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Download or read book Adders and Multipliers Using Neural Gates written by Nabil G. Taleb and published by . This book was released on 1993 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Author: Sandeep Saini
Publisher: CRC Press
Published: 2021-12-31
Total Pages: 292
ISBN-13: 1000523845
DOWNLOAD EBOOKMachine learning is a potential solution to resolve bottleneck issues in VLSI via optimizing tasks in the design process. This book aims to provide the latest machine-learning–based methods, algorithms, architectures, and frameworks designed for VLSI design. The focus is on digital, analog, and mixed-signal design techniques, device modeling, physical design, hardware implementation, testability, reconfigurable design, synthesis and verification, and related areas. Chapters include case studies as well as novel research ideas in the given field. Overall, the book provides practical implementations of VLSI design, IC design, and hardware realization using machine learning techniques. Features: Provides the details of state-of-the-art machine learning methods used in VLSI design Discusses hardware implementation and device modeling pertaining to machine learning algorithms Explores machine learning for various VLSI architectures and reconfigurable computing Illustrates the latest techniques for device size and feature optimization Highlights the latest case studies and reviews of the methods used for hardware implementation This book is aimed at researchers, professionals, and graduate students in VLSI, machine learning, electrical and electronic engineering, computer engineering, and hardware systems.
Download or read book VLSI and Hardware Implementations using Modern Machine Learning Methods written by Sandeep Saini and published by CRC Press. This book was released on 2021-12-31 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning is a potential solution to resolve bottleneck issues in VLSI via optimizing tasks in the design process. This book aims to provide the latest machine-learning–based methods, algorithms, architectures, and frameworks designed for VLSI design. The focus is on digital, analog, and mixed-signal design techniques, device modeling, physical design, hardware implementation, testability, reconfigurable design, synthesis and verification, and related areas. Chapters include case studies as well as novel research ideas in the given field. Overall, the book provides practical implementations of VLSI design, IC design, and hardware realization using machine learning techniques. Features: Provides the details of state-of-the-art machine learning methods used in VLSI design Discusses hardware implementation and device modeling pertaining to machine learning algorithms Explores machine learning for various VLSI architectures and reconfigurable computing Illustrates the latest techniques for device size and feature optimization Highlights the latest case studies and reviews of the methods used for hardware implementation This book is aimed at researchers, professionals, and graduate students in VLSI, machine learning, electrical and electronic engineering, computer engineering, and hardware systems.
Author: Yoshiyasu Takefuji
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 132
ISBN-13: 1461535824
DOWNLOAD EBOOKThis book brings together in one place important contributions and state-of-the-art research in the rapidly advancing area of analog VLSI neural networks. The book serves as an excellent reference, providing insights into some of the most important issues in analog VLSI neural networks research efforts.
Download or read book Analog VLSI Neural Networks written by Yoshiyasu Takefuji and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 132 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book brings together in one place important contributions and state-of-the-art research in the rapidly advancing area of analog VLSI neural networks. The book serves as an excellent reference, providing insights into some of the most important issues in analog VLSI neural networks research efforts.
Author: Janmenjoy Nayak
Publisher: Springer Nature
Published: 2022-05-06
Total Pages: 757
ISBN-13: 9811694478
DOWNLOAD EBOOKThis book addresses different methods and techniques of integration for enhancing the overall goal of data mining. The book is a collection of high-quality peer-reviewed research papers presented in the Sixth International Conference on Computational Intelligence in Data Mining (ICCIDM 2021) held at Aditya Institute of Technology and Management, Tekkali, Andhra Pradesh, India, during December 11–12, 2021. The book addresses the difficulties and challenges for the seamless integration of two core disciplines of computer science, i.e., computational intelligence and data mining. The book helps to disseminate the knowledge about some innovative, active research directions in the field of data mining, machine and computational intelligence, along with some current issues and applications of related topics.
Download or read book Computational Intelligence in Data Mining written by Janmenjoy Nayak and published by Springer Nature. This book was released on 2022-05-06 with total page 757 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book addresses different methods and techniques of integration for enhancing the overall goal of data mining. The book is a collection of high-quality peer-reviewed research papers presented in the Sixth International Conference on Computational Intelligence in Data Mining (ICCIDM 2021) held at Aditya Institute of Technology and Management, Tekkali, Andhra Pradesh, India, during December 11–12, 2021. The book addresses the difficulties and challenges for the seamless integration of two core disciplines of computer science, i.e., computational intelligence and data mining. The book helps to disseminate the knowledge about some innovative, active research directions in the field of data mining, machine and computational intelligence, along with some current issues and applications of related topics.
Author: Andrej Dobnikar
Publisher: Springer
Published: 2011-04-09
Total Pages: 448
ISBN-13: 3642202829
DOWNLOAD EBOOKThe two-volume set LNCS 6593 and 6594 constitutes the refereed proceedings of the 10th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2010, held in Ljubljana, Slovenia, in April 2010. The 83 revised full papers presented were carefully reviewed and selected from a total of 144 submissions. The first volume includes 42 papers and a plenary lecture and is organized in topical sections on neural networks and evolutionary computation.
Download or read book Adaptive and Natural Computing Algorithms written by Andrej Dobnikar and published by Springer. This book was released on 2011-04-09 with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 6593 and 6594 constitutes the refereed proceedings of the 10th International Conference on Adaptive and Natural Computing Algorithms, ICANNGA 2010, held in Ljubljana, Slovenia, in April 2010. The 83 revised full papers presented were carefully reviewed and selected from a total of 144 submissions. The first volume includes 42 papers and a plenary lecture and is organized in topical sections on neural networks and evolutionary computation.
Author: Paul Curzon
Publisher: World Scientific Publishing Company
Published: 2017-01-17
Total Pages: 232
ISBN-13: 1786341867
DOWNLOAD EBOOKFrom the team behind Computer Science for Fun (cs4fn), The Power of Computational Thinking shows that learning to think can be fascinating fun. Can you become a computational thinker?Can machines have brains?Do computers really see and understand the world?Can games help us to study nature, save lives and design the future?Can you use computational thinking in your everyday activities? Yes, and this book shows you how. Computational thinking has changed the way we all live, work and play. It has changed the way science is done too; won wars, created whole new industries and saved lives. It is at the heart of computer programming and is a powerful approach to problem solving, with or without computers. It is so important that many countries now require that primary school children learn the skills. Professors Paul Curzon and Peter McOwan of Queen Mary University of London have written a unique and enjoyable introduction. They describe the elements of computational thinking — such as algorithmic thinking, decomposition, abstraction and pattern matching — in an entertaining and accessible way, using magic tricks, games and puzzles, as well as through real and challenging problems that computer scientists work on. This book gives you a head start in learning the skills needed for coding, and will improve your real life problem solving skills. It will help you design and evaluate new technologies, as well as understand both your own brain and the digital world in a deeper way. Request Inspection Copy
Download or read book The Power of Computational Thinking written by Paul Curzon and published by World Scientific Publishing Company. This book was released on 2017-01-17 with total page 232 pages. Available in PDF, EPUB and Kindle. Book excerpt: From the team behind Computer Science for Fun (cs4fn), The Power of Computational Thinking shows that learning to think can be fascinating fun. Can you become a computational thinker?Can machines have brains?Do computers really see and understand the world?Can games help us to study nature, save lives and design the future?Can you use computational thinking in your everyday activities? Yes, and this book shows you how. Computational thinking has changed the way we all live, work and play. It has changed the way science is done too; won wars, created whole new industries and saved lives. It is at the heart of computer programming and is a powerful approach to problem solving, with or without computers. It is so important that many countries now require that primary school children learn the skills. Professors Paul Curzon and Peter McOwan of Queen Mary University of London have written a unique and enjoyable introduction. They describe the elements of computational thinking — such as algorithmic thinking, decomposition, abstraction and pattern matching — in an entertaining and accessible way, using magic tricks, games and puzzles, as well as through real and challenging problems that computer scientists work on. This book gives you a head start in learning the skills needed for coding, and will improve your real life problem solving skills. It will help you design and evaluate new technologies, as well as understand both your own brain and the digital world in a deeper way. Request Inspection Copy
Author: Boris Kryzhanovsky
Publisher: Springer
Published: 2017-08-28
Total Pages: 199
ISBN-13: 3319666045
DOWNLOAD EBOOKThis book describes new theories and applications of artificial neural networks, with a special focus on neural computation, cognitive science and machine learning. It discusses cutting-edge research at the intersection between different fields, from topics such as cognition and behavior, motivation and emotions, to neurocomputing, deep learning, classification and clustering. Further topics include signal processing methods, robotics and neurobionics, and computer vision alike. The book includes selected papers from the XIX International Conference on Neuroinformatics, held on October 2-6, 2017, in Moscow, Russia.
Download or read book Advances in Neural Computation, Machine Learning, and Cognitive Research written by Boris Kryzhanovsky and published by Springer. This book was released on 2017-08-28 with total page 199 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes new theories and applications of artificial neural networks, with a special focus on neural computation, cognitive science and machine learning. It discusses cutting-edge research at the intersection between different fields, from topics such as cognition and behavior, motivation and emotions, to neurocomputing, deep learning, classification and clustering. Further topics include signal processing methods, robotics and neurobionics, and computer vision alike. The book includes selected papers from the XIX International Conference on Neuroinformatics, held on October 2-6, 2017, in Moscow, Russia.
Author: Ulrich Ramacher
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 346
ISBN-13: 1461539943
DOWNLOAD EBOOKThe early era of neural network hardware design (starting at 1985) was mainly technology driven. Designers used almost exclusively analog signal processing concepts for the recall mode. Learning was deemed not to cause a problem because the number of implementable synapses was still so low that the determination of weights and thresholds could be left to conventional computers. Instead, designers tried to directly map neural parallelity into hardware. The architectural concepts were accordingly simple and produced the so called interconnection problem which, in turn, made many engineers believe it could be solved by optical implementation in adequate fashion only. Furthermore, the inherent fault-tolerance and limited computation accuracy of neural networks were claimed to justify that little effort is to be spend on careful design, but most effort be put on technology issues. As a result, it was almost impossible to predict whether an electronic neural network would function in the way it was simulated to do. This limited the use of the first neuro-chips for further experimentation, not to mention that real-world applications called for much more synapses than could be implemented on a single chip at that time. Meanwhile matters have matured. It is recognized that isolated definition of the effort of analog multiplication, for instance, would be just as inappropriate on the part ofthe chip designer as determination of the weights by simulation, without allowing for the computing accuracy that can be achieved, on the part of the user.
Download or read book VLSI Design of Neural Networks written by Ulrich Ramacher and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: The early era of neural network hardware design (starting at 1985) was mainly technology driven. Designers used almost exclusively analog signal processing concepts for the recall mode. Learning was deemed not to cause a problem because the number of implementable synapses was still so low that the determination of weights and thresholds could be left to conventional computers. Instead, designers tried to directly map neural parallelity into hardware. The architectural concepts were accordingly simple and produced the so called interconnection problem which, in turn, made many engineers believe it could be solved by optical implementation in adequate fashion only. Furthermore, the inherent fault-tolerance and limited computation accuracy of neural networks were claimed to justify that little effort is to be spend on careful design, but most effort be put on technology issues. As a result, it was almost impossible to predict whether an electronic neural network would function in the way it was simulated to do. This limited the use of the first neuro-chips for further experimentation, not to mention that real-world applications called for much more synapses than could be implemented on a single chip at that time. Meanwhile matters have matured. It is recognized that isolated definition of the effort of analog multiplication, for instance, would be just as inappropriate on the part ofthe chip designer as determination of the weights by simulation, without allowing for the computing accuracy that can be achieved, on the part of the user.
Author: Mona E. Zaghloul
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 293
ISBN-13: 1461526809
DOWNLOAD EBOOKWhen confronted with the hows and whys of nature's computational engines, some prefer to focus upon neural function: addressing issues of neural system behavior and its relation to natural intelligence. Then there are those who prefer the study of the "mechanics" of neural systems: the nuts and bolts of the "wetware": the neurons and synapses. Those who investigate pulse coded implementations ofartificial neural networks know what it means to stand at the boundary which lies between these two worlds: not just asking why natural neural systems behave as they do, but also how they achieve their marvelous feats. The research results presented in this book not only address more conventional abstract notions of neural-like processing, but also the more specific details ofneural-like processors. It has been established for some time that natural neural systems perform a great deal of information processing via electrochemical pulses. Accordingly, pulse coded neural network concepts are receiving increased attention in artificial neural network research. This increased interest is compounded by continuing advances in the field of VLSI circuit design. This is the first time in history in which it is practical to construct networks of neuron-like circuits of reasonable complexity that can be applied to real problems. We believe that the pioneering work in artificial neural systems presented in this book will lead to further advances that will not only be useful in some practical sense, but may also provide some additional insight into the operation of their natural counterparts.
Download or read book Silicon Implementation of Pulse Coded Neural Networks written by Mona E. Zaghloul and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: When confronted with the hows and whys of nature's computational engines, some prefer to focus upon neural function: addressing issues of neural system behavior and its relation to natural intelligence. Then there are those who prefer the study of the "mechanics" of neural systems: the nuts and bolts of the "wetware": the neurons and synapses. Those who investigate pulse coded implementations ofartificial neural networks know what it means to stand at the boundary which lies between these two worlds: not just asking why natural neural systems behave as they do, but also how they achieve their marvelous feats. The research results presented in this book not only address more conventional abstract notions of neural-like processing, but also the more specific details ofneural-like processors. It has been established for some time that natural neural systems perform a great deal of information processing via electrochemical pulses. Accordingly, pulse coded neural network concepts are receiving increased attention in artificial neural network research. This increased interest is compounded by continuing advances in the field of VLSI circuit design. This is the first time in history in which it is practical to construct networks of neuron-like circuits of reasonable complexity that can be applied to real problems. We believe that the pioneering work in artificial neural systems presented in this book will lead to further advances that will not only be useful in some practical sense, but may also provide some additional insight into the operation of their natural counterparts.
Author: José Mira
Publisher: Springer Science & Business Media
Published: 2003-05-22
Total Pages: 845
ISBN-13: 354040211X
DOWNLOAD EBOOKThe two-volume set LNCS 2686 and LNCS 2687 constitute the refereed proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2003, held in Maó, Menorca, Spain in June 2003. The 197 revised papers presented were carefully reviewed and selected for inclusion in the book and address the following topics: mathematical and computational methods in neural modelling, neurophysiological data analysis and modelling, structural and functional models of neurons, learning and other plasticity phenomena, complex systems dynamics, cognitive processes and artificial intelligence, methodologies for net design, bio-inspired systems and engineering, and applications in a broad variety of fields.
Download or read book Artificial Neural Nets. Problem Solving Methods written by José Mira and published by Springer Science & Business Media. This book was released on 2003-05-22 with total page 845 pages. Available in PDF, EPUB and Kindle. Book excerpt: The two-volume set LNCS 2686 and LNCS 2687 constitute the refereed proceedings of the 7th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2003, held in Maó, Menorca, Spain in June 2003. The 197 revised papers presented were carefully reviewed and selected for inclusion in the book and address the following topics: mathematical and computational methods in neural modelling, neurophysiological data analysis and modelling, structural and functional models of neurons, learning and other plasticity phenomena, complex systems dynamics, cognitive processes and artificial intelligence, methodologies for net design, bio-inspired systems and engineering, and applications in a broad variety of fields.
