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Author : Wassim Mansour
Publisher :
ISBN 13 :
Total Pages : 90 pages
Book Rating : 4.:/5 (11 download)
Download or read book Efficient Implementation of Hopfield Neural Network on FPGA written by Wassim Mansour and published by . This book was released on 2007 with total page 90 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Amos R. Omondi
Publisher : Springer Science & Business Media
ISBN 13 : 0387284877
Total Pages : 365 pages
Book Rating : 4.3/5 (872 download)
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.
Author : Avvaru Srinivasulu
Publisher : LAP Lambert Academic Publishing
ISBN 13 : 9783848435456
Total Pages : 76 pages
Book Rating : 4.4/5 (354 download)
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.
Author : Jaber Salem
Publisher :
ISBN 13 :
Total Pages : 160 pages
Book Rating : 4.:/5 (878 download)
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.
Author : Nan Zheng
Publisher : John Wiley & Sons
ISBN 13 : 1119507405
Total Pages : 389 pages
Book Rating : 4.1/5 (195 download)
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.
Author : Md Kamruzzaman Shuvo
Publisher :
ISBN 13 :
Total Pages : 98 pages
Book Rating : 4.:/5 (124 download)
Download or read book Hardware Efficient Deep Neural Network Implementation on FPGA written by Md Kamruzzaman Shuvo and published by . This book was released on 2020 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, there has been a significant push to implement Deep Neural Networks (DNNs) on edge devices, which requires power and hardware efficient circuits to carry out the intensive matrix-vector multiplication (MVM) operations. This work presents hardware efficient MVM implementation techniques using bit-serial arithmetic and a novel MSB first computation circuit. The proposed designs take advantage of the pre-trained network weight parameters, which are already known in the design stage. Thus, the partial computation results can be pre-computed and stored into look-up tables. Then the MVM results can be computed in a bit-serial manner without using multipliers. The proposed novel circuit implementation for convolution filters and rectified linear activation function used in deep neural networks conducts computation in an MSB-first bit-serial manner. It can predict earlier if the outcomes of filter computations will be negative and subsequently terminate the remaining computations to save power. The benefits of using the proposed MVM implementations techniques are demonstrated by comparing the proposed design with conventional implementation. The proposed circuit is implemented on an FPGA. It shows significant power and performance improvements compared to the conventional designs implemented on the same FPGA.
Author : Jason Moore
Publisher :
ISBN 13 :
Total Pages : 122 pages
Book Rating : 4.:/5 (343 download)
Download or read book Design and Implementation of an Expandable Hopfield Neural Network Using VHDL Behavioral and Structural Modeling written by Jason Moore and published by . This book was released on 1995 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author :
Publisher :
ISBN 13 :
Total Pages : 54 pages
Book Rating : 4.:/5 (12 download)
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.
Author : Marek Ponca
Publisher :
ISBN 13 :
Total Pages : 138 pages
Book Rating : 4.:/5 (255 download)
Download or read book Toward Efficient Implementation of Artificial Neural Networks in Systems on Chip written by Marek Ponca and published by . This book was released on 2006 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: Der Mangel an effektiv auf einem Chip implementierten künstlichen neuronalen Netze fordert einen neuen Ansatz. Die Ausnutzung der physikalischen Effekte, die uns die zur Realisierung benutzten Halbleitertechnologien bieten, können zum Beispiel für die flächensparende Implementierung genutzt werden. Neuronale Netze als integrierter Bestandteil komplexer Systeme können nur dann eingebettet werden, wenn die Voraussetzungen für eine kompakte Realisierung erfüllt sind. Dazu zählt vor allem eine flächensparende Implementierung aller Komponenten.Diese Arbeit behandelt neue Ansätze zur Implementierung künstlicher neuronaler Netze in digitaler Hardware. Da diese meistens wesentlich mehr Chipfläche bedürfen, dafür aber mit wesentlich höherer Bandbreite und Datenraten arbeiten, wird es immer den Bedarf an platzoptimierten Realisierungen geben. Im weiteren wird eine Vorgehensweise für die Implemetierung temporärer Dynamik neuronaler Potentiale mit minimierter Ressourcen-Ausnutzung präsentiert. Als Beispielapplikation wurde ein System für die Schallquellenlokalisierung benutzt.Zusätzlich wurde eine Methode entwickelt, mit der es möglich ist, die Hardware-Realisierungen der künstlichen neuronalen Netze miteinander zu vergleichen und objektiv zu bewerten. Verschiedene Kriterien und Ansichtsweisen können dabei in Betracht gezogen werden, z.B. ob der Wert auf der Genauigkeit der Nachbildung liegt oder ob eher die Rechenleistung im Vordergrund steht.
Author : Amit Parasmal Borundiya
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (233 download)
Download or read book Implementation of Hopfield Neural Network Using Double Gate MOSFET written by Amit Parasmal Borundiya and published by . This book was released on 2008 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author : Marek Ponca
Publisher :
ISBN 13 : 9783938843239
Total Pages : 131 pages
Book Rating : 4.8/5 (432 download)
Download or read book Towards Efficient Implementation of Artifical Neural Networks in Systems on Chip written by Marek Ponca and published by . This book was released on 2007 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: Der Mangel an effektiv auf einem Chip implementierten künstlichen neuronalen Netze fordert einen neuen Ansatz. Die Ausnutzung der physikalischen Effekte, die uns die zur Realisierung benutzten Halbleitertechnologien bieten, können zum Beispiel für die flächensparende Implementierung genutzt werden. Neuronale Netze als integrierter Bestandteil komplexer Systeme können nur dann eingebettet werden, wenn die Voraussetzungen für eine kompakte Realisierung erfüllt sind. Dazu zählt vor allem eine flächensparende Implementierung aller Komponenten.Diese Arbeit behandelt neue Ansätze zur Implementierung künstlicher neuronaler Netze in digitaler Hardware. Da diese meistens wesentlich mehr Chipfläche bedürfen, dafür aber mit wesentlich höherer Bandbreite und Datenraten arbeiten, wird es immer den Bedarf an platzoptimierten Realisierungen geben. Im weiteren wird eine Vorgehensweise für die Implemetierung temporärer Dynamik neuronaler Potentiale mit minimierter Ressourcen-Ausnutzung präsentiert. Als Beispielapplikation wurde ein System für die Schallquellenlokalisierung benutzt.Zusätzlich wurde eine Methode entwickelt, mit der es möglich ist, die Hardware-Realisierungen der künstlichen neuronalen Netze miteinander zu vergleichen und objektiv zu bewerten. Verschiedene Kriterien und Ansichtsweisen können dabei in Betracht gezogen werden, z.B. ob der Wert auf der Genauigkeit der Nachbildung liegt oder ob eher die Rechenleistung im Vordergrund steht.
Author : Danielle Tchuinkou Kwadjo
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (137 download)
Download or read book Towards Efficient Processing of Large Scale Deep Neural Network on Multi-FPGAs Platform written by Danielle Tchuinkou Kwadjo and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: parallelism within the resource budget, while maintaining a balanced rate between the layers. Then, we proceed with a multi-level graph partitioning that integrates Coarse-Grained floorplanning for high-quality and scalable routability-drive placement of CNN's components on the FPGAs. Prototyping results achieve an overall 37% higher frequency, better energy efficiency, and lower resource usage when compared to a baseline implementation on the same number of FPGAs.
Author : Věra Kůrková
Publisher : Springer Science & Business Media
ISBN 13 : 3540875581
Total Pages : 1012 pages
Book Rating : 4.5/5 (48 download)
Download or read book Artificial Neural Networks - ICANN 2008 written by Věra Kůrková and published by Springer Science & Business Media. This book was released on 2008 with total page 1012 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two volume set LNCS 5163 and LNCS 5164 constitutes the refereed proceedings of the 18th International Conference on Artificial Neural Networks, ICANN 2008, held in Prague Czech Republic, in September 2008. The 200 revised full papers presented were carefully reviewed and selected from more than 300 submissions. The second volume is devoted to pattern recognition and data analysis, hardware and embedded systems, computational neuroscience, connectionistic cognitive science, neuroinformatics and neural dynamics. it also contains papers from two special sessions coupling, synchronies, and firing patterns: from cognition to disease, and constructive neural networks and two workshops new trends in self-organization and optimization of artificial neural networks, and adaptive mechanisms of the perception-action cycle.
Author : Wlodzislaw Duch
Publisher : Springer Science & Business Media
ISBN 13 : 3540287558
Total Pages : 1051 pages
Book Rating : 4.5/5 (42 download)
Download or read book Artificial Neural Networks: Formal Models and Their Applications – ICANN 2005 written by Wlodzislaw Duch and published by Springer Science & Business Media. This book was released on 2005-08-31 with total page 1051 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two volume set LNCS 3696 and LNCS 3697 constitutes the refereed proceedings of the 15th International Conference on Artificial Neural Networks, ICANN 2005, held in Warsaw, Poland in September 2005. The over 600 papers submitted to ICANN 2005 were thoroughly reviewed and carefully selected for presentation. The first volume includes 106 contributions related to Biological Inspirations; topics addressed are modeling the brain and cognitive functions, development of cognitive powers in embodied systems spiking neural networks, associative memory models, models of biological functions, projects in the area of neuroIT, evolutionary and other biological inspirations, self-organizing maps and their applications, computer vision, face recognition and detection, sound and speech recognition, bioinformatics, biomedical applications, and information- theoretic concepts in biomedical data analysis. The second volume contains 162 contributions related to Formal Models and their Applications and deals with new neural network models, supervised learning algorithms, ensemble-based learning, unsupervised learning, recurent neural networks, reinforcement learning, bayesian approaches to learning, learning theory, artificial neural networks for system modeling, decision making, optimalization and control, knowledge extraction from neural networks, temporal data analysis, prediction and forecasting, support vector machines and kernel-based methods, soft computing methods for data representation, analysis and processing, data fusion for industrial, medical and environmental applications, non-linear predictive models for speech processing, intelligent multimedia and semantics, applications to natural language processing, various applications, computational intelligence in games, and issues in hardware implementation.
Author : Reiner W. Hartenstein
Publisher : Springer Science & Business Media
ISBN 13 : 9783540584193
Total Pages : 452 pages
Book Rating : 4.5/5 (841 download)
Download or read book Field-Programmable Logic: Architectures, Synthesis and Applications written by Reiner W. Hartenstein and published by Springer Science & Business Media. This book was released on 1994-08-24 with total page 452 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains the proceedings of the 4th International Workshop on Field-Programmable Logic and Applications (FPL '94), held in Prague, Czech Republic in September 1994. The growing importance of field-programmable devices is substantiated by the remarkably high number of 116 submissions for FPL '94; from them, the revised versions of 40 full papers and 24 high-quality poster presentations were accepted for inclusion in this volume. Among the topics treated are: testing, layout, synthesis tools, compilation research and CAD, trade-offs and experience, innovations and smart applications, FPGA-based computer architectures, high-level design, prototyping and ASIC emulators, commercial devices, new tools, CCMs and HW/SW co-design, modelers, educational experience, and novel architectures.
Author : Akshay Raju Krishnani
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (12 download)
Download or read book In-situ Implementation and Training of Convolutional Neural Network on FPGAs written by Akshay Raju Krishnani and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The main objective of this thesis is to investigate the efficiency of in-situ trainable Convolutional Neural Networks (CNNs) on modern programmable System-on-Chip (SoC) Field Programmable Gate Arrays (FPGAs) composed of embedded processors and reconfigurable fabric and to study the robustness of the system when faults happen. One particular characteristic of this work is that CNN is developed exclusively using High-Level Synthesis (HLS), particularly in SystemC, generating Verilog code. In this thesis, the feature maps are also being trained on the FPGA, which is traditionally done offline. The CNN architecture is instantiated on the FPGA and weights are trained through the software model on the ARM processor embedded into the FPGA and updated in the architecture through the AXI bus interface. Moreover, since CNN is implemented in hardware the resource used need to be minimized. This allows to choose a smaller, and cheaper FPGA, as well as reducing the total power consumption. To address this, the effect of bitwidth reduction of the CNN is investigated with respect to the accuracy of handwritten characters recognitions. Finally, the robustness of the CNN is analyzed by breaking internal connection of different neurons studying how the accuracy drops when the fault happens at different layers If the accuracy is reduced, then the CNN is re-trained in-situ to increase the accuracy of the CNN.
Author : Ibrahim Khaleel Suhrawardy
Publisher :
ISBN 13 :
Total Pages : 118 pages
Book Rating : 4.:/5 (27 download)
Download or read book Electronic Implementation of Hopfield Type Neural Networks written by Ibrahim Khaleel Suhrawardy and published by . This book was released on 1991 with total page 118 pages. Available in PDF, EPUB and Kindle. Book excerpt:
