FPGA Implementations of Neural Networks
FPGA Implementations of Neural Networks

Author: Amos R. Omondi

Publisher: Springer Science & Business Media

Published: 2006-10-04

Total Pages: 365

ISBN-13: 0387284877

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During the 1980s and early 1990s there was signi?cant work in the design and implementation of hardware neurocomputers. Nevertheless, most of these efforts may be judged to have been unsuccessful: at no time have have ha- ware neurocomputers been in wide use. This lack of success may be largely attributed to the fact that earlier work was almost entirely aimed at developing custom neurocomputers, based on ASIC technology, but for such niche - eas this technology was never suf?ciently developed or competitive enough to justify large-scale adoption. On the other hand, gate-arrays of the period m- tioned were never large enough nor fast enough for serious arti?cial-neur- network (ANN) applications. But technology has now improved: the capacity and performance of current FPGAs are such that they present a much more realistic alternative. Consequently neurocomputers based on FPGAs are now a much more practical proposition than they have been in the past. This book summarizes some work towards this goal and consists of 12 papers that were selected, after review, from a number of submissions. The book is nominally divided into three parts: Chapters 1 through 4 deal with foundational issues; Chapters 5 through 11 deal with a variety of implementations; and Chapter 12 looks at the lessons learned from a large-scale project and also reconsiders design issues in light of current and future technology.

Field-Programmable Logic and Applications
Field-Programmable Logic and Applications

Author: Peter Y.K. Cheung

Publisher: Springer Science & Business Media

Published: 2003-08-27

Total Pages: 1204

ISBN-13: 3540408223

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This book constitutes the refereed proceedings of the 13th International Conference on Field-Programmable Logic and Applications, FPL 2003, held in Lisbon, Portugal in September 2003. The 90 revised full papers and 56 revised poster papers presented were carefully reviewed and selected from 216 submissions. The papers are organized in topical sections on technologies and trends, communications applications, high level design tools, reconfigurable architecture, cryptographic applications, multi-context FPGAs, low-power issues, run-time reconfiguration, compilation tools, asynchronous techniques, bio-related applications, codesign, reconfigurable fabrics, image processing applications, SAT techniques, application-specific architectures, DSP applications, dynamic reconfiguration, SoC architectures, emulation, cache design, arithmetic, bio-inspired design, SoC design, cellular applications, fault analysis, and network applications.

FPGA Implementations of Neural Networks
FPGA Implementations of Neural Networks

Author: Amos R. Omondi

Publisher: Springer

Published: 2008-11-01

Total Pages: 0

ISBN-13: 9780387509167

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During the 1980s and early 1990s there was signi?cant work in the design and implementation of hardware neurocomputers. Nevertheless, most of these efforts may be judged to have been unsuccessful: at no time have have ha- ware neurocomputers been in wide use. This lack of success may be largely attributed to the fact that earlier work was almost entirely aimed at developing custom neurocomputers, based on ASIC technology, but for such niche - eas this technology was never suf?ciently developed or competitive enough to justify large-scale adoption. On the other hand, gate-arrays of the period m- tioned were never large enough nor fast enough for serious arti?cial-neur- network (ANN) applications. But technology has now improved: the capacity and performance of current FPGAs are such that they present a much more realistic alternative. Consequently neurocomputers based on FPGAs are now a much more practical proposition than they have been in the past. This book summarizes some work towards this goal and consists of 12 papers that were selected, after review, from a number of submissions. The book is nominally divided into three parts: Chapters 1 through 4 deal with foundational issues; Chapters 5 through 11 deal with a variety of implementations; and Chapter 12 looks at the lessons learned from a large-scale project and also reconsiders design issues in light of current and future technology.

Application of FPGA to Real‐Time Machine Learning
Application of FPGA to Real‐Time Machine Learning

Author: Piotr Antonik

Publisher: Springer

Published: 2018-05-18

Total Pages: 187

ISBN-13: 3319910531

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This book lies at the interface of machine learning – a subfield of computer science that develops algorithms for challenging tasks such as shape or image recognition, where traditional algorithms fail – and photonics – the physical science of light, which underlies many of the optical communications technologies used in our information society. It provides a thorough introduction to reservoir computing and field-programmable gate arrays (FPGAs). Recently, photonic implementations of reservoir computing (a machine learning algorithm based on artificial neural networks) have made a breakthrough in optical computing possible. In this book, the author pushes the performance of these systems significantly beyond what was achieved before. By interfacing a photonic reservoir computer with a high-speed electronic device (an FPGA), the author successfully interacts with the reservoir computer in real time, allowing him to considerably expand its capabilities and range of possible applications. Furthermore, the author draws on his expertise in machine learning and FPGA programming to make progress on a very different problem, namely the real-time image analysis of optical coherence tomography for atherosclerotic arteries.

Fpga Implementation of Hopfield Neural Network
Fpga Implementation of Hopfield Neural Network

Author: Avvaru Srinivasulu

Publisher: LAP Lambert Academic Publishing

Published: 2012-03

Total Pages: 76

ISBN-13: 9783848435456

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This work was to establish whether it was possible to achieve a reasonable speedup by implementing FPGA based Hopfield neural networks for some simple constraint satisfaction problems. The results are significant - our initial implementation using standard Xilinx FPGAs yielded 2-3 orders of magnitude speedup over the Sun Blade 2000 workstation comes with 1.2-GHz version of the 64-bit UltraSPARC III Cu processor. The main problem with the work to date is that the problems are both unrealistically small and simplistic. That is the constraints on the N-Queen problem are simpler than those found in many real world scheduling applications. Thus, it is not clear whether we will be able to optimize the neuron structure for more complex problems since the weights matrix may not contain as many zero elements. Thus a new method for speed improvement of Hopfield neural networks for solving constraint satisfaction problems using Field Programmable Gate Arrays (FPGAs) was proposed and implemented.

Reconfigurable Computing: Architectures and Applications
Reconfigurable Computing: Architectures and Applications

Author: Koen Bertels

Publisher: Springer Science & Business Media

Published: 2006-07-26

Total Pages: 484

ISBN-13: 354036708X

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This book constitutes the thoroughly refereed post-proceedings of the Second International Workshop on Reconfigurable Computing, ARC 2006, held in Delft, The Netherlands, in March 2006. The 22 revised full papers and 35 revised short papers presented were thoroughly reviewed and selected from 95 submissions. The papers are organized in topical sections on applications, power, image processing, organization and architecture, networks and communication, security, and tools.

FPGA Implementation of PSO Algorithm and Neural Networks
FPGA Implementation of PSO Algorithm and Neural Networks

Author: Parviz Michael Palangpour

Publisher:

Published: 2010

Total Pages: 0

ISBN-13:

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"This thesis describes the Field Programmable Gate Array (FPGA) implementations of two powerful techniques of Computational Intelligence (CI), the Particle Swarm Optimization algorithm (PSO) and the Neural Network (NN). Particle Swarm Optimization (PSO) is a popular population-based optimization algorithm. While PSO has been shown to perform well in a large variety of problems, PSO is typically implemented in software. Population-based optimization algorithms such as PSO are well suited for execution in parallel stages. This allows PSO to be implemented directly in hardware and achieve much faster execution times than possible in software. In this thesis, a pipelined architecture for hardware PSO implementation is presented. Benchmark functions solved by software and FPGA hardware PSO implementations are compared. NNs are inherently parallel, with each layer of neurons processing incoming data independently of each other. While general purpose processors have reached impressive processing speeds, they still cannot fully exploit this inherent parallelism due to their sequential architecture. In order to achieve the high neural network throughput needed for real-time applications, a custom hardware design is needed. In this thesis, a digital implementation of an NN is developed for FPGA implementation. The hardware PSO implementation is designed using only VHDL, while the NN hardware implementation is designed using Xilinx System Generator. Both designs are synthesized using Xilinx ISE and implemented on the Xilinx Virtex-II Pro FPGA Development Kit"--Abstract, leaf iii

Advances on P2P, Parallel, Grid, Cloud and Internet Computing
Advances on P2P, Parallel, Grid, Cloud and Internet Computing

Author: Leonard Barolli

Publisher: Springer Nature

Published: 2019-10-19

Total Pages: 963

ISBN-13: 3030335097

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This book presents the latest research findings, innovative research results, methods and development techniques related to P2P, grid, cloud and Internet computing from both theoretical and practical perspectives. It also reveals the synergies among such large-scale computing paradigms. P2P, grid, cloud and Internet computing technologies have rapidly become established as breakthrough paradigms for solving complex problems by enabling aggregation and sharing of an increasing variety of distributed computational resources at large scale. Grid computing originated as a paradigm for high-performance computing, as an alternative to expensive supercomputers through different forms of large-scale distributed computing. P2P computing emerged as a new paradigm after client–server and web-based computing and has proved useful in the development of social networking, B2B (business to business), B2C (business to consumer), B2G (business to government), and B2E (business to employee). Cloud computing has been defined as a “computing paradigm where the boundaries of computing are determined by economic rationale rather than technical limits,” and it has fast become a computing paradigm with applicability and adoption in all application domains and which provides utility computing at a large scale. Lastly, Internet computing is the basis of any large-scale distributed computing paradigms; it has developed into a vast area of flourishing fields with enormous impact on today’s information societies, and serving as a universal platform comprising a large variety of computing forms such as grid, P2P, cloud and mobile computing.

Neural Information Processing
Neural Information Processing

Author: Irwin King

Publisher: Springer Science & Business Media

Published: 2006

Total Pages: 1248

ISBN-13: 3540464840

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Annotation The three volume set LNCS 4232, LNCS 4233, and LNCS 4234 constitutes the refereed proceedings of the 13th International Conference on Neural Information Processing, ICONIP 2006, held in Hong Kong, China in October 2006. The 386 revised full papers presented were carefully reviewed and selected from 1175 submissions. The 126 papers of the first volume are organized in topical sections on neurobiological modeling and analysis, cognitive processing, mathematical modeling and analysis, learning algorithms, support vector machines, self-organizing maps, as well as independent component analysis and blind source separation. The second volume contains 128 contributions related to pattern classification, face analysis and processing, image processing, signal processing, computer vision, data pre-processing, forecasting and prediction, as well as neurodynamic and particle swarm optimization. The third volume offers 131 papers that deal with bioinformatics and biomedical applications, information security, data and text processing, financial applications, manufacturing systems, control and robotics, evolutionary algorithms and systems, fuzzy systems, and hardware implementations.