Vlsi For Artificial Intelligence And Neural Networks

Author : Jose G. Delgado-Frias,W.R. Moore
Publisher : Springer Science & Business Media
Page : 411 pages
File Size : 52,9 Mb
Release : 2012-12-06
Category : Computers
ISBN : 9781461537526

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VLSI for Artificial Intelligence and Neural Networks by Jose G. Delgado-Frias,W.R. Moore Pdf

This book is an edited selection of the papers presented at the International Workshop on VLSI for Artifidal Intelligence and Neural Networks which was held at the University of Oxford in September 1990. Our thanks go to all the contributors and especially to the programme committee for all their hard work. Thanks are also due to the ACM-SIGARCH, the IEEE Computer Society, and the lEE for publicizing the event and to the University of Oxford and SUNY-Binghamton for their active support. We are particularly grateful to Anna Morris, Maureen Doherty and Laura Duffy for coping with the administrative problems. Jose Delgado-Frias Will Moore April 1991 vii PROLOGUE Artificial intelligence and neural network algorithms/computing have increased in complexity as well as in the number of applications. This in tum has posed a tremendous need for a larger computational power than can be provided by conventional scalar processors which are oriented towards numeric and data manipulations. Due to the artificial intelligence requirements (symbolic manipulation, knowledge representation, non-deterministic computations and dynamic resource allocation) and neural network computing approach (non-programming and learning), a different set of constraints and demands are imposed on the computer architectures for these applications.

Author : Jose G. Delgado-Frias,W.R. Moore
Publisher : Springer Science & Business Media
Page : 318 pages
File Size : 49,8 Mb
Release : 2013-06-29
Category : Computers
ISBN : 9781489913319

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VLSI for Neural Networks and Artificial Intelligence by Jose G. Delgado-Frias,W.R. Moore Pdf

Neural network and artificial intelligence algorithrns and computing have increased not only in complexity but also in the number of applications. This in turn has posed a tremendous need for a larger computational power that conventional scalar processors may not be able to deliver efficiently. These processors are oriented towards numeric and data manipulations. Due to the neurocomputing requirements (such as non-programming and learning) and the artificial intelligence requirements (such as symbolic manipulation and knowledge representation) a different set of constraints and demands are imposed on the computer architectures/organizations for these applications. Research and development of new computer architectures and VLSI circuits for neural networks and artificial intelligence have been increased in order to meet the new performance requirements. This book presents novel approaches and trends on VLSI implementations of machines for these applications. Papers have been drawn from a number of research communities; the subjects span analog and digital VLSI design, computer design, computer architectures, neurocomputing and artificial intelligence techniques. This book has been organized into four subject areas that cover the two major categories of this book; the areas are: analog circuits for neural networks, digital implementations of neural networks, neural networks on multiprocessor systems and applications, and VLSI machines for artificial intelligence. The topics that are covered in each area are briefly introduced below.

Author : Mohamed I. Elmasry
Publisher : Springer Science & Business Media
Page : 335 pages
File Size : 46,7 Mb
Release : 2012-12-06
Category : Technology & Engineering
ISBN : 9781461527664

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VLSI Artificial Neural Networks Engineering by Mohamed I. Elmasry Pdf

Engineers have long been fascinated by how efficient and how fast biological neural networks are capable of performing such complex tasks as recognition. Such networks are capable of recognizing input data from any of the five senses with the necessary accuracy and speed to allow living creatures to survive. Machines which perform such complex tasks as recognition, with similar ac curacy and speed, were difficult to implement until the technological advances of VLSI circuits and systems in the late 1980's. Since then, the field of VLSI Artificial Neural Networks (ANNs) have witnessed an exponential growth and a new engineering discipline was born. Today, many engineering curriculums have included a course or more on the subject at the graduate or senior under graduate levels. Since the pioneering book by Carver Mead; "Analog VLSI and Neural Sys tems", Addison-Wesley, 1989; there were a number of excellent text and ref erence books on the subject, each dealing with one or two topics. This book attempts to present an integrated approach of a single research team to VLSI ANNs Engineering.

Author : Jose G. Delgado-Frias,Will Moore
Publisher : Springer Science & Business Media
Page : 285 pages
File Size : 51,6 Mb
Release : 2012-12-06
Category : Technology & Engineering
ISBN : 9781461316190

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VLSI for Artificial Intelligence by Jose G. Delgado-Frias,Will Moore Pdf

Author : Sandeep Saini,Kusum Lata,G.R. Sinha
Publisher : CRC Press
Page : 292 pages
File Size : 52,6 Mb
Release : 2021-12-31
Category : Technology & Engineering
ISBN : 9781000523843

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VLSI and Hardware Implementations using Modern Machine Learning Methods by Sandeep Saini,Kusum Lata,G.R. Sinha Pdf

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 : Ulrich Ramacher,Ulrich Rückert
Publisher : Springer Science & Business Media
Page : 346 pages
File Size : 48,7 Mb
Release : 2012-12-06
Category : Technology & Engineering
ISBN : 9781461539940

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VLSI Design of Neural Networks by Ulrich Ramacher,Ulrich Rückert Pdf

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 : Bing J. Sheu,Joongho Choi
Publisher : Springer Science & Business Media
Page : 569 pages
File Size : 47,5 Mb
Release : 2012-12-06
Category : Technology & Engineering
ISBN : 9781461522478

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Neural Information Processing and VLSI by Bing J. Sheu,Joongho Choi Pdf

Neural Information Processing and VLSI provides a unified treatment of this important subject for use in classrooms, industry, and research laboratories, in order to develop advanced artificial and biologically-inspired neural networks using compact analog and digital VLSI parallel processing techniques. Neural Information Processing and VLSI systematically presents various neural network paradigms, computing architectures, and the associated electronic/optical implementations using efficient VLSI design methodologies. Conventional digital machines cannot perform computationally-intensive tasks with satisfactory performance in such areas as intelligent perception, including visual and auditory signal processing, recognition, understanding, and logical reasoning (where the human being and even a small living animal can do a superb job). Recent research advances in artificial and biological neural networks have established an important foundation for high-performance information processing with more efficient use of computing resources. The secret lies in the design optimization at various levels of computing and communication of intelligent machines. Each neural network system consists of massively paralleled and distributed signal processors with every processor performing very simple operations, thus consuming little power. Large computational capabilities of these systems in the range of some hundred giga to several tera operations per second are derived from collectively parallel processing and efficient data routing, through well-structured interconnection networks. Deep-submicron very large-scale integration (VLSI) technologies can integrate tens of millions of transistors in a single silicon chip for complex signal processing and information manipulation. The book is suitable for those interested in efficient neurocomputing as well as those curious about neural network system applications. It has been especially prepared for use as a text for advanced undergraduate and first year graduate students, and is an excellent reference book for researchers and scientists working in the fields covered.

Author : Ibrahim (Abe) M. Elfadel,Duane S. Boning,Xin Li
Publisher : Springer
Page : 694 pages
File Size : 40,5 Mb
Release : 2019-03-15
Category : Technology & Engineering
ISBN : 9783030046668

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Machine Learning in VLSI Computer-Aided Design by Ibrahim (Abe) M. Elfadel,Duane S. Boning,Xin Li Pdf

This book provides readers with an up-to-date account of the use of machine learning frameworks, methodologies, algorithms and techniques in the context of computer-aided design (CAD) for very-large-scale integrated circuits (VLSI). Coverage includes the various machine learning methods used in lithography, physical design, yield prediction, post-silicon performance analysis, reliability and failure analysis, power and thermal analysis, analog design, logic synthesis, verification, and neuromorphic design. Provides up-to-date information on machine learning in VLSI CAD for device modeling, layout verifications, yield prediction, post-silicon validation, and reliability; Discusses the use of machine learning techniques in the context of analog and digital synthesis; Demonstrates how to formulate VLSI CAD objectives as machine learning problems and provides a comprehensive treatment of their efficient solutions; Discusses the tradeoff between the cost of collecting data and prediction accuracy and provides a methodology for using prior data to reduce cost of data collection in the design, testing and validation of both analog and digital VLSI designs. From the Foreword As the semiconductor industry embraces the rising swell of cognitive systems and edge intelligence, this book could serve as a harbinger and example of the osmosis that will exist between our cognitive structures and methods, on the one hand, and the hardware architectures and technologies that will support them, on the other....As we transition from the computing era to the cognitive one, it behooves us to remember the success story of VLSI CAD and to earnestly seek the help of the invisible hand so that our future cognitive systems are used to design more powerful cognitive systems. This book is very much aligned with this on-going transition from computing to cognition, and it is with deep pleasure that I recommend it to all those who are actively engaged in this exciting transformation. Dr. Ruchir Puri, IBM Fellow, IBM Watson CTO & Chief Architect, IBM T. J. Watson Research Center

Author : Bank W. Lee,Bing J. Sheu
Publisher : Springer Science & Business Media
Page : 251 pages
File Size : 49,9 Mb
Release : 2012-12-06
Category : Technology & Engineering
ISBN : 9781461539841

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Hardware Annealing in Analog VLSI Neurocomputing by Bank W. Lee,Bing J. Sheu Pdf

Rapid advances in neural sciences and VLSI design technologies have provided an excellent means to boost the computational capability and efficiency of data and signal processing tasks by several orders of magnitude. With massively parallel processing capabilities, artificial neural networks can be used to solve many engineering and scientific problems. Due to the optimized data communication structure for artificial intelligence applications, a neurocomputer is considered as the most promising sixth-generation computing machine. Typical applica tions of artificial neural networks include associative memory, pattern classification, early vision processing, speech recognition, image data compression, and intelligent robot control. VLSI neural circuits play an important role in exploring and exploiting the rich properties of artificial neural networks by using pro grammable synapses and gain-adjustable neurons. Basic building blocks of the analog VLSI neural networks consist of operational amplifiers as electronic neurons and synthesized resistors as electronic synapses. The synapse weight information can be stored in the dynamically refreshed capacitors for medium-term storage or in the floating-gate of an EEPROM cell for long-term storage. The feedback path in the amplifier can continuously change the output neuron operation from the unity-gain configuration to a high-gain configuration. The adjustability of the vol tage gain in the output neurons allows the implementation of hardware annealing in analog VLSI neural chips to find optimal solutions very efficiently. Both supervised learning and unsupervised learning can be implemented by using the programmable neural chips.

Author : José Mira,Joan Cabestany,Alberto Prieto
Publisher : Springer Science & Business Media
Page : 772 pages
File Size : 42,8 Mb
Release : 1993-05-27
Category : Computers
ISBN : 3540567984

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New Trends in Neural Computation by José Mira,Joan Cabestany,Alberto Prieto Pdf

Neural computation arises from the capacity of nervous tissue to process information and accumulate knowledge in an intelligent manner. Conventional computational machines have encountered enormous difficulties in duplicatingsuch functionalities. This has given rise to the development of Artificial Neural Networks where computation is distributed over a great number of local processing elements with a high degree of connectivityand in which external programming is replaced with supervised and unsupervised learning. The papers presented in this volume are carefully reviewed versions of the talks delivered at the International Workshop on Artificial Neural Networks (IWANN '93) organized by the Universities of Catalonia and the Spanish Open University at Madrid and held at Barcelona, Spain, in June 1993. The 111 papers are organized in seven sections: biological perspectives, mathematical models, learning, self-organizing networks, neural software, hardware implementation, and applications (in five subsections: signal processing and pattern recognition, communications, artificial vision, control and robotics, and other applications).

Author : Manfred Glesner,Werner Pöchmüller
Publisher : Unknown
Page : 314 pages
File Size : 52,9 Mb
Release : 1994
Category : Computers
ISBN : UOM:39015032504642

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Neurocomputers by Manfred Glesner,Werner Pöchmüller Pdf

Author : I. Aleksander,J. Taylor
Publisher : Elsevier
Page : 878 pages
File Size : 43,8 Mb
Release : 2014-06-28
Category : Science
ISBN : 9781483298061

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Artificial Neural Networks, 2 by I. Aleksander,J. Taylor Pdf

This two-volume proceedings compilation is a selection of research papers presented at the ICANN-92. The scope of the volumes is interdisciplinary, ranging from the minutiae of VLSI hardware, to new discoveries in neurobiology, through to the workings of the human mind. USA and European research is well represented, including not only new thoughts from old masters but also a large number of first-time authors who are ensuring the continued development of the field.

Author : K. Mäkisara,O. Simula,J. Kangas,T. Kohonen
Publisher : Elsevier
Page : 836 pages
File Size : 40,9 Mb
Release : 2014-06-28
Category : Computers
ISBN : 9781483298009

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Artificial Neural Networks by K. Mäkisara,O. Simula,J. Kangas,T. Kohonen Pdf

This two-volume proceedings compiles a selection of research papers presented at the ICANN-91. The scope of the volumes is interdisciplinary, ranging from mathematics and engineering to cognitive sciences and biology. European research is well represented. Volume 1 contains all the orally presented papers, including both invited talks and submitted papers. Volume 2 contains the plenary talks and the poster presentations.

Author : E Fiesler,R Beale
Publisher : CRC Press
Page : 1094 pages
File Size : 46,6 Mb
Release : 2020-01-15
Category : Computers
ISBN : 9781420050646

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Handbook of Neural Computation by E Fiesler,R Beale Pdf

The Handbook of Neural Computation is a practical, hands-on guide to the design and implementation of neural networks used by scientists and engineers to tackle difficult and/or time-consuming problems. The handbook bridges an information pathway between scientists and engineers in different disciplines who apply neural networks to similar probl

Author : Dhanasekar, S.,Sagayam, K. Martin,Vijh, Surbhi,Tyagi, Vipin,Norta, Alex
Publisher : IGI Global
Page : 400 pages
File Size : 49,8 Mb
Release : 2023-07-19
Category : Computers
ISBN : 9781668465981

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Neuromorphic Computing Systems for Industry 4.0 by Dhanasekar, S.,Sagayam, K. Martin,Vijh, Surbhi,Tyagi, Vipin,Norta, Alex Pdf

As artificial intelligence (AI) processing moves from the cloud to the edge of the network, battery-powered and deeply embedded devices are challenged to perform AI functions such as computer vision and voice recognition. Microchip Technology Inc., via its Silicon Storage Technology (SST) subsidiary, is addressing this challenge by significantly reducing power with its analog memory technology, the memBrain Memory Solution. The memBrain solution is being adopted by today’s companies looking to advance machine learning capacities in edge devices. Due to its ability to significantly reduce power, this analog in-memory computer solution is ideal for an AI application. Neuromorphic Computing Systems for Industry 4.0 covers the available literature in the field of neural computing-based microchip technology. It provides further research opportunities in this dynamic field. Covering topics such as emotion recognition, biometric authentication, and neural network protection, this premier reference source is an essential resource for technology developers, computer scientists, engineers, students and educators of higher education, librarians, researchers, and academicians.