Ai For Computer Architecture
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Ai For Computer Architecture
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Author : Lizhong Chen
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Ai For Computer Architecture written by Lizhong Chen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Technology & Engineering categories.
Artificial intelligence has already enabled pivotal advances in diverse fields, yet its impact on computer architecture has only just begun. In particular, recent work has explored broader application to the design, optimization, and simulation of computer architecture. Notably, machine-learning-based strategies often surpass prior state-of-the-art analytical, heuristic, and human-expert approaches. This book reviews the application of machine learning in system-wide simulation and run-time optimization, and in many individual components such as caches/memories, branch predictors, networks-on-chip, and GPUs. The book further analyzes current practice to highlight useful design strategies and identify areas for future work, based on optimized implementation strategies, opportune extensions to existing work, and ambitious long term possibilities. Taken together, these strategies and techniques present a promising future for increasingly automated computer architecture designs.
Processing In Memory For Ai
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Author : Joo-Young Kim
language : en
Publisher: Springer Nature
Release Date : 2022-07-09
Processing In Memory For Ai written by Joo-Young Kim and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-09 with Technology & Engineering categories.
This book provides a comprehensive introduction to processing-in-memory (PIM) technology, from its architectures to circuits implementations on multiple memory types and describes how it can be a viable computer architecture in the era of AI and big data. The authors summarize the challenges of AI hardware systems, processing-in-memory (PIM) constraints and approaches to derive system-level requirements for a practical and feasible PIM solution. The presentation focuses on feasible PIM solutions that can be implemented and used in real systems, including architectures, circuits, and implementation cases for each major memory type (SRAM, DRAM, and ReRAM).
Deep Learning For Computer Architects
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Author : Brandon Reagen
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Deep Learning For Computer Architects written by Brandon Reagen and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Technology & Engineering categories.
Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems. The emergence of deep learning is widely attributed to a virtuous cycle whereby fundamental advancements in training deeper models were enabled by the availability of massive datasets and high-performance computer hardware. This text serves as a primer for computer architects in a new and rapidly evolving field. We review how machine learning has evolved since its inception in the 1960s and track the key developments leading up to the emergence of the powerful deep learning techniques that emerged in the last decade. Next we review representative workloads, including the most commonly used datasets and seminal networks across a variety of domains. In addition to discussing the workloads themselves, we also detail the most popular deep learning tools and show how aspiring practitioners can use the tools with the workloads to characterize and optimize DNNs. The remainder of the book is dedicated to the design and optimization of hardware and architectures for machine learning. As high-performance hardware was so instrumental in the success of machine learning becoming a practical solution, this chapter recounts a variety of optimizations proposed recently to further improve future designs. Finally, we present a review of recent research published in the area as well as a taxonomy to help readers understand how various contributions fall in context.
Learning Deep Architectures For Ai
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Author : Yoshua Bengio
language : en
Publisher: Now Publishers Inc
Release Date : 2009
Learning Deep Architectures For Ai written by Yoshua Bengio and has been published by Now Publishers Inc this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Computational learning theory categories.
Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.
Computers For Artificial Intelligence Processing
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Author : Benjamin W. Wah
language : en
Publisher: Wiley-Interscience
Release Date : 1990-10-12
Computers For Artificial Intelligence Processing written by Benjamin W. Wah and has been published by Wiley-Interscience this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990-10-12 with Computers categories.
The present book supports the increasing complexity and the growing need for computational power of artificial intelligence (AI) by providing comprehensive treatments of new hardware and software engineering met in AI language design and applications. The book is a collection of 16 substantial papers (chapters), the contributors being 51 well-known researchers in the AI fields. The papers are grouped into the following five sections: Section 1 represents a well documented survey on symbolic processing computers. Section 2 (Language-based AI Architectures) supports the design and implementation of AI language-oriented computers. Three (2-4) chapters are devoted to (computer architecture concerning) sequential Lisp processing: architectural features of Lisp computers, Symbolics’ Lisp computer architecture, memory management and performance evaluation of a Lisp machine system. Other three (5-7) chapters discuss multiprocessing and parallel processing of Lisp (and general functional) programs. The last two chapters of section 2 are presenting architectures supporting object-oriented programming (Smalltalk) and production systems. Section 3 (Multiprocessor AI Architecture) contains two (10-11) chapters, dealing with Connection Machine architecture and its applications, and with the design of data/knowledge base machines for AI processing. Section 4 (Connectionist Architectures and Applications) include two (12-13) chapters, illustrating the connectionist model architecture design and learning. Section 5 (Software Architectures for AI Applications) is made up of three (14-16) chapters, analysing the relationship between AI and software engineering, the development tools for AI programs, and the problem of AI hardware and software reliability. This book addresses a wide range of AI readers, from beginners to professionals. It carries forth doubtless qualities: compact and well-dimensioned chapters, comprehensively written by AI remarkable professionals, covering up-to-date AI topics and trends.
Multi Computer Architectures For Artificial Intelligence
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Author : Leonard Uhr
language : en
Publisher: Wiley-Interscience
Release Date : 1987-02-20
Multi Computer Architectures For Artificial Intelligence written by Leonard Uhr and has been published by Wiley-Interscience this book supported file pdf, txt, epub, kindle and other format this book has been release on 1987-02-20 with Computers categories.
This volume is the most current and complete evaluation of multi-computer networks. It presents an up-to-date and detailed survey which describes the large number of multi-computer architectures that have been built during the past few years, and the even greater variety that are presently being built, designed, and explored. Special emphasis is given to approaches that show promise for handling complex real-world problems in real time. Describes the range of problems that confront workers in artificial intelligence (AI), and explains some of the major approaches being taken by AI researchers, focusing on the robust, flexible, extremely fast (hence highly parallel) systems. Presents judgements and opinions as to the most promising multi-computer archtectures for AI--including a number of exciting multi-computer structures.
Architecture In The Age Of Artificial Intelligence
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Author : Neil Leach
language : en
Publisher: Bloomsbury Publishing
Release Date : 2021-11-18
Architecture In The Age Of Artificial Intelligence written by Neil Leach and has been published by Bloomsbury Publishing this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-18 with Architecture categories.
Artificial intelligence is everywhere – from the apps on our phones to the algorithms of search engines. Without us noticing, the AI revolution has arrived. But what does this mean for the world of design? The first volume in a two-book series, Architecture in the Age of Artificial Intelligence introduces AI for designers and considers its positive potential for the future of architecture and design. Explaining what AI is and how it works, the book examines how different manifestations of AI will impact the discipline and profession of architecture. Highlighting current case-studies as well as near-future applications, it shows how AI is already being used as a powerful design tool, and how AI-driven information systems will soon transform the design of buildings and cities. Far-sighted, provocative and challenging, yet rooted in careful research and cautious speculation, this book, written by architect and theorist Neil Leach, is a must-read for all architects and designers – including students of architecture and all design professionals interested in keeping their practice at the cutting edge of technology.
Parallel Computation And Computers For Artificial Intelligence
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Author : J.S. Kowalik
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Parallel Computation And Computers For Artificial Intelligence written by J.S. Kowalik 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 Computers categories.
It has been widely recognized that artificial intelligence computations offer large potential for distributed and parallel processing. Unfortunately, not much is known about designing parallel AI algorithms and efficient, easy-to-use parallel computer architectures for AI applications. The field of parallel computation and computers for AI is in its infancy, but some significant ideas have appeared and initial practical experience has become available. The purpose of this book has been to collect in one volume contributions from several leading researchers and pioneers of AI that represent a sample of these ideas and experiences. This sample does not include all schools of thought nor contributions from all leading researchers, but it covers a relatively wide variety of views and topics and in this sense can be helpful in assessing the state ofthe art. We hope that the book will serve, at least, as a pointer to more specialized literature and that it will stimulate interest in the area of parallel AI processing. It has been a great pleasure and a privilege to cooperate with all contributors to this volume. They have my warmest thanks and gratitude. Mrs. Birgitta Knapp has assisted me in the editorial task and demonstrated a great deal of skill and patience. Janusz S. Kowalik vii INTRODUCTION Artificial intelligence (AI) computer programs can be very time-consuming.
Efficient Processing Of Deep Neural Networks
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Author : Vivienne Sze
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Efficient Processing Of Deep Neural Networks written by Vivienne Sze and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Technology & Engineering 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 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.
Vlsi For Artificial Intelligence
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Author : Jose G. Delgado-Frias
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Vlsi For Artificial Intelligence written by Jose G. Delgado-Frias 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.