Fpga Implementation Of Backpropagation Algorithm Of Artificial Neural Networks

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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.

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.

FANN-BACK: an FPGA-based Artificial Neural Network Trained by the Back Propagation Algorithm

Author : Royce Kimball Presley
Publisher :
ISBN 13 :
Total Pages : 404 pages
Book Rating : 4.:/5 (311 download)

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Book Synopsis FANN-BACK: an FPGA-based Artificial Neural Network Trained by the Back Propagation Algorithm by : Royce Kimball Presley

Download or read book FANN-BACK: an FPGA-based Artificial Neural Network Trained by the Back Propagation Algorithm written by Royce Kimball Presley and published by . This book was released on 1994 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Reconfigurable Back Propagation Neural Network Implementation for FPGA.

Author :
Publisher :
ISBN 13 :
Total Pages : 106 pages
Book Rating : 4.:/5 (817 download)

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Book Synopsis Reconfigurable Back Propagation Neural Network Implementation for FPGA. by :

Download or read book Reconfigurable Back Propagation Neural Network Implementation for FPGA. written by and published by . This book was released on 2008 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Artificial Neural Networks

Author : Joao Luis Garcia Rosa
Publisher : BoD – Books on Demand
ISBN 13 : 9535127047
Total Pages : 416 pages
Book Rating : 4.5/5 (351 download)

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Book Synopsis Artificial Neural Networks by : Joao Luis Garcia Rosa

Download or read book Artificial Neural Networks written by Joao Luis Garcia Rosa and published by BoD – Books on Demand. This book was released on 2016-10-19 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: The idea of simulating the brain was the goal of many pioneering works in Artificial Intelligence. The brain has been seen as a neural network, or a set of nodes, or neurons, connected by communication lines. Currently, there has been increasing interest in the use of neural network models. This book contains chapters on basic concepts of artificial neural networks, recent connectionist architectures and several successful applications in various fields of knowledge, from assisted speech therapy to remote sensing of hydrological parameters, from fabric defect classification to application in civil engineering. This is a current book on Artificial Neural Networks and Applications, bringing recent advances in the area to the reader interested in this always-evolving machine learning technique.

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.

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.

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.

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.

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:

Artificial Intelligence and Applied Mathematics in Engineering Problems

Author : D. Jude Hemanth
Publisher : Springer Nature
ISBN 13 : 3030361780
Total Pages : 1105 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Artificial Intelligence and Applied Mathematics in Engineering Problems by : D. Jude Hemanth

Download or read book Artificial Intelligence and Applied Mathematics in Engineering Problems written by D. Jude Hemanth and published by Springer Nature. This book was released on 2020-01-03 with total page 1105 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book features research presented at the 1st International Conference on Artificial Intelligence and Applied Mathematics in Engineering, held on 20–22 April 2019 at Antalya, Manavgat (Turkey). In today’s world, various engineering areas are essential components of technological innovations and effective real-world solutions for a better future. In this context, the book focuses on problems in engineering and discusses research using artificial intelligence and applied mathematics. Intended for scientists, experts, M.Sc. and Ph.D. students, postdocs and anyone interested in the subjects covered, the book can also be used as a reference resource for courses related to artificial intelligence and applied mathematics.

Proceedings of the 2009 International Conference on Signals, Systems and Automation (ICSSA 2009)

Author : Himanshu Soni
Publisher : Universal-Publishers
ISBN 13 : 1599428695
Total Pages : 362 pages
Book Rating : 4.5/5 (994 download)

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Book Synopsis Proceedings of the 2009 International Conference on Signals, Systems and Automation (ICSSA 2009) by : Himanshu Soni

Download or read book Proceedings of the 2009 International Conference on Signals, Systems and Automation (ICSSA 2009) written by Himanshu Soni and published by Universal-Publishers. This book was released on 2010-04-30 with total page 362 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of papers from the 2009 International Conference on Signals, Systems and Automation (ICSSA 2009). The conference at a glance: - Pre-conference Workshops/Tutorials on 27th Dec, 2009 - Five Plenary talks - Paper/Poster Presentation: 28-29 Dec, 2009 - Demonstrations by SKYVIEWInc, SLS Inc., BSNL, Baroda Electric Meters, SIS - On line paper submission facility on website - 200+ papers are received from India and abroad - Delegates from different countries including Poland, Iran, USA - Delegates from 16 states of India - Conference website is seen by more than 3000 persons across the world (27 countries and 120 cities)

Efficient Processing of Deep Neural Networks

Author : Vivienne Sze
Publisher : Springer Nature
ISBN 13 : 3031017668
Total Pages : 254 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Efficient Processing of Deep Neural Networks by : Vivienne Sze

Download or read book Efficient Processing of Deep Neural Networks written by Vivienne Sze and published by Springer Nature. This book was released on 2022-05-31 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Neural Networks and Statistical Learning

Author : Ke-Lin Du
Publisher : Springer Nature
ISBN 13 : 1447174526
Total Pages : 988 pages
Book Rating : 4.4/5 (471 download)

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Book Synopsis Neural Networks and Statistical Learning by : Ke-Lin Du

Download or read book Neural Networks and Statistical Learning written by Ke-Lin Du and published by Springer Nature. This book was released on 2019-09-12 with total page 988 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a broad yet detailed introduction to neural networks and machine learning in a statistical framework. A single, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with examples and exercises and allows readers to gain a practical working understanding of the content. This updated new edition presents recently published results and includes six new chapters that correspond to the recent advances in computational learning theory, sparse coding, deep learning, big data and cloud computing. Each chapter features state-of-the-art descriptions and significant research findings. The topics covered include: • multilayer perceptron; • the Hopfield network; • associative memory models;• clustering models and algorithms; • t he radial basis function network; • recurrent neural networks; • nonnegative matrix factorization; • independent component analysis; •probabilistic and Bayesian networks; and • fuzzy sets and logic. Focusing on the prominent accomplishments and their practical aspects, this book provides academic and technical staff, as well as graduate students and researchers with a solid foundation and comprehensive reference on the fields of neural networks, pattern recognition, signal processing, and machine learning.

Computational Intelligence in Optimization

Author : Yoel Tenne
Publisher : Springer Science & Business Media
ISBN 13 : 3642127754
Total Pages : 424 pages
Book Rating : 4.6/5 (421 download)

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Book Synopsis Computational Intelligence in Optimization by : Yoel Tenne

Download or read book Computational Intelligence in Optimization written by Yoel Tenne and published by Springer Science & Business Media. This book was released on 2010-06-30 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: This collection of recent studies spans a range of computational intelligence applications, emphasizing their application to challenging real-world problems. Covers Intelligent agent-based algorithms, Hybrid intelligent systems, Machine learning and more.

Advances in Computational Intelligence

Author : Ignacio Rojas
Publisher : Springer
ISBN 13 : 3319192221
Total Pages : 637 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Advances in Computational Intelligence by : Ignacio Rojas

Download or read book Advances in Computational Intelligence written by Ignacio Rojas and published by Springer. This book was released on 2015-06-05 with total page 637 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set LNCS 9094 and LNCS 9095 constitutes the thoroughly refereed proceedings of the 13th International Work-Conference on Artificial Neural Networks, IWANN 2015, held in Palma de Mallorca, Spain, in June 2013. The 99 revised full papers presented together with 1 invited talk were carefully reviewed and selected from 195 submissions. The papers are organized in topical sections on brain-computer interfaces: applications and tele-services; multi-robot systems: applications and theory (MRSAT); video and image processing; transfer learning; structures, algorithms and methods in artificial intelligence; interactive and cognitive environments; mathematical and theoretical methods in fuzzy systems; pattern recognition; embedded intelligent systems; expert systems; advances in computational intelligence; and applications of computational intelligence.

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

Author : Nan Zheng
Publisher : John Wiley & Sons
ISBN 13 : 1119507405
Total Pages : 389 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 389 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.