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.


Analog VLSI and Neural Systems

Analog VLSI and Neural Systems

Author: Carver Mead

Publisher: Addison Wesley Publishing Company

Published: 1989

Total Pages: 416

ISBN-13:

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A self-contained text, suitable for a broad audience. Presents basic concepts in electronics, transistor physics, and neurobiology for readers without backgrounds in those areas. Annotation copyrighted by Book News, Inc., Portland, OR


Neural Networks: Computational Models and Applications

Neural Networks: Computational Models and Applications

Author: Huajin Tang

Publisher: Springer Science & Business Media

Published: 2007-03-12

Total Pages: 310

ISBN-13: 3540692258

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Neural Networks: Computational Models and Applications presents important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence: pattern recognition, uniform approximation, constrained optimization, NP-hard problems, and image segmentation. The book offers a compact, insightful understanding of the broad and rapidly growing neural networks domain.


Encyclopedia of Complexity and Systems Science

Encyclopedia of Complexity and Systems Science

Author:

Publisher: Springer

Published: 2009-06-26

Total Pages: 10398

ISBN-13: 9780387758886

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This encyclopedia provides an authoritative single source for understanding and applying the concepts of complexity theory together with the tools and measures for analyzing complex systems in all fields of science and engineering. It links fundamental concepts of mathematics and computational sciences to applications in the physical sciences, engineering, biomedicine, economics and the social sciences.


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.


Analog VLSI Implementation of Neural Systems

Analog VLSI Implementation of Neural Systems

Author: Carver Mead

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 250

ISBN-13: 1461316391

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This volume contains the proceedings of a workshop on Analog Integrated Neural Systems held May 8, 1989, in connection with the International Symposium on Circuits and Systems. The presentations were chosen to encompass the entire range of topics currently under study in this exciting new discipline. Stringent acceptance requirements were placed on contributions: (1) each description was required to include detailed characterization of a working chip, and (2) each design was not to have been published previously. In several cases, the status of the project was not known until a few weeks before the meeting date. As a result, some of the most recent innovative work in the field was presented. Because this discipline is evolving rapidly, each project is very much a work in progress. Authors were asked to devote considerable attention to the shortcomings of their designs, as well as to the notable successes they achieved. In this way, other workers can now avoid stumbling into the same traps, and evolution can proceed more rapidly (and less painfully). The chapters in this volume are presented in the same order as the corresponding presentations at the workshop. The first two chapters are concerned with fmding solutions to complex optimization problems under a predefmed set of constraints. The first chapter reports what is, to the best of our knowledge, the first neural-chip design. In each case, the physics of the underlying electronic medium is used to represent a cost function in a natural way, using only nearest-neighbor connectivity.


Analog and Hybrid Computer Programming

Analog and Hybrid Computer Programming

Author: Bernd Ulmann

Publisher: Walter de Gruyter GmbH & Co KG

Published: 2023-05-22

Total Pages: 441

ISBN-13: 3110787881

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As classic digital computers are about to reach their physical and architectural boundaries, interest in unconventional approaches to computing, such as quantum and analog computers, is rapidly increasing. For a wide variety of practical applications, analog computers can outperform classic digital computers in terms of both raw computational speed and energy efficiency. This makes them ideally suited a co-processors to digital computers, thus forming hybrid computers. This second edition of "Analog and Hybrid Computer Programming" provides a thorough introduction to the programming of analog and hybrid computers. It contains a wealth of practical examples, ranging from simple problems such as radioactive decay, harmonic oscillators, and chemical reaction kinetics to advanced topics which include the simulation of neurons, chaotic systems such as a double-pendulum simulation and many more. In addition to these examples, it contains a chapter on special functions which can be used as "subroutines" in an analog computer setup.


Photonic Reservoir Computing

Photonic Reservoir Computing

Author: Daniel Brunner

Publisher: Walter de Gruyter GmbH & Co KG

Published: 2019-07-08

Total Pages: 276

ISBN-13: 3110583496

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Photonics has long been considered an attractive substrate for next generation implementations of machine-learning concepts. Reservoir Computing tremendously facilitated the realization of recurrent neural networks in analogue hardware. This concept exploits the properties of complex nonlinear dynamical systems, giving rise to photonic reservoirs implemented by semiconductor lasers, telecommunication modulators and integrated photonic chips.


Using Artificial Neural Networks for Analog Integrated Circuit Design Automation

Using Artificial Neural Networks for Analog Integrated Circuit Design Automation

Author: João P. S. Rosa

Publisher: Springer Nature

Published: 2019-12-11

Total Pages: 117

ISBN-13: 3030357430

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This book addresses the automatic sizing and layout of analog integrated circuits (ICs) using deep learning (DL) and artificial neural networks (ANN). It explores an innovative approach to automatic circuit sizing where ANNs learn patterns from previously optimized design solutions. In opposition to classical optimization-based sizing strategies, where computational intelligence techniques are used to iterate over the map from devices’ sizes to circuits’ performances provided by design equations or circuit simulations, ANNs are shown to be capable of solving analog IC sizing as a direct map from specifications to the devices’ sizes. Two separate ANN architectures are proposed: a Regression-only model and a Classification and Regression model. The goal of the Regression-only model is to learn design patterns from the studied circuits, using circuit’s performances as input features and devices’ sizes as target outputs. This model can size a circuit given its specifications for a single topology. The Classification and Regression model has the same capabilities of the previous model, but it can also select the most appropriate circuit topology and its respective sizing given the target specification. The proposed methodology was implemented and tested on two analog circuit topologies.


Advances in Neural Information Processing Systems 9

Advances in Neural Information Processing Systems 9

Author: Michael C. Mozer

Publisher: MIT Press

Published: 1997

Total Pages: 1128

ISBN-13: 9780262100656

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The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. It draws preeminent academic researchers from around the world and is widely considered to be a showcase conference for new developments in network algorithms and architectures. The broad range of interdisciplinary research areas represented includes neural networks and genetic algorithms, cognitive science, neuroscience and biology, computer science, AI, applied mathematics, physics, and many branches of engineering. Only about 30% of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. All of the papers presented appear in these proceedings.