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Stochastic Computing Techniques And Applications
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Book Synopsis Stochastic Computing: Techniques and Applications by : Warren J. Gross
Download or read book Stochastic Computing: Techniques and Applications written by Warren J. Gross and published by Springer. This book was released on 2019-02-18 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Stochastic Computing by : Warren J. Gross
Download or read book Stochastic Computing written by Warren J. Gross and published by . This book was released on 2019 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Towards Practical Stochastic Computing Architectures for Emerging Applications by : Vincent T. Lee
Download or read book Towards Practical Stochastic Computing Architectures for Emerging Applications written by Vincent T. Lee and published by . This book was released on 2019 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Systematic Design of Low-power Processing Elements Using Stochastic and Approximate Computing Techniques by : Ardalan Najafi
Download or read book Systematic Design of Low-power Processing Elements Using Stochastic and Approximate Computing Techniques written by Ardalan Najafi and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Design and Applications of Emerging Computer Systems by : Weiqiang Liu
Download or read book Design and Applications of Emerging Computer Systems written by Weiqiang Liu and published by Springer Nature. This book was released on with total page 745 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Intelligent Computing Applications for Sustainable Real-World Systems by : Manjaree Pandit
Download or read book Intelligent Computing Applications for Sustainable Real-World Systems written by Manjaree Pandit and published by Springer Nature. This book was released on 2020-04-03 with total page 584 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Stochastic Optimization by : Johannes Schneider
Download or read book Stochastic Optimization written by Johannes Schneider and published by Springer Science & Business Media. This book was released on 2007-08-06 with total page 551 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Numerical Methods for Stochastic Computations by : Dongbin Xiu
Download or read book Numerical Methods for Stochastic Computations written by Dongbin Xiu and published by Princeton University Press. This book was released on 2010-07-01 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Book Synopsis Learned Approximate Computing for Machine Learning by : Tianmu Li
Download or read book Learned Approximate Computing for Machine Learning written by Tianmu Li and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: {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.
Book Synopsis Modern Trends in Controlled Stochastic Processes: by : Alexey Piunovskiy
Download or read book Modern Trends in Controlled Stochastic Processes: written by Alexey Piunovskiy and published by Springer Nature. This book was released on 2021-06-04 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents state-of-the-art solution methods and applications of stochastic optimal control. It is a collection of extended papers discussed at the traditional Liverpool workshop on controlled stochastic processes with participants from both the east and the west. New problems are formulated, and progresses of ongoing research are reported. Topics covered in this book include theoretical results and numerical methods for Markov and semi-Markov decision processes, optimal stopping of Markov processes, stochastic games, problems with partial information, optimal filtering, robust control, Q-learning, and self-organizing algorithms. Real-life case studies and applications, e.g., queueing systems, forest management, control of water resources, marketing science, and healthcare, are presented. Scientific researchers and postgraduate students interested in stochastic optimal control,- as well as practitioners will find this book appealing and a valuable reference.
Book Synopsis Probability and Computing by : Michael Mitzenmacher
Download or read book Probability and Computing written by Michael Mitzenmacher and published by Cambridge University Press. This book was released on 2005-01-31 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: Randomization and probabilistic techniques play an important role in modern computer science, with applications ranging from combinatorial optimization and machine learning to communication networks and secure protocols. This 2005 textbook is designed to accompany a one- or two-semester course for advanced undergraduates or beginning graduate students in computer science and applied mathematics. It gives an excellent introduction to the probabilistic techniques and paradigms used in the development of probabilistic algorithms and analyses. It assumes only an elementary background in discrete mathematics and gives a rigorous yet accessible treatment of the material, with numerous examples and applications. The first half of the book covers core material, including random sampling, expectations, Markov's inequality, Chevyshev's inequality, Chernoff bounds, the probabilistic method and Markov chains. The second half covers more advanced topics such as continuous probability, applications of limited independence, entropy, Markov chain Monte Carlo methods and balanced allocations. With its comprehensive selection of topics, along with many examples and exercises, this book is an indispensable teaching tool.
Book Synopsis An Introduction to Stochastic Modeling by : Howard M. Taylor
Download or read book An Introduction to Stochastic Modeling written by Howard M. Taylor and published by Academic Press. This book was released on 2014-05-10 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt: An Introduction to Stochastic Modeling provides information pertinent to the standard concepts and methods of stochastic modeling. This book presents the rich diversity of applications of stochastic processes in the sciences. Organized into nine chapters, this book begins with an overview of diverse types of stochastic models, which predicts a set of possible outcomes weighed by their likelihoods or probabilities. This text then provides exercises in the applications of simple stochastic analysis to appropriate problems. Other chapters consider the study of general functions of independent, identically distributed, nonnegative random variables representing the successive intervals between renewals. This book discusses as well the numerous examples of Markov branching processes that arise naturally in various scientific disciplines. The final chapter deals with queueing models, which aid the design process by predicting system performance. This book is a valuable resource for students of engineering and management science. Engineers will also find this book useful.
Book Synopsis Monte Carlo Strategies in Scientific Computing by : Jun S. Liu
Download or read book Monte Carlo Strategies in Scientific Computing written by Jun S. Liu and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a self-contained and up-to-date treatment of the Monte Carlo method and develops a common framework under which various Monte Carlo techniques can be "standardized" and compared. Given the interdisciplinary nature of the topics and a moderate prerequisite for the reader, this book should be of interest to a broad audience of quantitative researchers such as computational biologists, computer scientists, econometricians, engineers, probabilists, and statisticians. It can also be used as a textbook for a graduate-level course on Monte Carlo methods.
Book Synopsis New Soft Computing Techniques for System Modeling, Pattern Classification and Image Processing by : Leszek Rutkowski
Download or read book New Soft Computing Techniques for System Modeling, Pattern Classification and Image Processing written by Leszek Rutkowski and published by Springer. This book was released on 2013-03-09 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Science has made great progress in the twentieth century, with the establishment of proper disciplines in the fields of physics, computer science, molecular biology, and many others. At the same time, there have also emerged many engineering ideas that are interdisciplinary in nature, beyond the realm of such orthodox disciplines. These in clude, for example, artificial intelligence, fuzzy logic, artificial neural networks, evolutional computation, data mining, and so on. In or der to generate new technology that is truly human-friendly in the twenty-first century, integration of various methods beyond specific disciplines is required. Soft computing is a key concept for the creation of such human friendly technology in our modern information society. Professor Rutkowski is a pioneer in this field, having devoted himself for many years to publishing a large variety of original work. The present vol ume, based mostly on his own work, is a milestone in the devel opment of soft computing, integrating various disciplines from the fields of information science and engineering. The book consists of three parts, the first of which is devoted to probabilistic neural net works. Neural excitation is stochastic, so it is natural to investi gate the Bayesian properties of connectionist structures developed by Professor Rutkowski. This new approach has proven to be par ticularly useful for handling regression and classification problems vi Preface in time-varying environments. Throughout this book, major themes are selected from theoretical subjects that are tightly connected with challenging applications.
Book Synopsis Stochastic Computing by : John Sartori
Download or read book Stochastic Computing written by John Sartori and published by . This book was released on 2011-12 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic computing techniques that exploit error resilience and enable energy-reliability tradeoffs have been proposed at nearly all levels of the computing stack. This reviews work in the area of stochastic computing and discuss the promise and challenges of the field.
Book Synopsis Applications of Stochastic Programming by : Stein W. Wallace
Download or read book Applications of Stochastic Programming written by Stein W. Wallace and published by SIAM. This book was released on 2005-06-01 with total page 701 pages. Available in PDF, EPUB and Kindle. Book excerpt: Consisting of two parts, this book presents papers describing publicly available stochastic programming systems that are operational. It presents a diverse collection of application papers in areas such as production, supply chain and scheduling, gaming, environmental and pollution control, financial modeling, telecommunications, and electricity.
Download or read book Analog Computing written by Bernd Ulmann and published by Walter de Gruyter GmbH & Co KG. This book was released on 2022-11-07 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analog computing is one of the main pillars of Unconventional Computing. Almost forgotten for decades, we now see an ever-increasing interest in electronic analog computing because it offers a path to high-performance and highly energy-efficient computing. These characteristics are of great importance in a world where vast amounts of electric energy are consumed by today’s computer systems. Analog computing can deliver efficient solutions to many computing problems, ranging from general purpose analog computation to specialised systems like analog artificial neural networks. The book “Analog Computing” has established itself over the past decade as the standard textbook on the subject and has been substantially extended in this second edition, which includes more than 300 additional bibliographical entries, and has been expanded in many areas to include much greater detail. These enhancements will confirm this book’s status as the leading work in the field. It covers the history of analog computing from the Antikythera Mechanism to recent electronic analog computers and uses a wide variety of worked examples to provide a comprehensive introduction to programming analog computers. It also describes hybrid computers, digital differential analysers, the simulation of analog computers, stochastic computers, and provides a comprehensive treatment of classic and current analog computer applications. The last chapter looks into the promising future of analog computing.
