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This book describes novel methods for network-on-chip (NoC) design, using source-synchronous high-speed resonant clocks. The authors discuss NoCs from the bottom up, providing circuit level details, before providing architectural simulations. As a result, readers will get a complete picture of how a NoC can be designed and optimized. Using the methods described in this book, readers are enabled to design NoCs that are 5X better than existing approaches in terms of latency and throughput and can also sustain a significantly greater amount of traffic.
"IEEE Press is pleased to bring you this Second Edition of Phillip A. Laplante's best-selling and widely-acclaimed practical guide to building real-time systems. This book is essential for improved system designs, faster computation, better insights, and ultimate cost savings. Unlike any other book in the field, REAL-TIME SYSTEMS DESIGN AND ANALYSIS provides a holistic, systems-based approach that is devised to help engineers write problem-solving software. Laplante's no-nonsense guide to real-time system design features practical coverage of: Related technologies and their histories Time-saving tips * Hands-on instructions Pascal code Insights into decreasing ramp-up times and more!"
This book constitutes the refereed proceedings of the 8th International Symposium on Reconfigurable Computing: Architectures, Tools and Applications, ARC 2012, held in Hongkong, China, in March 2012. The 35 revised papers presented, consisting of 25 full papers and 10 poster papers were carefully reviewed and selected from 44 submissions. The topics covered are applied RC design methods and tools, applied RC architectures, applied RC applications and critical issues in applied RC.
This work is a comprehensive study of the field. It provides an entry point to the novice willing to move in the research field reconfigurable computing, FPGA and system on programmable chip design. The book can also be used as teaching reference for a graduate course in computer engineering, or as reference to advance electrical and computer engineers. It provides a very strong theoretical and practical background to the field, from the early Estrin’s machine to the very modern architecture such as embedded logic devices.
Big Data Analytics in Cyber-Physical Systems: Machine Learning for the Internet of Things examines sensor signal processing, IoT gateways, optimization and decision-making, intelligent mobility, and implementation of machine learning algorithms in embedded systems. This book focuses on the interaction between IoT technology and the mathematical tools used to evaluate the extracted data of those systems. Each chapter provides the reader with a broad list of data analytics and machine learning methods for multiple IoT applications. Additionally, this volume addresses the educational transfer needed to incorporate these technologies into our society by examining new platforms for IoT in schools, new courses and concepts for universities and adult education on IoT and data science. - Bridges the gap between IoT, CPS, and mathematical modelling - Features numerous use cases that discuss how concepts are applied in different domains and applications - Provides "best practices", "winning stories" and "real-world examples" to complement innovation - Includes highlights of mathematical foundations of signal processing and machine learning in CPS and IoT
This title serves as an introduction ans reference for the field, with the papers that have shaped the hardware/software co-design since its inception in the early 90s.
This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications.
Conceptual and precise, Modern Processor Design brings together numerous microarchitectural techniques in a clear, understandable framework that is easily accessible to both graduate and undergraduate students. Complex practices are distilled into foundational principles to reveal the authors insights and hands-on experience in the effective design of contemporary high-performance micro-processors for mobile, desktop, and server markets. Key theoretical and foundational principles are presented in a systematic way to ensure comprehension of important implementation issues. The text presents fundamental concepts and foundational techniques such as processor design, pipelined processors, memory and I/O systems, and especially superscalar organization and implementations. Two case studies and an extensive survey of actual commercial superscalar processors reveal real-world developments in processor design and performance. A thorough overview of advanced instruction flow techniques, including developments in advanced branch predictors, is incorporated. Each chapter concludes with homework problems that will institute the groundwork for emerging techniques in the field and an introduction to multiprocessor systems.
Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.