research and application, this book covers the parallel implementation aspects of all major artificial neural network models in a single text. Parallel Architectures for Artificial Neural Networks details implementations on various processor architectures built on different hardware platforms, ranging from large, general purpose parallel computers to custom built MIMD machine. Working experts describe their implementation research including results that are then divided into three sections: The theoretical analysis of parallel implementation schemes on MIMD message passing machines The details of parallel implementation of BP neural networks on general purpose, large, parallel computers Four specific purpose parallel neural computer configuration Aimed at graduate students and researchers working in artificial neural networks and parallel computing this work can be used by graduate level educators to illustrate parallel computing methods for ANN simulation. Practitioners will also find the text an ideal reference tool for lucid mathematical analyses.
Author: P Saratchandran Publisher: World Scientific ISBN: 9814498998 Category : Computers Languages : en Pages : 222
Book Description
This book presents a systematic approach to parallel implementation of feedforward neural networks on an array of transputers. The emphasis is on backpropagation learning and training set parallelism. Using systematic analysis, a theoretical model has been developed for the parallel implementation. The model is used to find the optimal mapping to minimize the training time for large backpropagation neural networks. The model has been validated experimentally on several well known benchmark problems. Use of genetic algorithms for optimizing the performance of the parallel implementations is described. Guidelines for efficient parallel implementations are highlighted.
Author: Amos R. Omondi Publisher: Springer Science & Business Media ISBN: 0387284877 Category : Technology & Engineering Languages : en Pages : 365
Book Description
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: Sun Yuan Kung Publisher: Prentice Hall ISBN: Category : Computers Languages : en Pages : 472
Book Description
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: Ioannis Pitas Publisher: ISBN: Category : Computers Languages : en Pages : 424
Book Description
World-renowned contributors present papers concerning algorithms used on the latest generation of parallel machines (MIMD). Details key applications running the gamut from medical imaging, visualization and remote sensing to HDTV, demonstrating the large computational complexity necessary to perform these tasks.
Author: Sied Mehdi Fakhraie Publisher: Springer Science & Business Media ISBN: 1461563119 Category : Technology & Engineering Languages : en Pages : 216
Book Description
This book introduces several state-of-the-art VLSI implementations of artificial neural networks (ANNs). It reviews various hardware approaches to ANN implementations: analog, digital and pulse-coded. The analog approach is emphasized as the main one taken in the later chapters of the book. The area of VLSI implementation of ANNs has been progressing for the last 15 years, but not at the fast pace originally predicted. Several reasons have contributed to the slow progress, with the main one being that VLSI implementation of ANNs is an interdisciplinaly area where only a few researchers, academics and graduate students are willing to venture. The work of Professors Fakhraie and Smith, presented in this book, is a welcome addition to the state-of-the-art and will greatly benefit researchers and students working in this area. Of particular value is the use of experimental results to backup extensive simulations and in-depth modeling. The introduction of a synapse-MOS device is novel. The book applies the concept to a number of applications and guides the reader through more possible applications for future work. I am confident that the book will benefit a potentially wide readership. M. I. Elmasry University of Waterloo Waterloo, Ontario Canada Preface Neural Networks (NNs), generally defined as parallel networks that employ a large number of simple processing elements to perform computation in a distributed fashion, have attracted a lot of attention in the past fifty years. As the result. many new discoveries have been made.
Author: E Fiesler Publisher: CRC Press ISBN: 1420050648 Category : Computers Languages : en Pages : 1094
Book Description
The Handbook of Neural Computation is a practical, hands-on guide to the design and implementation of neural networks used by scientists and engineers to tackle difficult and/or time-consuming problems. The handbook bridges an information pathway between scientists and engineers in different disciplines who apply neural networks to similar probl
Author: K. Wojtek Przytula
Author: K. Wojtek Przytula
Author: Yoshiyasu Takefuji
Author: N. Sundararajan
Author: P Saratchandran
Author: Amos R. Omondi
Author: Sun Yuan Kung
Author: Paul Taylor Wildes
Author: Ioannis Pitas
Author: Sied Mehdi Fakhraie
Author: E Fiesler