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Neural Networks And Systolic Array Design


Neural Networks And Systolic Array Design
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Neural Networks And Systolic Array Design


Neural Networks And Systolic Array Design
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Author : Sankar K. Pal
language : en
Publisher: World Scientific
Release Date : 2002

Neural Networks And Systolic Array Design written by Sankar K. Pal and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002 with Computers categories.


Neural networks (NNs) and systolic arrays (SAs) have many similar features. This volume describes, in a unified way, the basic concepts, theories and characteristic features of integrating or formulating different facets of NNs and SAs, as well as presents recent developments and significant applications. The articles, written by experts from all over the world, demonstrate the various ways this integration can be made to efficiently design methodologies, algorithms and architectures, and also implementations, for NN applications. The book will be useful to graduate students and researchers in many related areas, not only as a reference book but also as a textbook for some parts of the curriculum. It will also benefit researchers and practitioners in industry and R&D laboratories who are working in the fields of system design, VLSI, parallel processing, neural networks, and vision.



Neural Networks And Systolic Array Design


Neural Networks And Systolic Array Design
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Author : Sankar Kumar Pal
language : en
Publisher: World Scientific
Release Date : 2002-06-27

Neural Networks And Systolic Array Design written by Sankar Kumar Pal and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-06-27 with Technology & Engineering categories.


Neural networks (NNs) and systolic arrays (SAs) have many similar features. This volume describes, in a unified way, the basic concepts, theories and characteristic features of integrating or formulating different facets of NNs and SAs, as well as presents recent developments and significant applications. The articles, written by experts from all over the world, demonstrate the various ways this integration can be made to efficiently design methodologies, algorithms and architectures, and also implementations, for NN applications. The book will be useful to graduate students and researchers in many related areas, not only as a reference book but also as a textbook for some parts of the curriculum. It will also benefit researchers and practitioners in industry and R&D laboratories who are working in the fields of system design, VLSI, parallel processing, neural networks, and vision.



Computational Intelligence In Optimization


Computational Intelligence In Optimization
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Author : Yoel Tenne
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-06-30

Computational Intelligence In Optimization written by Yoel Tenne and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-06-30 with Technology & Engineering categories.


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.



Vlsi Design Of Neural Networks


Vlsi Design Of Neural Networks
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Author : Ulrich Ramacher
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06

Vlsi Design Of Neural Networks written by Ulrich Ramacher and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.


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.



Efficient Processing Of Deep Neural Networks


Efficient Processing Of Deep Neural Networks
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Author : Vivienne Sze
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2020-06-24

Efficient Processing Of Deep Neural Networks written by Vivienne Sze and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-06-24 with Computers categories.


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 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 the 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 a formalization and organization of key concepts from contemporary works that provides insights that may spark new ideas.



Deep Learning And Parallel Computing Environment For Bioengineering Systems


Deep Learning And Parallel Computing Environment For Bioengineering Systems
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Author : Arun Kumar Sangaiah
language : en
Publisher: Academic Press
Release Date : 2019-07-26

Deep Learning And Parallel Computing Environment For Bioengineering Systems written by Arun Kumar Sangaiah and has been published by Academic Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-26 with Technology & Engineering categories.


Deep Learning and Parallel Computing Environment for Bioengineering Systems delivers a significant forum for the technical advancement of deep learning in parallel computing environment across bio-engineering diversified domains and its applications. Pursuing an interdisciplinary approach, it focuses on methods used to identify and acquire valid, potentially useful knowledge sources. Managing the gathered knowledge and applying it to multiple domains including health care, social networks, mining, recommendation systems, image processing, pattern recognition and predictions using deep learning paradigms is the major strength of this book. This book integrates the core ideas of deep learning and its applications in bio engineering application domains, to be accessible to all scholars and academicians. The proposed techniques and concepts in this book can be extended in future to accommodate changing business organizations' needs as well as practitioners' innovative ideas. - Presents novel, in-depth research contributions from a methodological/application perspective in understanding the fusion of deep machine learning paradigms and their capabilities in solving a diverse range of problems - Illustrates the state-of-the-art and recent developments in the new theories and applications of deep learning approaches applied to parallel computing environment in bioengineering systems - Provides concepts and technologies that are successfully used in the implementation of today's intelligent data-centric critical systems and multi-media Cloud-Big data



Advances In Neural Networks Isnn 2006


Advances In Neural Networks Isnn 2006
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Author : Jun Wang
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-05-11

Advances In Neural Networks Isnn 2006 written by Jun Wang and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-05-11 with Computers categories.


This is Volume III of a three volume set constituting the refereed proceedings of the Third International Symposium on Neural Networks, ISNN 2006. 616 revised papers are organized in topical sections on neurobiological analysis, theoretical analysis, neurodynamic optimization, learning algorithms, model design, kernel methods, data preprocessing, pattern classification, computer vision, image and signal processing, system modeling, robotic systems, transportation systems, communication networks, information security, fault detection, financial analysis, bioinformatics, biomedical and industrial applications, and more.



Application Of Neural Networks And Other Learning Technologies In Process Engineering


Application Of Neural Networks And Other Learning Technologies In Process Engineering
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Author : I. M. Mujtaba
language : en
Publisher: World Scientific
Release Date : 2001

Application Of Neural Networks And Other Learning Technologies In Process Engineering written by I. M. Mujtaba and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001 with Technology & Engineering categories.


This book is a follow-up to the IChemE symposium on ?Neural Networks and Other Learning Technologies?, held at Imperial College, UK, in May 1999. The interest shown by the participants, especially those from the industry, has been instrumental in producing the book. The papers have been written by contributors of the symposium and experts in this field from around the world. They present all the important aspects of neural network utilisation as well as show the versatility of neural networks in various aspects of process engineering problems ? modelling, estimation, control, optimisation and industrial applications.



Integration Of Swarm Intelligence And Artificial Neural Network


Integration Of Swarm Intelligence And Artificial Neural Network
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Author : Satchidananda Dehuri
language : en
Publisher: World Scientific
Release Date : 2011

Integration Of Swarm Intelligence And Artificial Neural Network written by Satchidananda Dehuri and has been published by World Scientific this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011 with Computers categories.


This book provides a new forum for the dissemination of knowledge in both theoretical and applied research on swarm intelligence (SI) and artificial neural network (ANN). It accelerates interaction between the two bodies of knowledge and fosters a unified development in the next generation of computational model for machine learning. To the best of our knowledge, the integration of SI and ANN is the first attempt to integrate various aspects of both the independent research area into a single volume.



Neural Networks In Design And Manufacturing


Neural Networks In Design And Manufacturing
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Author : Jun Wang
language : en
Publisher: World Scientific Publishing Company Incorporated
Release Date : 1993-01-01

Neural Networks In Design And Manufacturing written by Jun Wang and has been published by World Scientific Publishing Company Incorporated this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993-01-01 with Computers categories.