Implementation Of An Fpga Based Artificial Neural Network

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FPGA Implementations of Neural Networks

Author : Amos R. Omondi
Publisher : Springer Science & Business Media
ISBN 13 : 0387284877
Total Pages : 365 pages
Book Rating : 4.3/5 (872 download)

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Book Synopsis FPGA Implementations of Neural Networks by : Amos R. Omondi

Download or read book FPGA Implementations of Neural Networks written by Amos R. Omondi and published by Springer Science & Business Media. This book was released on 2006-10-04 with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Implementation of an FPGA-based Artificial Neural Network

Author : Luya Gao
Publisher :
ISBN 13 :
Total Pages : 33 pages
Book Rating : 4.:/5 (113 download)

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Book Synopsis Implementation of an FPGA-based Artificial Neural Network by : Luya Gao

Download or read book Implementation of an FPGA-based Artificial Neural Network written by Luya Gao and published by . This book was released on 2019 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Implementation of FPGA-Based Artificial Neural Network Combined with Genetic Algorithm

Author :
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (853 download)

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Book Synopsis Implementation of FPGA-Based Artificial Neural Network Combined with Genetic Algorithm by :

Download or read book Implementation of FPGA-Based Artificial Neural Network Combined with Genetic Algorithm written by and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Implementation of FPGA-based Artificial Neural Network for Character Recognition

Author : Omar Sadeq Salman
Publisher :
ISBN 13 :
Total Pages : 104 pages
Book Rating : 4.:/5 (11 download)

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Book Synopsis Implementation of FPGA-based Artificial Neural Network for Character Recognition by : Omar Sadeq Salman

Download or read book Implementation of FPGA-based Artificial Neural Network for Character Recognition written by Omar Sadeq Salman and published by . This book was released on 2014 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: This project may be seen as a place to begin for learning Artificial Neural Network (ANN).

Field-Programmable Logic and Applications

Author : Peter Y.K. Cheung
Publisher : Springer Science & Business Media
ISBN 13 : 3540408223
Total Pages : 1204 pages
Book Rating : 4.5/5 (44 download)

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Book Synopsis Field-Programmable Logic and Applications by : Peter Y.K. Cheung

Download or read book Field-Programmable Logic and Applications written by Peter Y.K. Cheung and published by Springer Science & Business Media. This book was released on 2003-08-27 with total page 1204 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Neural Information Processing

Author : Irwin King
Publisher : Springer Science & Business Media
ISBN 13 : 3540464840
Total Pages : 1248 pages
Book Rating : 4.5/5 (44 download)

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Book Synopsis Neural Information Processing by : Irwin King

Download or read book Neural Information Processing written by Irwin King and published by Springer Science & Business Media. This book was released on 2006 with total page 1248 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Application of FPGA to Real‐Time Machine Learning

Author : Piotr Antonik
Publisher : Springer
ISBN 13 : 3319910531
Total Pages : 187 pages
Book Rating : 4.3/5 (199 download)

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Book Synopsis Application of FPGA to Real‐Time Machine Learning by : Piotr Antonik

Download or read book Application of FPGA to Real‐Time Machine Learning written by Piotr Antonik and published by Springer. This book was released on 2018-05-18 with total page 187 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Intelligent Neural Network Control System Design and FPGA Based Implementation

Author : Jonathan Turner
Publisher :
ISBN 13 :
Total Pages : 114 pages
Book Rating : 4.:/5 (812 download)

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Book Synopsis Intelligent Neural Network Control System Design and FPGA Based Implementation by : Jonathan Turner

Download or read book Intelligent Neural Network Control System Design and FPGA Based Implementation written by Jonathan Turner and published by . This book was released on 2012 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: Author's abstract: This work documents a study of intelligent neural network control system design and implementation for engineering applications. In this study, the effectiveness of single multiplicative neuron (SMN) in place of traditional multi-layer perceptron (MLP) is investigated. The objectives were to (i) verify the feasibility of SMN based control systems, (ii) quantitatively compare the performance of SMN and MLP based systems, (iii) determine the amount of computation that could be saved by using SMN instead of MLP in a control system, and (iv) determine the performance of a SMN in an adaptive critic design (ACD) using action dependent heuristic dynamic programming (ADHDP). It was hypothesized that the replacement of a MLP network with a SMN would result in a controller that would achieve the same control quality in a less processor intensive manner, possibly allowing controller implementation on a less costly computer or microcontroller system. Controllers featuring the MLP and the SMN were implemented in LabVIEW for two physical systems and compared based on their ability to accurately control the system response when given complex reference inputs. The SMN based control systems were implemented with both off-line and on-line training using conventional and field programmable gate array (FPGA) based data acquisition hardware. The controllers were also compared based on the number of calculations required to complete the artificial neural network (ANN) related sections of the control loop. The SMN based control systems were found to perform as well as, if not better than their MLP based counterparts, in all cases studied, while significantly reducing required computations. The SMN was finally implemented in an intelligent controller based on ADHDP and found to perform better than conventional controllers like PID with a periodic disturbance.

2018 5th NAFOSTED Conference on Information and Computer Science (NICS)

Author : IEEE Staff
Publisher :
ISBN 13 : 9781538679845
Total Pages : pages
Book Rating : 4.6/5 (798 download)

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Book Synopsis 2018 5th NAFOSTED Conference on Information and Computer Science (NICS) by : IEEE Staff

Download or read book 2018 5th NAFOSTED Conference on Information and Computer Science (NICS) written by IEEE Staff and published by . This book was released on 2018-11-23 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The 5th NAFOSTED Conference on Information and Computer Science (NICS) 2018 is an international conference It aims to build a durable, innovative and conducive forum for international researchers to present and discuss recent advancements and future directions in the field of information and computer science

A Simple FPGA-based Architecture Design of Reconfigurable Neural Network

Author : Jaber Salem
Publisher :
ISBN 13 :
Total Pages : 160 pages
Book Rating : 4.:/5 (878 download)

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Book Synopsis A Simple FPGA-based Architecture Design of Reconfigurable Neural Network by : Jaber Salem

Download or read book A Simple FPGA-based Architecture Design of Reconfigurable Neural Network written by Jaber Salem and published by . This book was released on 2013 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: In contrast with analog design, digital design and implementation of any logic circuit suffer much from the difficulty in terms of economy and implementation. Neural networks are artificial systems inspired by the brain's cognitive behavior, which can learn tasks with some degree of complexity, such as, optimization problems, text and speech recognition. Since the topology of neural networks is highly crucial to the performance, the reconfigurable ability of the neural network hardware is very essential. Reconfigurability factually means several different designs can be implemented on a single architecture. Therefore, this work proposes an efficient architecture to implement the reconfigurable back propagation and Hopfield neural networks. We specifically adopted the reconfigurable artificial neural networks approach to show how it is possible to build an efficient chip. Simple neural network models with an appropriate training were used to behave as traditional logic functions in the bit- level. In order to further reduce the hardware, memories-sharing method has been adopted. Also, a comparison between the proposed and traditional networks shows that the proposed network has significantly reduced the time delay and power consumption. Xilinx - ISE is used to synthesize our design. VHDL code is used to build the architecture. The architecture code is then downloaded to FPGAs (Field Programmable Gate Array) to implement the design. FPGAs are strong tools to implement ANNs as one can exploit concurrency and rapidly reconfigure to adapt the weights and topologies of an ANN. Also, XPower, as one of the best tools in Xilinx, was used to measure the total required power by our architecture. Finally, the results showed that the proposed reconfigurable architecture leads to a considerable decrease in the consumed power to almost 43% as well as the total time delay. Also, the architecture can easily be scalable as a future work and is able to cope with several network sizes with the same hardware.

Engineering Applications of FPGAs

Author : Esteban Tlelo-Cuautle
Publisher : Springer
ISBN 13 : 3319341154
Total Pages : 230 pages
Book Rating : 4.3/5 (193 download)

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Book Synopsis Engineering Applications of FPGAs by : Esteban Tlelo-Cuautle

Download or read book Engineering Applications of FPGAs written by Esteban Tlelo-Cuautle and published by Springer. This book was released on 2016-05-28 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers readers a clear guide to implementing engineering applications with FPGAs, from the mathematical description to the hardware synthesis, including discussion of VHDL programming and co-simulation issues. Coverage includes FPGA realizations such as: chaos generators that are described from their mathematical models; artificial neural networks (ANNs) to predict chaotic time series, for which a discussion of different ANN topologies is included, with different learning techniques and activation functions; random number generators (RNGs) that are realized using different chaos generators, and discussions of their maximum Lyapunov exponent values and entropies. Finally, optimized chaotic oscillators are synchronized and realized to implement a secure communication system that processes black and white and grey-scale images. In each application, readers will find VHDL programming guidelines and computer arithmetic issues, along with co-simulation examples with Active-HDL and Simulink. The whole book provides a practical guide to implementing a variety of engineering applications from VHDL programming and co-simulation issues, to FPGA realizations of chaos generators, ANNs for chaotic time-series prediction, RNGs and chaotic secure communications for image transmission.

Fpga Implementation of Hopfield Neural Network

Author : Avvaru Srinivasulu
Publisher : LAP Lambert Academic Publishing
ISBN 13 : 9783848435456
Total Pages : 76 pages
Book Rating : 4.4/5 (354 download)

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Book Synopsis Fpga Implementation of Hopfield Neural Network by : Avvaru Srinivasulu

Download or read book Fpga Implementation of Hopfield Neural Network written by Avvaru Srinivasulu and published by LAP Lambert Academic Publishing. This book was released on 2012-03 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Hardware Implementation of Artificial Neural Network on Fpga for Sulfate-reducing Bacteria Detection Based on Culture Method

Author : Earn Tzeh Tan
Publisher :
ISBN 13 :
Total Pages : 232 pages
Book Rating : 4.:/5 (96 download)

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Book Synopsis Hardware Implementation of Artificial Neural Network on Fpga for Sulfate-reducing Bacteria Detection Based on Culture Method by : Earn Tzeh Tan

Download or read book Hardware Implementation of Artificial Neural Network on Fpga for Sulfate-reducing Bacteria Detection Based on Culture Method written by Earn Tzeh Tan and published by . This book was released on 2014 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt:

2021 29th Signal Processing and Communications Applications Conference (SIU)

Author : IEEE Staff
Publisher :
ISBN 13 : 9781665436502
Total Pages : pages
Book Rating : 4.4/5 (365 download)

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Book Synopsis 2021 29th Signal Processing and Communications Applications Conference (SIU) by : IEEE Staff

Download or read book 2021 29th Signal Processing and Communications Applications Conference (SIU) written by IEEE Staff and published by . This book was released on 2021-06-09 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Signal Processing and Communications Applications Conference (S U) is one of the most prominent scientific events in Turkey During the conference, researchers share their novel contributions to scientific and technological development

Design of a Neural Network for FPGA Implementation

Author : Ee Ric Lim
Publisher :
ISBN 13 :
Total Pages : 117 pages
Book Rating : 4.:/5 (96 download)

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Book Synopsis Design of a Neural Network for FPGA Implementation by : Ee Ric Lim

Download or read book Design of a Neural Network for FPGA Implementation written by Ee Ric Lim and published by . This book was released on 2013 with total page 117 pages. Available in PDF, EPUB and Kindle. Book excerpt:

FPGA Implementation of Backpropagation Algorithm of Artificial Neural Networks

Author :
Publisher :
ISBN 13 :
Total Pages : 54 pages
Book Rating : 4.:/5 (12 download)

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Book Synopsis FPGA Implementation of Backpropagation Algorithm of Artificial Neural Networks by :

Download or read book FPGA Implementation of Backpropagation Algorithm of Artificial Neural Networks written by and published by . This book was released on 2017 with total page 54 pages. Available in PDF, EPUB and Kindle. Book excerpt: Back-Propagation (BP) Algorithm is one of the efficient learning algorithms for the training of Artificial Neural Networks (ANN). The efficient hardware implementation of the BP Algorithm can find its application in the broad field of applications. The common computing platforms to build the BP algorithm based ANN Systems are Application Specific Integrated Circuits (ASICs) and General-Purpose Processors (GPP) based computers. However, due to a high demand of maintaining a trade-off between performance and flexibility, such computing machines become a bottleneck for further advanced improvements. In the last few decades, there has been significant progress in the field of Field Programmable Gate Arrays (FPGAs), which are based on the reconfigurable hardware platform. One of the main advantages of FPGAs are its flexibility, it is possible to reprogram the same hardware and achieve good performance by allowing parallel computation at the same time. The focus of this thesis is to implement the BP algorithm based ANN system on reconfigurable platform(FPGA). The proposed designs are coded on the software platform, MATLAB and in Verilog Hardware Description Language (Verilog HDL) on FPGA and synthesized on artix-7 FPGA evaluation kit. The validation of the design is verified on two benchmarks and comparisons are observed and discussed between two platforms.

Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design

Author : Nan Zheng
Publisher : John Wiley & Sons
ISBN 13 : 1119507391
Total Pages : 296 pages
Book Rating : 4.1/5 (195 download)

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Book Synopsis Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design by : Nan Zheng

Download or read book Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design written by Nan Zheng and published by John Wiley & Sons. This book was released on 2019-10-18 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explains current co-design and co-optimization methodologies for building hardware neural networks and algorithms for machine learning applications This book focuses on how to build energy-efficient hardware for neural networks with learning capabilities—and provides co-design and co-optimization methodologies for building hardware neural networks that can learn. Presenting a complete picture from high-level algorithm to low-level implementation details, Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design also covers many fundamentals and essentials in neural networks (e.g., deep learning), as well as hardware implementation of neural networks. The book begins with an overview of neural networks. It then discusses algorithms for utilizing and training rate-based artificial neural networks. Next comes an introduction to various options for executing neural networks, ranging from general-purpose processors to specialized hardware, from digital accelerator to analog accelerator. A design example on building energy-efficient accelerator for adaptive dynamic programming with neural networks is also presented. An examination of fundamental concepts and popular learning algorithms for spiking neural networks follows that, along with a look at the hardware for spiking neural networks. Then comes a chapter offering readers three design examples (two of which are based on conventional CMOS, and one on emerging nanotechnology) to implement the learning algorithm found in the previous chapter. The book concludes with an outlook on the future of neural network hardware. Includes cross-layer survey of hardware accelerators for neuromorphic algorithms Covers the co-design of architecture and algorithms with emerging devices for much-improved computing efficiency Focuses on the co-design of algorithms and hardware, which is especially critical for using emerging devices, such as traditional memristors or diffusive memristors, for neuromorphic computing Learning in Energy-Efficient Neuromorphic Computing: Algorithm and Architecture Co-Design is an ideal resource for researchers, scientists, software engineers, and hardware engineers dealing with the ever-increasing requirement on power consumption and response time. It is also excellent for teaching and training undergraduate and graduate students about the latest generation neural networks with powerful learning capabilities.