Effective Mapping of Artificial Neural Network Algorithms Onto Massively Parallel Hardware PDF Download

Author: Guang Li
Publisher:
ISBN:
Category :
Languages : en
Pages : 19

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Book Description

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

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Book Description
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: V. Lakshmi Narasimhan
Publisher: Institute of Electrical & Electronics Engineers(IEEE)
ISBN:
Category : Computer algorithms
Languages : en
Pages : 512

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Book Description

Author: N. Sundararajan
Publisher: Wiley-IEEE Computer Society Press
ISBN:
Category : Computers
Languages : en
Pages : 424

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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:
Publisher:
ISBN:
Category : Computer programming
Languages : en
Pages : 510

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Book Description

Author: Qutaibah M. Malluhi
Publisher:
ISBN:
Category : Neural networks (Computer science)
Languages : en
Pages : 256

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Book Description

Author: K. Wojtek Przytula
Publisher: Prentice Hall
ISBN:
Category : Computers
Languages : en
Pages : 346

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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: IEEE Computer Society
Publisher:
ISBN:
Category : Computer algorithms
Languages : en
Pages : 504

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Book Description

Author: Linqian Liu
Publisher:
ISBN:
Category : Computer architecture
Languages : en
Pages : 168

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Book Description
Artificial neural network algorithms inherently possess fine-grain parallelism and offer the potential for fully distributed and local computation. A scalable hardware computing platform that can take advantage of such a massive parallelism and distributed computation attributes of artificial neural networks is considered to be well-poised to compute real-time solution of complex and large-scale problems. This thesis proposes a novel computing architecture for parallel and distributed computation where the hardware-software platform is the wireless sensor networks complete with its wireless protocol stack. More specifically, the proposed idea leverages the existing wireless sensor networks technology to serve as a hardware-software platform to implement and realize certain type of algorithms with fine-grain parallelism, such as those in the domain of artificial neural networks, in massively parallel and fully distributed mode. The research vision is to enable real time computation of solutions of large-scale and complex problems through the proposed parallel and distributed hardware realization of computational algorithms. The thesis defines the new parallel and distributed processing (PDP) and computing architecture and its application for artificial neural network computations. The underlying architectural principles, and structure of the proposed parallel and distributed computing platform are formulated and established. The proposed design is illustrated for feasibility through a simulation-based case study that leverages Kohonen's self-organizing map or SOM neural network on a number of different problem domains or data sets. The research study demonstrates mapping Kohonen's self-organizing map or SOM, configured for a set of domain specific problems, to the proposed PDP architecture. A comprehensive simulation study is conducted to assess the performance profile of and demonstrate the proposed computing architecture, with respect to feasibility. A wireless sensor network simulator (PROWLER) is employed for validation and performance assessment of the proposed computational framework. Three data sets, namely Alphanumeric or Text, Iris, and Wine, where each one differs in the number of attributes, instances, and clusters, are employed to profile the performance of the proposed computing platform. The simulation results are compared with those from the literature and through the MATLAB SOM toolbox. Comparative performance analysis suggests that the proposed computing platform is feasible and promising. The proposed design has potentially much wider applicability for problems with inherent fine-grain parallelism in various domains where mathematics-based problem-solving methodology is not applicable due to lack of a closed-form model for the process or system. Solving complex and very large-scale problems in real time is likely to have radical and ground-breaking impact on the entire spectrum of scientific, technological, economic and industrial endeavors enabling many solutions that were simply not feasible.

Author: Paul Werbos
Publisher: Routledge
ISBN: 1317713427
Category : Psychology
Languages : en
Pages : 860

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Book Description
Centered around 20 major topic areas of both theoretical and practical importance, the World Congress on Neural Networks provides its registrants -- from a diverse background encompassing industry, academia, and government -- with the latest research and applications in the neural network field.