Neural Network Parallel Computing

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Neural Network Parallel Computing

Author : Yoshiyasu Takefuji
Publisher : Springer Science & Business Media
ISBN 13 : 1461536421
Total Pages : 237 pages
Book Rating : 4.4/5 (615 download)

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Book Synopsis Neural Network Parallel Computing by : Yoshiyasu Takefuji

Download or read book Neural Network Parallel Computing written by Yoshiyasu Takefuji and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Deep Learning and Parallel Computing Environment for Bioengineering Systems

Author : Arun Kumar Sangaiah
Publisher : Academic Press
ISBN 13 : 0128172932
Total Pages : 280 pages
Book Rating : 4.1/5 (281 download)

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Book Synopsis Deep Learning and Parallel Computing Environment for Bioengineering Systems by : Arun Kumar Sangaiah

Download or read book Deep Learning and Parallel Computing Environment for Bioengineering Systems written by Arun Kumar Sangaiah and published by Academic Press. This book was released on 2019-07-26 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, mining, recommendation systems, image processing, pattern recognition and predictions using deep learning paradigms is the major strength of this book. This book integrates the core ideas of deep learning and its applications in bio engineering application domains, to be accessible to all scholars and academicians. The proposed techniques and concepts in this book can be extended in future to accommodate changing business organizations’ needs as well as practitioners’ innovative ideas. Presents novel, in-depth research contributions from a methodological/application perspective in understanding the fusion of deep machine learning paradigms and their capabilities in solving a diverse range of problems Illustrates the state-of-the-art and recent developments in the new theories and applications of deep learning approaches applied to parallel computing environment in bioengineering systems Provides concepts and technologies that are successfully used in the implementation of today's intelligent data-centric critical systems and multi-media Cloud-Big data

Parallel Processing in Neural Systems and Computers

Author : Rolf Eckmiller
Publisher : North Holland
ISBN 13 :
Total Pages : 652 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Parallel Processing in Neural Systems and Computers by : Rolf Eckmiller

Download or read book Parallel Processing in Neural Systems and Computers written by Rolf Eckmiller and published by North Holland. This book was released on 1990 with total page 652 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 119 contributions in this book cover a range of topics, including parallel computing, parallel processing in biological neural systems, simulators for artificial neural networks, neural networks for visual and auditory pattern recognition as well as for motor control, AI, and examples of optical and molecular computing. The book may be regarded as a state-of-the-art report and at the same time as an Interdisciplinary Reference Source' for parallel processing. It should catalyze international and interdisciplinary cooperation among computer scientists, neuroscientists, physicists and engineers in the attempt to: 1) decipher parallel information processes in biology, physics and chemistry 2) design conceptually similar technical parallel information processors."

Parallel Architectures for Artificial Neural Networks

Author : N. Sundararajan
Publisher : Wiley-IEEE Computer Society Press
ISBN 13 :
Total Pages : 424 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Parallel Architectures for Artificial Neural Networks by : N. Sundararajan

Download or read book Parallel Architectures for Artificial Neural Networks written by N. Sundararajan and published by Wiley-IEEE Computer Society Press. This book was released on 1998-12-14 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Adaptive and Natural Computing Algorithms

Author : Mikko Kolehmainen
Publisher : Springer Science & Business Media
ISBN 13 : 3642049206
Total Pages : 645 pages
Book Rating : 4.6/5 (42 download)

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Book Synopsis Adaptive and Natural Computing Algorithms by : Mikko Kolehmainen

Download or read book Adaptive and Natural Computing Algorithms written by Mikko Kolehmainen and published by Springer Science & Business Media. This book was released on 2009-10-15 with total page 645 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-proceedings of the 9th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2009, held in Kuopio, Finland, in April 2009. The 63 revised full papers presented were carefully reviewed and selected from a total of 112 submissions. The papers are organized in topical sections on neutral networks, evolutionary computation, learning, soft computing, bioinformatics as well as applications.

ADVANCED TOPICS IN NEURAL NETWORKS WITH MATLAB. PARALLEL COMPUTING, OPTIMIZE AND TRAINING

Author : PEREZ C.
Publisher : CESAR PEREZ
ISBN 13 : 1974082040
Total Pages : 78 pages
Book Rating : 4.9/5 (74 download)

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Book Synopsis ADVANCED TOPICS IN NEURAL NETWORKS WITH MATLAB. PARALLEL COMPUTING, OPTIMIZE AND TRAINING by : PEREZ C.

Download or read book ADVANCED TOPICS IN NEURAL NETWORKS WITH MATLAB. PARALLEL COMPUTING, OPTIMIZE AND TRAINING written by PEREZ C. and published by CESAR PEREZ. This book was released on 2023-12-13 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt: Neural networks are inherently parallel algorithms. Multicore CPUs, graphical processing units (GPUs), and clusters of computers with multiple CPUs and GPUs can take advantage of this parallelism. Parallel Computing Toolbox, when used in conjunction with Neural Network Toolbox, enables neural network training and simulation to take advantage of each mode of parallelism. Parallel Computing Toolbox allows neural network training and simulation to run across multiple CPU cores on a single PC, or across multiple CPUs on multiple computers on a network using MATLAB Distributed Computing Server. Using multiple cores can speed calculations. Using multiple computers can allow you to solve problems using data sets too big to fit in the RAM of a single computer. The only limit to problem size is the total quantity of RAM available across all computers. Distributed and GPU computing can be combined to run calculations across multiple CPUs and/or GPUs on a single computer, or on a cluster with MATLAB Distributed Computing Server. It is desirable to determine the optimal regularization parameters in an automated fashion. One approach to this process is the Bayesian framework. In this framework, the weights and biases of the network are assumed to be random variables with specified distributions. The regularization parameters are related to the unknown variances associated with these distributions. You can then estimate these parameters using statistical techniques. It is very difficult to know which training algorithm will be the fastest for a given problem. It depends on many factors, including the complexity of the problem, the number of data points in the training set, the number of weights and biases in the network, the error goal, and whether the network is being used for pattern recognition (discriminant analysis) or function approximation (regression). This book compares the various training algorithms. One of the problems that occur during neural network training is called overfitting. The error on the training set is driven to a very small value, but when new data is presented to the network the error is large. The network has memorized the training examples, but it has not learned to generalize to new situations. This book develops the following topics: Neural Networks with Parallel and GPU Computing Deep Learning Optimize Neural Network Training Speed and Memory Improve Neural Network Generalization and Avoid Overfitting Create and Train Custom Neural Network Architectures Deploy Training of Neural Networks Perceptron Neural Networks Linear Neural Networks Hopfield Neural Network Neural Network Object Reference Neural Network Simulink Block Library Deploy Neural Network Simulink Diagrams

Programming Massively Parallel Processors

Author : David B. Kirk
Publisher : Newnes
ISBN 13 : 0123914183
Total Pages : 514 pages
Book Rating : 4.1/5 (239 download)

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Book Synopsis Programming Massively Parallel Processors by : David B. Kirk

Download or read book Programming Massively Parallel Processors written by David B. Kirk and published by Newnes. This book was released on 2012-12-31 with total page 514 pages. Available in PDF, EPUB and Kindle. Book excerpt: Programming Massively Parallel Processors: A Hands-on Approach, Second Edition, teaches students how to program massively parallel processors. It offers a detailed discussion of various techniques for constructing parallel programs. Case studies are used to demonstrate the development process, which begins with computational thinking and ends with effective and efficient parallel programs. This guide shows both student and professional alike the basic concepts of parallel programming and GPU architecture. Topics of performance, floating-point format, parallel patterns, and dynamic parallelism are covered in depth. This revised edition contains more parallel programming examples, commonly-used libraries such as Thrust, and explanations of the latest tools. It also provides new coverage of CUDA 5.0, improved performance, enhanced development tools, increased hardware support, and more; increased coverage of related technology, OpenCL and new material on algorithm patterns, GPU clusters, host programming, and data parallelism; and two new case studies (on MRI reconstruction and molecular visualization) that explore the latest applications of CUDA and GPUs for scientific research and high-performance computing. This book should be a valuable resource for advanced students, software engineers, programmers, and hardware engineers. New coverage of CUDA 5.0, improved performance, enhanced development tools, increased hardware support, and more Increased coverage of related technology, OpenCL and new material on algorithm patterns, GPU clusters, host programming, and data parallelism Two new case studies (on MRI reconstruction and molecular visualization) explore the latest applications of CUDA and GPUs for scientific research and high-performance computing

Adaptive and Natural Computing Algorithms

Author : Andrej Dobnikar
Publisher : Springer
ISBN 13 : 3642202829
Total Pages : 433 pages
Book Rating : 4.6/5 (422 download)

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Book Synopsis Adaptive and Natural Computing Algorithms by : Andrej Dobnikar

Download or read book Adaptive and Natural Computing Algorithms written by Andrej Dobnikar and published by Springer. This book was released on 2011-04-09 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 6593 and 6594 constitutes the refereed proceedings of the 10th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2010, held in Ljubljana, Slovenia, in April 2010. The 83 revised full papers presented were carefully reviewed and selected from a total of 144 submissions. The first volume includes 42 papers and a plenary lecture and is organized in topical sections on neural networks and evolutionary computation.

Neural Information Processing and VLSI

Author : Bing J. Sheu
Publisher : Springer Science & Business Media
ISBN 13 : 1461522471
Total Pages : 569 pages
Book Rating : 4.4/5 (615 download)

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Book Synopsis Neural Information Processing and VLSI by : Bing J. Sheu

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.

Parallel Distributed Processing

Author : David E. Rumelhart
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (135 download)

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Book Synopsis Parallel Distributed Processing by : David E. Rumelhart

Download or read book Parallel Distributed Processing written by David E. Rumelhart and published by . This book was released on 1986 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: What makes people smarter than computers? These volumes by a pioneering neurocomputing group suggest that the answer lies in the massively parallel architecture of the human mind. They describe a new theory of cognition called connectionism that is challenging the idea of symbolic computation that has traditionally been at the center of debate in theoretical discussions about the mind. The authors' theory assumes the mind is composed of a great number of elementary units connected in a neural network. Mental processes are interactions between these units which excite and inhibit each other in parallel rather than sequential operations. In this context, knowledge can no longer be thought of as stored in localized structures; instead, it consists of the connections between pairs of units that are distributed throughout the network. Volume 1 lays the foundations of this exciting theory of parallel distributed processing, while Volume 2 applies it to a number of specific issues in cognitive science and neuroscience, with chapters describing models of aspects of perception, memory, language, and thought.

Network and Parallel Computing

Author : Xiaoxin Tang
Publisher : Springer Nature
ISBN 13 : 3030307093
Total Pages : 385 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Network and Parallel Computing by : Xiaoxin Tang

Download or read book Network and Parallel Computing written by Xiaoxin Tang and published by Springer Nature. This book was released on 2019-09-28 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 16th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2019, held in Hohhot, China, in August 2019. The 22 full and 11 short papers presented in this volume were carefully reviewed and selected from 107 submissions. They were organized in topical sections named: graph computing; NOC and networks; neural networks; big data and cloud; HPC; emerging topics; memory and file system.

Advenced Neural Networks With Matlab

Author : L. Abell
Publisher : Createspace Independent Publishing Platform
ISBN 13 : 9781547013043
Total Pages : 438 pages
Book Rating : 4.0/5 (13 download)

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Book Synopsis Advenced Neural Networks With Matlab by : L. Abell

Download or read book Advenced Neural Networks With Matlab written by L. Abell and published by Createspace Independent Publishing Platform. This book was released on 2017-05-29 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATLAB Neural Network Toolbox provides algorithms, pretrained models, and apps to create, train, visualize, and simulate both shallow and deep neural networks. You can perform classification, regression, clustering, dimensionality reduction, time-series forecasting, and dynamic system modeling and control. Deep learning networks include convolutional neural networks (ConvNets, CNNs) and autoencoders for image classification, regression, and feature learning. For small training sets, you can quickly apply deep learning by performing transfer learning with pretrained deep networks. To speed up training on large datasets, you can use Parallel Computing Toolbox to distribute computations and data across multicore processors and GPUs on the desktop, and you can scale up to clusters and clouds (including Amazon EC2(R) P2 GPU instances) with MATLAB(R) Distributed Computing Server. The Key Features developed in this book are de next: - Deep learning with convolutional neural networks (for classification and regression) and autoencoders (for feature learning) - Transfer learning with pretrained convolutional neural network models - Training and inference with CPUs or multi-GPUs on desktops, clusters, and clouds - Unsupervised learning algorithms, including self-organizing maps and competitive layers - Supervised learning algorithms, including multilayer, radial basis, learning vector quantization (LVQ), time-delay, nonlinear autoregressive (NARX), and recurrent neural network (RNN) - Preprocessing, postprocessing, and network visualization for improving training efficiency and assessing network performance

Parallel Computing Technologies

Author : Viktor Ėmmanuilovich Malyshkin
Publisher : Springer Science & Business Media
ISBN 13 : 3540281266
Total Pages : 480 pages
Book Rating : 4.5/5 (42 download)

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Book Synopsis Parallel Computing Technologies by : Viktor Ėmmanuilovich Malyshkin

Download or read book Parallel Computing Technologies written by Viktor Ėmmanuilovich Malyshkin and published by Springer Science & Business Media. This book was released on 2005-08-18 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 8th International Conference on Parallel Computing Technologies, PaCT 2005, held in Krasnoyarsk, Russia in September 2005. The 38 revised full papers presented together with 1 invited paper were carefully reviewed and selected from 78 submissions. The papers are organized in topical sections on theory, fine-grain parallelism, software, tools, and applications. A broad variety of parallel processing issues and distributed computing in general are addressed as well.

SpiNNaker - A Spiking Neural Network Architecture

Author : Steve Furber
Publisher : NowOpen
ISBN 13 : 9781680836523
Total Pages : 352 pages
Book Rating : 4.8/5 (365 download)

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Book Synopsis SpiNNaker - A Spiking Neural Network Architecture by : Steve Furber

Download or read book SpiNNaker - A Spiking Neural Network Architecture written by Steve Furber and published by NowOpen. This book was released on 2020-03-15 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: This books tells the story of the origins of the world's largest neuromorphic computing platform, its development and its deployment, and the immense software development effort that has gone into making it openly available and accessible to researchers and students the world over

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.

Parallel Computing

Author : Jonathan P. Gray
Publisher : IOS Press
ISBN 13 : 9789051991963
Total Pages : 312 pages
Book Rating : 4.9/5 (919 download)

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Book Synopsis Parallel Computing by : Jonathan P. Gray

Download or read book Parallel Computing written by Jonathan P. Gray and published by IOS Press. This book was released on 1995 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: The broadening of interest in parallel computing and transputers is reflected this book. Topics discussed include: concurrent programming; graphics and image processing; parallel applications; robotics; and control and software tools. The book also features a collection of abstracts of poster presentations.

Parallel Algorithms for Digital Image Processing, Computer Vision and Neural Networks

Author : Ioannis Pitas
Publisher :
ISBN 13 :
Total Pages : 424 pages
Book Rating : 4.F/5 ( download)

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Book Synopsis Parallel Algorithms for Digital Image Processing, Computer Vision and Neural Networks by : Ioannis Pitas

Download or read book Parallel Algorithms for Digital Image Processing, Computer Vision and Neural Networks written by Ioannis Pitas and published by . This book was released on 1993-04-09 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.