Neural Nets for Massively Parallel Optimization PDF Download

Author: L. C. W. Dixon
Publisher:
ISBN:
Category : Bionics
Languages : en
Pages :

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Author: Gilbert Kalb
Publisher: IOS Press
ISBN: 9789051990973
Category : Computers
Languages : en
Pages : 220

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A survey of products and research projects in the field of highly parallel, optical and neural computers in the USA. It covers operating systems, language projects and market analysis, as well as optical computing devices and optical connections of electronic parts.

Author: Michael Oldroyd
Publisher: Fultus Corporation
ISBN: 1596820101
Category : Neural networks (Computer science)
Languages : en
Pages : 161

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Book Description: Most current artificial neural networks exist only within software simulators running on conventional computers. Simulators can provide great flexibility, but require immensely powerful and costly hardware for even very small networks. An artificial neural network implemented as a custom integrated circuit could operate many thousands of times faster than any simulator as each neuron can operate simultaneously. A significant problem with implementing neural networks in hardware is that larger networks require a great deal of silicon area, making them too costly to design and produce. In this book, I test the effectiveness of a number of algorithms that reduce the size of a trained neural network while maintaining accuracy. Author Biography: Michael Oldroyd is a software development veteran who started progamming professionally back in 1992. He is now development manager at AES Data Systems. He has worked as a consultant and software developer for a number of international organisations including Mobil Oil, The European Commission, Deutsche Bank, Compaq Computer, and the Cabinet Office. He has developed several bespoke AI trading and decision support tools used on trading floors in the currency, stock and energy markets. He is a professional member of the IEEE and the Computational Intelligence Society.

Author: Valmir C. Barbosa
Publisher: Prentice Hall
ISBN:
Category : Computers
Languages : en
Pages : 280

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This text explores the simulation by distributed parallel computers of massively parallel models of interest in artificial intelligence. A series of models are surveyed, including cellular automata, Hopfield neural networks, Bayesian networks, Markov random fields and Boltzmann machines.

Author: Xinzhi Li
Publisher:
ISBN:
Category :
Languages : de
Pages : 166

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Author:
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Category :
Languages : en
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In previous work by the authors, the ''optimum finding'' properties of Hopfield neural nets were applied to the nets themselves to create a ''neural compiler.'' This was done in such a way that the problem of programming the attractors of one neural net (called the Slave net) was expressed as an optimization problem that was in turn solved by a second neural net (the Master net). In this series of papers that approach is extended to programming nets that contain interneurons (sometimes called ''hidden neurons''), and thus deals with nets capable of universal computation. 22 refs.

Author: A. Migdalas
Publisher: Springer
ISBN:
Category : Business & Economics
Languages : en
Pages : 616

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During the last three decades, breakthroughs in computer technology have made a tremendous impact on optimization. In particular, parallel computing has made it possible to solve larger and computationally more difficult problems. The book covers recent developments in novel programming and algorithmic aspects of parallel computing as well as technical advances in parallel optimization. Each contribution is essentially expository in nature, but of scholarly treatment. In addition, each chapter includes a collection of carefully selected problems. The first two chapters discuss theoretical models for parallel algorithm design and their complexity. The next chapter gives the perspective of the programmer practicing parallel algorithm development on real world platforms. Solving systems of linear equations efficiently is of great importance not only because they arise in many scientific and engineering applications but also because algorithms for solving many optimization problems need to call system solvers and subroutines (chapters four and five). Chapters six through thirteen are dedicated to optimization problems and methods. They include parallel algorithms for network problems, parallel branch and bound techniques, parallel heuristics for discrete and continuous problems, decomposition methods, parallel algorithms for variational inequality problems, parallel algorithms for stochastic programming, and neural networks. Audience: Parallel Computing in Optimization is addressed not only to researchers of mathematical programming, but to all scientists in various disciplines who use optimization methods in parallel and multiprocessing environments to model and solve problems.

Author: PEREZ C.
Publisher: CESAR PEREZ
ISBN: 1974082040
Category : Computers
Languages : en
Pages : 78

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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

Author: David B. Kirk
Publisher: Newnes
ISBN: 0123914183
Category : Computers
Languages : en
Pages : 519

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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

Author: Walter Edward Hammond
Publisher: AIAA
ISBN: 9781600860454
Category : Astronautics
Languages : en
Pages : 906

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Annotation "Design Methodologies for Space Transportation Systems is a sequel to the author's earlier text, "Space Transportation: A Systems Approach to Analysis and Design. Both texts represent the most comprehensive exposition of the existing knowledge and practice in the design and project management of space transportation systems, and they reflect a wealth of experience by the author with the design and management of space systems. The text discusses new conceptual changes in the design philosophy away from multistage expendable vehicles to winged, reusable launch vehicles and presents an overview of the systems engineering and vehicle design process as well as systems trades and analysis. Individual chapters are devoted to specific disciplines such as aerodynamics, aerothermal analysis, structures, materials, propulsion, flight mechanics and trajectories, avionics and computers, and control systems. The final chapters deal with human factors, payload, launch and mission operations, safety, and mission assurance. The two texts by the author provide a valuable source of information for the space transportation community of designers, operators, and managers. A companion CD-ROM succinctly packages some oversized figures and tables, resources for systems engineering and launch ranges, and a compendium of software programs. The computer programs include the USAF AIRPLANE AND MISSILE DATCOM CODES (with extensive documentation); COSTMODL for software costing; OPGUID launch vehicle trajectory generator; SUPERFLO-a series of 11 programs intended for solving compressible flow problems in ducts and pipes found in industrial facilities; and a wealth of Microsoft Excel spreadsheet programs covering thedisciplines of statistics, vehicle trajectories, propulsion performance, math utilities,