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Author: Warren J. Gross Publisher: Springer ISBN: 3030037304 Category : Technology & Engineering Languages : en Pages : 215
Book Description
This book covers the history and recent developments of stochastic computing. Stochastic computing (SC) was first introduced in the 1960s for logic circuit design, but its origin can be traced back to von Neumann's work on probabilistic logic. In SC, real numbers are encoded by random binary bit streams, and information is carried on the statistics of the binary streams. SC offers advantages such as hardware simplicity and fault tolerance. Its promise in data processing has been shown in applications including neural computation, decoding of error-correcting codes, image processing, spectral transforms and reliability analysis. There are three main parts to this book. The first part, comprising Chapters 1 and 2, provides a history of the technical developments in stochastic computing and a tutorial overview of the field for both novice and seasoned stochastic computing researchers. In the second part, comprising Chapters 3 to 8, we review both well-established and emerging design approaches for stochastic computing systems, with a focus on accuracy, correlation, sequence generation, and synthesis. The last part, comprising Chapters 9 and 10, provides insights into applications in machine learning and error-control coding.
Author: Warren J. Gross Publisher: Springer ISBN: 3030037304 Category : Technology & Engineering Languages : en Pages : 215
Book Description
This book covers the history and recent developments of stochastic computing. Stochastic computing (SC) was first introduced in the 1960s for logic circuit design, but its origin can be traced back to von Neumann's work on probabilistic logic. In SC, real numbers are encoded by random binary bit streams, and information is carried on the statistics of the binary streams. SC offers advantages such as hardware simplicity and fault tolerance. Its promise in data processing has been shown in applications including neural computation, decoding of error-correcting codes, image processing, spectral transforms and reliability analysis. There are three main parts to this book. The first part, comprising Chapters 1 and 2, provides a history of the technical developments in stochastic computing and a tutorial overview of the field for both novice and seasoned stochastic computing researchers. In the second part, comprising Chapters 3 to 8, we review both well-established and emerging design approaches for stochastic computing systems, with a focus on accuracy, correlation, sequence generation, and synthesis. The last part, comprising Chapters 9 and 10, provides insights into applications in machine learning and error-control coding.
Author: Warren J. Gross Publisher: ISBN: 9783030037314 Category : Probabilistic automata Languages : en Pages : 215
Book Description
This book covers the history and recent developments of stochastic computing. Stochastic computing (SC) was first introduced in the 1960s for logic circuit design, but its origin can be traced back to von Neumann's work on probabilistic logic. In SC, real numbers are encoded by random binary bit streams, and information is carried on the statistics of the binary streams. SC offers advantages such as hardware simplicity and fault tolerance. Its promise in data processing has been shown in applications including neural computation, decoding of error-correcting codes, image processing, spectral transforms and reliability analysis. There are three main parts to this book. The first part, comprising Chapters 1 and 2, provides a history of the technical developments in stochastic computing and a tutorial overview of the field for both novice and seasoned stochastic computing researchers. In the second part, comprising Chapters 3 to 8, we review both well-established and emerging design approaches for stochastic computing systems, with a focus on accuracy, correlation, sequence generation, and synthesis. The last part, comprising Chapters 9 and 10, provides insights into applications in machine learning and error-control coding.
Author: Vincent T. Lee Publisher: ISBN: Category : Languages : en Pages : 148
Book Description
The end of Dennard scaling and demands for energy efficient, low power, and high density computing solutions over the past decade has forced exploration of new computing technologies. Stochastic computing is one of these alternative computing technologies which has enjoyed renewed interest and is the primary focus of this dissertation. Stochastic computing is a form of approximate computing which encodes values as probabilistic bitstreams where the ratio of 1s and 0s determines the encoded value. This representation allows stochastic computing to achieve lower operating power, higher computational density, and better error resilience compared to conventional binary-encoded circuits. In its current form, stochastic computing presents a number of challenges before it can become a practical replacement for conventional binary-encoded computing. First, there is little prior work detailing design methodologies to guide effective implementation and integration of stochastic computing into accelerator architectures. Second, the application space where stochastic computing yields compelling gains is far from obvious and has only seen limited exploration. Third, stochastic arithmetic circuits are unintuitive to design because they require careful consideration of correlation and quantization effects. This thesis focuses on new circuit components, applications, architectural considerations, and design techniques to improve the practicality of stochastic computing accelerators. I first propose novel stochastic circuits to improve the accuracy of stochastic computations and augment the range of implementable functions. I then evaluate the viability of stochastic computing with a design space exploration of end-to-end stochastic computing accelerator architectures. In this exploration, I evaluate under what design parameters and conditions stochastic computing accelerators are competitive alternatives to their binary-encoded counterparts. Using these guidelines, I use these results to establish a set of architecture design guidelines to help designers identify when and why they should consider stochastic computing. I then evaluate codesign opportunities and empirically measuring power, area, and energy efficiency for emerging applications. I also propose borrowing techniques from program synthesis such as stochastic synthesis and mixed integer linear programming to automatically synthesize novel stochastic circuits. Finally, I conclude with future directions for further improving the practicality of stochastic computing as well as additional research directions beyond stochastic computing.
Author: Ardalan Najafi Publisher: ISBN: Category : Languages : en Pages :
Book Description
The approximate and stochastic computing have been developed, on the one hand, to address the diminishing gains of technology scaling, and on the other hand, to exploit the intrinsic error resilience of many applications. They, indeed, take advantage of the disparity between the level of accuracy required by the application and that provided by the computing system, for achieving energy efficiency. As of the most important constitutes of an integrated circuit, arithmetic units often lie within the critical path of a processing system. They play a vital role in determining the performance and power consumption of the computing system. In the past decade, the design of the approximate arithmetic units has been in the center of attentions of the VLSI design research community; resulting in a numerous proposed approximate designs in the literature. In spite of a decade work on the approximate computing, there are still unresolved challenges faced by digital designers. The concept of acceptable quality of the results forms the foundation of the approximate and stochastic computing. In view of this fact, it is crucially decisive to have a clear, quantifiable definition of what signifies an acceptable quality. Indeed, the current metrics most often do not capture the requirements of a target application, and hence, mislead to sub-optimal design options for the application. Moreover, non-systematic designs, lack of fair comparisons and reproducible research have resulted in somewhat limited progresses in the field of approximate and stochastic computing. Besides, the accuracy requirements of an application is not a static property and may change across the different phases of the application. Therefore, it is important to systematically develop approximate and stochastic computing platforms which offer a variety of output qualities. In this dissertation, the aim is to take fundamental steps towards resolving the aforementioned challenges. Correspondingly, the following contributions are made in this dissertation. First, to palliate the lack of expressiveness of current metrics, a new parameterizable metric which correlates more precisely to the accuracy of the applications is proposed in this dissertation. Afterwards, the importance of fair comparisons for approximate computing units is underlined in this work. Subsequently, through generalizing and systematically optimizing an architectural template for approximate adders, an architecture is proposed which outperforms its existing counterparts. A conceptual framework for the systematic design of approximate adders including hybrid and non-equally segmented approaches is developed next. The framework discriminates the scenarios where approximate processing does not provide significant benefits from those where it does; in this latter case, it aids in obtaining optimal configurations for the adders. Furthermore, in order to address the dynamic configuration of the error characteristics, a stochastically-tunable adder is proposed which reduces the energy-delay product considerably in comparison with its conventional counterpart. In addition, we develop data-dependent corrections for truncated multipliers, where the proposed architectures surpass the existing approximate multipliers in the literature. The applicability of the proposed methods, and in general approximate computing units is eventually studied in modern applications. The correlation between the errors of a single unit and the whole system's accuracy is also investigated in the applications.
Author: Latesh Malik Publisher: CRC Press ISBN: 1000473503 Category : Computers Languages : en Pages : 359
Book Description
Making use of digital technology for social care is a major responsibility of the computing domain. Social care services require attention for ease in social systems, e-farming, and automation, etc. Thus, the book focuses on suggesting software solutions for supporting social issues, such as health care, learning about and monitoring for disabilities, and providing technical solutions for better living. Technology is enabling people to have access to advances so that they can have better health. To undergo the digital transformation, the current processes need to be completely re-engineered to make use of technologies like the Internet of Things (IoT), big data analytics, artificial intelligence, and others. Furthermore, it is also important to consider digital initiatives in tandem with their cloud strategy instead of treating them in isolation. At present, the world is going through another, possibly even stronger revolution: the use of recent computing models to perform complex cognitive tasks to solve social problems in ways that were previously either highly complicated or extremely resource intensive. This book not only focuses the computing technologies, basic theories, challenges, and implementation but also covers case studies. It focuses on core theories, architectures, and technologies necessary to develop and understand the computing models and their applications. The book also has a high potential to be used as a recommended textbook for research scholars and post-graduate programs. The book deals with a problem-solving approach using recent tools and technology for problems in health care, social care, etc. Interdisciplinary studies are emerging as both necessary and practical in universities. This book helps to improve computational thinking to "understand and change the world’. It will be a link between computing and a variety of other fields. Case studies on social aspects of modern societies and smart cities add to the contents of the book to enhance book adoption potential. This book will be useful to undergraduates, postgraduates, researchers, and industry professionals. Every chapter covers one possible solution in detail, along with results.
Author: Manjaree Pandit Publisher: Springer Nature ISBN: 3030447588 Category : Technology & Engineering Languages : en Pages : 584
Book Description
This book delves into various solution paradigms such as artificial neural network, support vector machine, wavelet transforms, evolutionary computing, swarm intelligence. During the last decade, novel solution technologies based on human and species intelligence have gained immense popularity due to their flexible and unconventional approach. New analytical tools are also being developed to handle big data processing and smart decision making. The idea behind compiling this work is to familiarize researchers, academicians, industry persons and students with various applications of intelligent techniques for producing sustainable, cost-effective and robust solutions of frequently encountered complex, real-world problems in engineering and science disciplines. The practical problems in smart grids, communication, waste management, elimination of harmful elements from nature, etc., are identified, and smart and optimal solutions are proposed.
Author: Johannes Schneider Publisher: Springer Science & Business Media ISBN: 3540345604 Category : Computers Languages : en Pages : 551
Book Description
This book addresses stochastic optimization procedures in a broad manner. The first part offers an overview of relevant optimization philosophies; the second deals with benchmark problems in depth, by applying a selection of optimization procedures. Written primarily with scientists and students from the physical and engineering sciences in mind, this book addresses a larger community of all who wish to learn about stochastic optimization techniques and how to use them.
Author: Dongbin Xiu Publisher: Princeton University Press ISBN: 1400835348 Category : Mathematics Languages : en Pages : 142
Book Description
The@ first graduate-level textbook to focus on fundamental aspects of numerical methods for stochastic computations, this book describes the class of numerical methods based on generalized polynomial chaos (gPC). These fast, efficient, and accurate methods are an extension of the classical spectral methods of high-dimensional random spaces. Designed to simulate complex systems subject to random inputs, these methods are widely used in many areas of computer science and engineering. The book introduces polynomial approximation theory and probability theory; describes the basic theory of gPC methods through numerical examples and rigorous development; details the procedure for converting stochastic equations into deterministic ones; using both the Galerkin and collocation approaches; and discusses the distinct differences and challenges arising from high-dimensional problems. The last section is devoted to the application of gPC methods to critical areas such as inverse problems and data assimilation. Ideal for use by graduate students and researchers both in the classroom and for self-study, Numerical Methods for Stochastic Computations provides the required tools for in-depth research related to stochastic computations. The first graduate-level textbook to focus on the fundamentals of numerical methods for stochastic computations Ideal introduction for graduate courses or self-study Fast, efficient, and accurate numerical methods Polynomial approximation theory and probability theory included Basic gPC methods illustrated through examples
Author: Tianmu Li Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
{Machine learning using deep neural networks is growing in popularity and is demanding increasing computation requirements at the same time. Approximate computing is a promising approach that trades accuracy for performance, and stochastic computing is an especially interesting approach that preserves the compute units of single-bit computation while allowing adjustable compute precision. This dissertation centers around enabling and improving stochastic computing for neural networks, while also discussing works that lead up to stochastic computing and how the techniques developed for stochastic computing are applied to other approximate computing methods and applications other than deep neural networks. We start with 3pxnet, which combines extreme quantization with model pruning. While 3pxnet achieves extremely compact models, it demonstrates limits of binarization, including the inability to scale to higher precision levels and performance bottlenecks from accumulation. This leads us to stochastic computing, which performs single-gate multiplications and additions on probabilistic bit streams. The initial SC neural network implementation in ACOUSTIC aims at maximizing SC performance benefits while achieving usable accuracy. This is achieved through design choices in stream representation, performance optimizations using pooling layers, and training modifications to make single-gate accumulation possible. The subsequent work in GEO improves the stream generation and computation aspects of stochastic computing and reduces the accuracy gap between stochastic computing and fixed-point computing. The accumulation part of SC is further optimized in REX-SC, which allows efficient modeling of SC accumulation during training. During these iterations of the SC algorithm, we developed efficient training pipelines that target various aspects of training for approximate computing. Both forward and backward passes of training are optimized, which allows us to demonstrate model convergence results using SC and other approximate computing methods with limited hardware resources. Finally, we apply the training concept to other applications. In LAC, we show that an almost arbitrary parameterized application can be trained to perform well with approximate computing. At the same time, we can search for the optimal hardware configuration using NAS techniques.
Author: Warren J. Gross
Author: Warren J. Gross
Author: Warren J. Gross
Author: Vincent T. Lee
Author: Ardalan Najafi
Author: Weiqiang Liu
Author: Latesh Malik
Author: Manjaree Pandit
Author: Johannes Schneider
Author: Dongbin Xiu
Author: Tianmu Li