Implementing Binary Neural Networks

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Implementing Binary Neural Networks

Author : Joshua Wolff Fromm
Publisher :
ISBN 13 :
Total Pages : 79 pages
Book Rating : 4.:/5 (115 download)

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Book Synopsis Implementing Binary Neural Networks by : Joshua Wolff Fromm

Download or read book Implementing Binary Neural Networks written by Joshua Wolff Fromm and published by . This book was released on 2020 with total page 79 pages. Available in PDF, EPUB and Kindle. Book excerpt: The recent renaissance of deep neural networks has lead to impressive advancements in many domains of machine learning. However, the computational cost of these neural models in- creases in line with their performance, with many state-of-the-art models only being able to run on expensive high-end hardware. The need to efficiently deploy neural networks to commodity platforms has made network optimization a popular field of research. One particularly promising technique is network binarization, which quantizes the weights and activations of a model to only one or two bits. Although binarization offers theoretical oper- ation count reductions of up to 32X, no actual measurements have been reported. This is a symptom of the gap between theory and implementation of binary networks that exists to- day. In this work, we bridge the gap between abstract simulations and real usable high speed networks. To do so, we identify errors in the existing literature, develop novel algorithms, and introduce Riptide, an open source system that can train and deploy state-of-the-art binary neural networks to multiple hardware backends.

Neural Information Processing: Research and Development

Author : Jagath Chandana Rajapakse
Publisher : Springer
ISBN 13 : 3540399356
Total Pages : 487 pages
Book Rating : 4.5/5 (43 download)

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Book Synopsis Neural Information Processing: Research and Development by : Jagath Chandana Rajapakse

Download or read book Neural Information Processing: Research and Development written by Jagath Chandana Rajapakse and published by Springer. This book was released on 2012-12-06 with total page 487 pages. Available in PDF, EPUB and Kindle. Book excerpt: The field of neural information processing has two main objects: investigation into the functioning of biological neural networks and use of artificial neural networks to sol ve real world problems. Even before the reincarnation of the field of artificial neural networks in mid nineteen eighties, researchers have attempted to explore the engineering of human brain function. After the reincarnation, we have seen an emergence of a large number of neural network models and their successful applications to solve real world problems. This volume presents a collection of recent research and developments in the field of neural information processing. The book is organized in three Parts, i.e., (1) architectures, (2) learning algorithms, and (3) applications. Artificial neural networks consist of simple processing elements called neurons, which are connected by weights. The number of neurons and how they are connected to each other defines the architecture of a particular neural network. Part 1 of the book has nine chapters, demonstrating some of recent neural network architectures derived either to mimic aspects of human brain function or applied in some real world problems. Muresan provides a simple neural network model, based on spiking neurons that make use of shunting inhibition, which is capable of resisting small scale changes of stimulus. Hoshino and Zheng simulate a neural network of the auditory cortex to investigate neural basis for encoding and perception of vowel sounds.

Binary Neural Networks

Author : Baochang Zhang
Publisher : CRC Press
ISBN 13 : 1003816797
Total Pages : 218 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Binary Neural Networks by : Baochang Zhang

Download or read book Binary Neural Networks written by Baochang Zhang and published by CRC Press. This book was released on 2023-12-13 with total page 218 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning has achieved impressive results in image classification, computer vision, and natural language processing. To achieve better performance, deeper and wider networks have been designed, which increase the demand for computational resources. The number of floatingpoint operations (FLOPs) has increased dramatically with larger networks, and this has become an obstacle for convolutional neural networks (CNNs) being developed for mobile and embedded devices. In this context, Binary Neural Networks: Algorithms, Architectures, and Applications will focus on CNN compression and acceleration, which are important for the research community. We will describe numerous methods, including parameter quantization, network pruning, low-rank decomposition, and knowledge distillation. More recently, to reduce the burden of handcrafted architecture design, neural architecture search (NAS) has been used to automatically build neural networks by searching over a vast architecture space. Our book will also introduce NAS and binary NAS and its superiority and state-of-the-art performance in various applications, such as image classification and object detection. We also describe extensive applications of compressed deep models on image classification, speech recognition, object detection, and tracking. These topics can help researchers better understand the usefulness and the potential of network compression on practical applications. Moreover, interested readers should have basic knowledge of machine learning and deep learning to better understand the methods described in this book. Key Features Reviews recent advances in CNN compression and acceleration Elaborates recent advances on binary neural network (BNN) technologies Introduces applications of BNN in image classification, speech recognition, object detection, and more

Design of Asynchronous Binary Neural Networks Using Linear Programming

Author : Kerem Irten
Publisher :
ISBN 13 :
Total Pages : 106 pages
Book Rating : 4.:/5 (264 download)

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Book Synopsis Design of Asynchronous Binary Neural Networks Using Linear Programming by : Kerem Irten

Download or read book Design of Asynchronous Binary Neural Networks Using Linear Programming written by Kerem Irten and published by . This book was released on 1992 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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.

Improved Grover's Implementation of Quantum Binary Neural Networks

Author : Brody A Wrighter
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (138 download)

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Book Synopsis Improved Grover's Implementation of Quantum Binary Neural Networks by : Brody A Wrighter

Download or read book Improved Grover's Implementation of Quantum Binary Neural Networks written by Brody A Wrighter and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Binary Neural Networks (BNNs) are the result of a simplification of network parameters in Artificial Neural Networks (ANNs). The computational complexity of training ANNs increases significantly as the size of the network increases. This complexity can be greatly reduced if the parameters of the network are binarized. Binarization, which is a one bit quantization, can also come with complications including quantization error and information loss. The implementation of BNNs on quantum hardware could potentially provide a computational advantage over its classical counterpart. This is due to the fact that binarized parameters fit nicely to the nature of quantum hardware. Quantum superposition allows the network to be trained more efficiently, without using back propagation techniques, with the application of Grover’s Algorithm for the training process. This thesis presents two BNN designs that utilize only quantum hardware, and provides practical implementations for both of them. Looking into their scalability, improvements on the design are proposed to reduce complexity even further."--Abstract.

RAM-based Neural Networks

Author : James Austin
Publisher : World Scientific
ISBN 13 : 9789810232535
Total Pages : 256 pages
Book Rating : 4.2/5 (325 download)

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Book Synopsis RAM-based Neural Networks by : James Austin

Download or read book RAM-based Neural Networks written by James Austin and published by World Scientific. This book was released on 1998 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: RAM-based networks are a class of methods for building pattern recognition systems. Unlike other neural network methods, they learn very quickly and as a result are applicable to a wide variety of problems. This important book presents the latest work by the majority of researchers in the field of RAM-based networks.

Guide to Convolutional Neural Networks

Author : Hamed Habibi Aghdam
Publisher : Springer
ISBN 13 : 3319575503
Total Pages : 303 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Guide to Convolutional Neural Networks by : Hamed Habibi Aghdam

Download or read book Guide to Convolutional Neural Networks written by Hamed Habibi Aghdam and published by Springer. This book was released on 2017-05-17 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: This must-read text/reference introduces the fundamental concepts of convolutional neural networks (ConvNets), offering practical guidance on using libraries to implement ConvNets in applications of traffic sign detection and classification. The work presents techniques for optimizing the computational efficiency of ConvNets, as well as visualization techniques to better understand the underlying processes. The proposed models are also thoroughly evaluated from different perspectives, using exploratory and quantitative analysis. Topics and features: explains the fundamental concepts behind training linear classifiers and feature learning; discusses the wide range of loss functions for training binary and multi-class classifiers; illustrates how to derive ConvNets from fully connected neural networks, and reviews different techniques for evaluating neural networks; presents a practical library for implementing ConvNets, explaining how to use a Python interface for the library to create and assess neural networks; describes two real-world examples of the detection and classification of traffic signs using deep learning methods; examines a range of varied techniques for visualizing neural networks, using a Python interface; provides self-study exercises at the end of each chapter, in addition to a helpful glossary, with relevant Python scripts supplied at an associated website. This self-contained guide will benefit those who seek to both understand the theory behind deep learning, and to gain hands-on experience in implementing ConvNets in practice. As no prior background knowledge in the field is required to follow the material, the book is ideal for all students of computer vision and machine learning, and will also be of great interest to practitioners working on autonomous cars and advanced driver assistance systems.

Multi-Valued and Universal Binary Neurons

Author : Igor Aizenberg
Publisher : Springer Science & Business Media
ISBN 13 : 1475731159
Total Pages : 274 pages
Book Rating : 4.4/5 (757 download)

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Book Synopsis Multi-Valued and Universal Binary Neurons by : Igor Aizenberg

Download or read book Multi-Valued and Universal Binary Neurons written by Igor Aizenberg and published by Springer Science & Business Media. This book was released on 2013-03-14 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: Multi-Valued and Universal Binary Neurons deals with two new types of neurons: multi-valued neurons and universal binary neurons. These neurons are based on complex number arithmetic and are hence much more powerful than the typical neurons used in artificial neural networks. Therefore, networks with such neurons exhibit a broad functionality. They can not only realise threshold input/output maps but can also implement any arbitrary Boolean function. Two learning methods are presented whereby these networks can be trained easily. The broad applicability of these networks is proven by several case studies in different fields of application: image processing, edge detection, image enhancement, super resolution, pattern recognition, face recognition, and prediction. The book is hence partitioned into three almost equally sized parts: a mathematical study of the unique features of these new neurons, learning of networks of such neurons, and application of such neural networks. Most of this work was developed by the first two authors over a period of more than 10 years and was only available in the Russian literature. With this book we present the first comprehensive treatment of this important class of neural networks in the open Western literature. Multi-Valued and Universal Binary Neurons is intended for anyone with a scholarly interest in neural network theory, applications and learning. It will also be of interest to researchers and practitioners in the fields of image processing, pattern recognition, control and robotics.

Practical Neural Network Recipies in C++

Author : Masters
Publisher : Elsevier
ISBN 13 : 0080514332
Total Pages : 512 pages
Book Rating : 4.0/5 (85 download)

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Book Synopsis Practical Neural Network Recipies in C++ by : Masters

Download or read book Practical Neural Network Recipies in C++ written by Masters and published by Elsevier. This book was released on 2014-06-28 with total page 512 pages. Available in PDF, EPUB and Kindle. Book excerpt: This text serves as a cookbook for neural network solutions to practical problems using C++. It will enable those with moderate programming experience to select a neural network model appropriate to solving a particular problem, and to produce a working program implementing that network. The book provides guidance along the entire problem-solving path, including designing the training set, preprocessing variables, training and validating the network, and evaluating its performance. Though the book is not intended as a general course in neural networks, no background in neural works is assumed and all models are presented from the ground up.The principle focus of the book is the three layer feedforward network, for more than a decade as the workhorse of professional arsenals. Other network models with strong performance records are also included.Bound in the book is an IBM diskette that includes the source code for all programs in the book. Much of this code can be easily adapted to C compilers. In addition, the operation of all programs is thoroughly discussed both in the text and in the comments within the code to facilitate translation to other languages.

Orthogonal Patterns in Binary Neural Networks

Author : Yoram Baram
Publisher :
ISBN 13 :
Total Pages : 20 pages
Book Rating : 4.:/5 (317 download)

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Book Synopsis Orthogonal Patterns in Binary Neural Networks by : Yoram Baram

Download or read book Orthogonal Patterns in Binary Neural Networks written by Yoram Baram and published by . This book was released on 1988 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Digital Systems

Author : Vahid Asadpour
Publisher : BoD – Books on Demand
ISBN 13 : 1789845408
Total Pages : 165 pages
Book Rating : 4.7/5 (898 download)

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Book Synopsis Digital Systems by : Vahid Asadpour

Download or read book Digital Systems written by Vahid Asadpour and published by BoD – Books on Demand. This book was released on 2018-11-28 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an approach toward the applications and principle theory of digital signal processing in modern intelligent systems, biological engineering, telecommunication, and information technology. Assuming the reader already has prior knowledge of signal processing theory, this book will be useful for finding novel methods that fit special needs in digital signal processing (DSP). The combination of signal processing and intelligent systems in hybrid structures rather than serial or parallel processing provide the best mechanism that is a better fit with the comprehensive nature of human. This book is a practical reference that places the emphasis on principles and applications of DSP in digital systems. It covers a broad area of digital systems and applications of machine learning methods including convolutional neural networks, evolutionary algorithms, adaptive filters, spectral estimation, data compression and functional verification. The level of the book is ideal for professional DSP users and useful for graduate students who are looking for solutions to their design problems. The theoretical principles provide the required base for comprehension of the methods and application of modifications for the special needs of practical projects.

Rethinking Binary Neural Network Design for FPGA Implementation

Author : Erwei Wang
Publisher :
ISBN 13 :
Total Pages : 0 pages
Book Rating : 4.:/5 (133 download)

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Book Synopsis Rethinking Binary Neural Network Design for FPGA Implementation by : Erwei Wang

Download or read book Rethinking Binary Neural Network Design for FPGA Implementation written by Erwei Wang and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Computer Vision – ECCV 2016

Author : Bastian Leibe
Publisher : Springer
ISBN 13 : 3319464930
Total Pages : 902 pages
Book Rating : 4.3/5 (194 download)

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Book Synopsis Computer Vision – ECCV 2016 by : Bastian Leibe

Download or read book Computer Vision – ECCV 2016 written by Bastian Leibe and published by Springer. This book was released on 2016-09-16 with total page 902 pages. Available in PDF, EPUB and Kindle. Book excerpt: The eight-volume set comprising LNCS volumes 9905-9912 constitutes the refereed proceedings of the 14th European Conference on Computer Vision, ECCV 2016, held in Amsterdam, The Netherlands, in October 2016. The 415 revised papers presented were carefully reviewed and selected from 1480 submissions. The papers cover all aspects of computer vision and pattern recognition such as 3D computer vision; computational photography, sensing and display; face and gesture; low-level vision and image processing; motion and tracking; optimization methods; physicsbased vision, photometry and shape-from-X; recognition: detection, categorization, indexing, matching; segmentation, grouping and shape representation; statistical methods and learning; video: events, activities and surveillance; applications. They are organized in topical sections on detection, recognition and retrieval; scene understanding; optimization; image and video processing; learning; action activity and tracking; 3D; and 9 poster sessions.

Applying Neural Networks

Author : Kevin Swingler
Publisher : Morgan Kaufmann
ISBN 13 : 9780126791709
Total Pages : 348 pages
Book Rating : 4.7/5 (917 download)

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Book Synopsis Applying Neural Networks by : Kevin Swingler

Download or read book Applying Neural Networks written by Kevin Swingler and published by Morgan Kaufmann. This book was released on 1996 with total page 348 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is designed to enable the reader to design and run a neural network-based project. It presents everything the reader will need to know to ensure the success of such a project. The book contains a free disk with C and C++ programs, which implement many of the techniques discussed in the book.

2009 IEEE Conference on Computer Vision and Pattern Recognition

Author : IEEE Staff
Publisher :
ISBN 13 : 9781509073504
Total Pages : pages
Book Rating : 4.0/5 (735 download)

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Book Synopsis 2009 IEEE Conference on Computer Vision and Pattern Recognition by : IEEE Staff

Download or read book 2009 IEEE Conference on Computer Vision and Pattern Recognition written by IEEE Staff and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

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.