Neural Network Parallel Computing
Neural Network Parallel Computing

Author: Yoshiyasu Takefuji

Publisher: Springer Science & Business Media

Published: 1992-01-31

Total Pages: 254

ISBN-13: 9780792391906

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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.

Neural Network Parallel Computing
Neural Network Parallel Computing

Author: Yoshiyasu Takefuji

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 237

ISBN-13: 1461536421

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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.

Adaptive and Natural Computing Algorithms
Adaptive and Natural Computing Algorithms

Author: Mikko Kolehmainen

Publisher: Springer Science & Business Media

Published: 2009-10-15

Total Pages: 645

ISBN-13: 3642049206

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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.

Adaptive and Natural Computing Algorithms
Adaptive and Natural Computing Algorithms

Author: Andrej Dobnikar

Publisher: Springer Science & Business Media

Published: 2011-03-03

Total Pages: 448

ISBN-13: 3642202810

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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.

Artificial Intelligence in the Age of Neural Networks and Brain Computing
Artificial Intelligence in the Age of Neural Networks and Brain Computing

Author: Robert Kozma

Publisher: Academic Press

Published: 2023-10-11

Total Pages: 398

ISBN-13: 0323958168

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Artificial Intelligence in the Age of Neural Networks and Brain Computing, Second Edition demonstrates that present disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel processing, black-box inference, intrinsic nonlinearity, and smart autonomous search engines. The book covers the major basic ideas of "brain-like computing" behind AI, provides a framework to deep learning, and launches novel and intriguing paradigms as possible future alternatives. The present success of AI-based commercial products proposed by top industry leaders, such as Google, IBM, Microsoft, Intel, and Amazon, can be interpreted using the perspective presented in this book by viewing the co-existence of a successful synergism among what is referred to as computational intelligence, natural intelligence, brain computing, and neural engineering. The new edition has been updated to include major new advances in the field, including many new chapters. - Developed from the 30th anniversary of the International Neural Network Society (INNS) and the 2017 International Joint Conference on Neural Networks (IJCNN - Authored by top experts, global field pioneers, and researchers working on cutting-edge applications in signal processing, speech recognition, games, adaptive control and decision-making - Edited by high-level academics and researchers in intelligent systems and neural networks - Includes all new chapters, including topics such as Frontiers in Recurrent Neural Network Research; Big Science, Team Science, Open Science for Neuroscience; A Model-Based Approach for Bridging Scales of Cortical Activity; A Cognitive Architecture for Object Recognition in Video; How Brain Architecture Leads to Abstract Thought; Deep Learning-Based Speech Separation and Advances in AI, Neural Networks

Neural Networks and Systolic Array Design
Neural Networks and Systolic Array Design

Author: Sankar K. Pal

Publisher: World Scientific

Published: 2002

Total Pages: 421

ISBN-13: 981277808X

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Neural networks (NNs) and systolic arrays (SAs) have many similar features. This volume describes, in a unified way, the basic concepts, theories and characteristic features of integrating or formulating different facets of NNs and SAs, as well as presents recent developments and significant applications. The articles, written by experts from all over the world, demonstrate the various ways this integration can be made to efficiently design methodologies, algorithms and architectures, and also implementations, for NN applications. The book will be useful to graduate students and researchers in many related areas, not only as a reference book but also as a textbook for some parts of the curriculum. It will also benefit researchers and practitioners in industry and R&D laboratories who are working in the fields of system design, VLSI, parallel processing, neural networks, and vision.

Models of Massive Parallelism
Models of Massive Parallelism

Author: Max Garzon

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 284

ISBN-13: 3642779050

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Locality is a fundamental restriction in nature. On the other hand, adaptive complex systems, life in particular, exhibit a sense of permanence and time lessness amidst relentless constant changes in surrounding environments that make the global properties of the physical world the most important problems in understanding their nature and structure. Thus, much of the differential and integral Calculus deals with the problem of passing from local information (as expressed, for example, by a differential equation, or the contour of a region) to global features of a system's behavior (an equation of growth, or an area). Fundamental laws in the exact sciences seek to express the observable global behavior of physical objects through equations about local interaction of their components, on the assumption that the continuum is the most accurate model of physical reality. Paradoxically, much of modern physics calls for a fundamen tal discrete component in our understanding of the physical world. Useful computational models must be eventually constructed in hardware, and as such can only be based on local interaction of simple processing elements.

Neural Networks and Analog Computation
Neural Networks and Analog Computation

Author: Hava T. Siegelmann

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 193

ISBN-13: 146120707X

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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.

Oscillatory Neural Networks
Oscillatory Neural Networks

Author: Margarita G. Kuzmina

Publisher: Walter de Gruyter

Published: 2013

Total Pages: 160

ISBN-13: 9783110269215

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Neural-like oscillatory network models allow to elucidate the possibilities of dynamical, synchronization-based types of image processing exploited by the brain. The oscillatory network capabilities, studied by means of computer modeling and qualitative analysis, are presented and discussed in this book, as well as several other problems of parallel distributed information processing.

Efficient Processing of Deep Neural Networks
Efficient Processing of Deep Neural Networks

Author: Vivienne Sze

Publisher: Springer Nature

Published: 2022-05-31

Total Pages: 254

ISBN-13: 3031017668

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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.