Dataarkitektur og logisk design

In this book we give an overview of modeling techniques used to describe computer systems to mathematical optimization tools. We give a brief introduction to various classes of mathematical optimization frameworks with special focus on mixed integer linear programming which provides a good balance between solver time and expressiveness. We present four detailed case studies -- instruction set customization, data center resource management, spatial architecture scheduling, and resource allocation in tiled architectures -- showing how MILP can be used and quantifying by how much it outperforms traditional design exploration techniques. This book should help a skilled systems designer to learn techniques for using MILP in their problems, and the skilled optimization expert to understand the types of computer systems problems that MILP can be applied to.

As the number of cores on a chip continues to climb, architects will need to address both bandwidth and power consumption issues related to the interconnection network. Electrical interconnects are not likely to scale well to a large number of processors for energy efficiency reasons, and the problem is compounded by the fact that there is a fixed total power budget for a die, dictated by the amount of heat that can be dissipated without special (and expensive) cooling and packaging techniques. Thus, there is a need to seek alternatives to electrical signaling for on-chip interconnection applications. Photonics, which has a fundamentally different mechanism of signal propagation, offers the potential to not only overcome the drawbacks of electrical signaling, but also enable the architect to build energy efficient, scalable systems. The purpose of this book is to introduce computer architects to the possibilities and challenges of working with photons and designing on-chip photonic interconnection networks.

With growing interest in computer security and the protection of the code and data which execute on commodity computers, the amount of hardware security features in today's processors has increased significantly over the recent years. No longer of just academic interest, security features inside processors have been embraced by industry as well, with a number of commercial secure processor architectures available today. This book aims to give readers insights into the principles behind the design of academic and commercial secure processor architectures. Secure processor architecture research is concerned with exploring and designing hardware features inside computer processors, features which can help protect confidentiality and integrity of the code and data executing on the processor. Unlike traditional processor architecture research that focuses on performance, efficiency, and energy as the first-order design objectives, secure processor architecture design has security as the first-order design objective (while still keeping the others as important design aspects that need to be considered).This book aims to present the different challenges of secure processor architecture design to graduate students interested in research on architecture and hardware security and computer architects working in industry interested in adding security features to their designs. It aims to educate readers about how the different challenges have been solved in the past and what are the best practices, i.e., the principles, for design of new secure processor architectures. Based on the careful review of past work by many computer architects and security researchers, readers also will come to know the five basic principles needed for secure processor architecture design. The book also presents existing research challenges and potential new research directions. Finally, this book presents numerous design suggestions, as well as discusses pitfalls and fallacies that designers should avoid.

This historical survey of parallel processing from 1980 to 2020 is a follow-up to the authors' 1981 Tutorial on Parallel Processing, which covered the state of the art in hardware, programming languages, and applications. Here, we cover the evolution of the field since 1980 in: parallel computers, ranging from the Cyber 205 to clusters now approaching an exaflop, to multicore microprocessors, and Graphic Processing Units (GPUs) in commodity personal devices; parallel programming notations such as OpenMP, MPI message passing, and CUDA streaming notation; and seven parallel applications, such as finite element analysis and computer vision. Some things that looked like they would be major trends in 1981, such as big Single Instruction Multiple Data arrays disappeared for some time but have been revived recently in deep neural network processors. There are now major trends that did not exist in 1980, such as GPUs, distributed memory machines, and parallel processing in nearly every commodity device.This book is intended for those that already have some knowledge of parallel processing today and want to learn about the history of the three areas. In parallel hardware, every major parallel architecture type from 1980 has scaled-up in performance and scaled-out into commodity microprocessors and GPUs, so that every personal and embedded device is a parallel processor. There has been a confluence of parallel architecture types into hybrid parallel systems. Much of the impetus for change has been Moore's Law, but as clock speed increases have stopped and feature size decreases have slowed down, there has been increased demand on parallel processing to continue performance gains. In programming notations and compilers, we observe that the roots of today's programming notations existed before 1980. And that, through a great deal of research, the most widely used programming notations today, although the result of much broadening of these roots, remain close to target system architectures allowing the programmer to almost explicitly use the target's parallelism to the best of their ability. The parallel versions of applications directly or indirectly impact nearly everyone, computer expert or not, and parallelism has brought about major breakthroughs in numerous application areas. Seven parallel applications are studied in this book.

The memory system has the potential to be a hub for future innovation. While conventional memory systems focused primarily on high density, other memory system metrics like energy, security, and reliability are grabbing modern research headlines. With processor performance stagnating, it is also time to consider new programming models that move some application computations into the memory system. This, in turn, will lead to feature-rich memory systems with new interfaces. The past decade has seen a number of memory system innovations that point to this future where the memory system will be much more than dense rows of unintelligent bits. This book takes a tour through recent and prominent research works, touching upon new DRAM chip designs and technologies, near data processing approaches, new memory channel architectures, techniques to tolerate the overheads of refresh and fault tolerance, security attacks and mitigations, and memory scheduling.

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.

This book provides a structured introduction of the key concepts and techniques that enable in-/near-memory computing. For decades, processing-in-memory or near-memory computing has been attracting growing interest due to its potential to break the memory wall. Near-memory computing moves compute logic near the memory, and thereby reduces data movement. Recent work has also shown that certain memories can morph themselves into compute units by exploiting the physical properties of the memory cells, enabling in-situ computing in the memory array. While in- and near-memory computing can circumvent overheads related to data movement, it comes at the cost of restricted flexibility of data representation and computation, design challenges of compute capable memories, and difficulty in system and software integration. Therefore, wide deployment of in-/near-memory computing cannot be accomplished without techniques that enable efficient mapping of data-intensive applications to such devices, without sacrificing accuracy or increasing hardware costs excessively. This book describes various memory substrates amenable to in- and near-memory computing, architectural approaches for designing efficient and reliable computing devices, and opportunities for in-/near-memory acceleration of different classes of applications.

This book summarizes the landscape of cache replacement policies for CPU data caches. The emphasis is on algorithmic issues, so the authors start by defining a taxonomy that places previous policies into two broad categories, which they refer to as coarse-grained and fine-grained policies. Each of these categories is then divided into three subcategories that describe different approaches to solving the cache replacement problem, along with summaries of significant work in each category. Richer factors, including solutions that optimize for metrics beyond cache miss rates, that are tailored to multi-core settings, that consider interactions with prefetchers, and that consider new memory technologies, are then explored. The book concludes by discussing trends and challenges for future work. This book, which assumes that readers will have a basic understanding of computer architecture and caches, will be useful to academics and practitioners across the field.

This book targets computer scientists and engineers who are familiar with concepts in classical computer systems but are curious to learn the general architecture of quantum computing systems. It gives a concise presentation of this new paradigm of computing from a computer systems' point of view without assuming any background in quantum mechanics. As such, it is divided into two parts. The first part of the book provides a gentle overview on the fundamental principles of the quantum theory and their implications for computing. The second part is devoted to state-of-the-art research in designing practical quantum programs, building a scalable software systems stack, and controlling quantum hardware components. Most chapters end with a summary and an outlook for future directions. This book celebrates the remarkable progress that scientists across disciplines have made in the past decades and reveals what roles computer scientists and engineers can play to enable practical-scale quantum computing.

This book introduces readers to emerging persistent memory (PM) technologies that promise the performance of dynamic random-access memory (DRAM) with the durability of traditional storage media, such as hard disks and solid-state drives (SSDs). Persistent memories (PMs), such as Intel's Optane DC persistent memories, are commercially available today. Unlike traditional storage devices, PMs can be accessed over a byte-addressable load-store interface with access latency that is comparable to DRAM. Unfortunately, existing hardware and software systems are ill-equipped to fully avail the potential of these byte-addressable memory technologies as they have been designed to access traditional storage media over a block-based interface. Several mechanisms have been explored in the research literature over the past decade to design hardware and software systems that provide high-performance access to PMs.Because PMs are durable, they can retain data across failures, such as power failures andprogram crashes. Upon a failure, recovery mechanisms may inspect PM data, reconstruct state and resume program execution. Correct recovery of data requires that operations to the PM are properly ordered during normal program execution. Memory persistency models define the order in which memory operations are performed at the PM. Much like memory consistency models, memory persistency models may be relaxed to improve application performance. Several proposals have emerged recently to design memory persistency models for hardware and software systems and for high-level programming languages. These proposals differ in several key aspects; they relax PM ordering constraints, introduce varying programmability burden, and introduce differing granularity of failure atomicity for PM operations.This primer provides a detailed overview of the various classes of the memory persistency models, their implementations in hardware, programming languages and software systems proposed in the recent research literature, and the PM ordering techniques employed by modern processors.

This book constitutes the refereed proceedings of the 37th International Conference on High Performance Computing, ISC High Performance 2022, held in Hamburg, Germany, during May 29 - June 2, 2022.The 18 full papers presented were carefully reviewed and selected from 53 submissions. The papers are categorized into the following topical sub-headings: Architecture, Networks, and Storage; Machine Learning, AI, Emerging Technologies; HPC Algorithms and Applications; Performance Modeling, Evaluation and Analysis; and Programming Environments and Systems Software.

There has been phenomenal uptake of wireless and mobile networking technologies in the past decades. Significant developments have taken place during this time making the wireless technology more affordable, effective, and reliable. This book explains the fundamental principles and protocols of key existing and emerging wireless networking technologies. The book begins with a review of the fundamentals of wireless communications. It covers the basic theories and terminologies of coding and modulation, which maps digital information to the underlying signal, as well as the models to capture the dynamics of wireless signal propagation in the environment. It provides in-depth coverage of the WiFi evolution covering both the mainstream WiFi, which operates in 2.4/5GHz with new versions targeting 6GHz, as well as some of the niche WiFi standards that operate outside the mainstream bands such as 802.11af in 700MHz TV bands, 802.11ah in 900MHz to connect the Internet of Things (IoT), and 802.11ad/ay in 60GHz to support multi-gigabit applications. The book covers the fundamental concepts of cellular networks, examines the advancements brought forth by each generation, and discusses new applications and the underpinning wireless technologies promised by 5G. It also covers a recently developed long-range low-power wireless networking technology called LoRa, which is the fastest growing technology to connect millions of IoT sensors and devices throughout the world. The concluding chapters examine emerging wireless paradigms such as Artificial Intelligence for wireless networking, sensing with wireless signals, and mobile networking with flying base stations carried by drones and unmanned aerial vehicles (UAVs). With many worked-out examples, illustrative figures, and multiple choice questions, this book is an ideal for students and a valuable reference for anyone working in this rapidly evolving field.

The reference text covers emerging wireless communication technologies, such as massive MIMO, mmWave, and visible light communication. It will be an ideal reference text for senior undergraduate, graduate students, and professionals in the fields of electrical engineering, and electronics and communication engineering.

The main goal of Internet of Things (IoT) is to make secure, reliable, and fully automated smart environments. However, there are many technological challenges in deploying IoT. This includes connectivity and networking, timeliness, power and energy consumption dependability, security and privacy, compatibility and longevity, and network/protocol standards. Internet of Things and Secure Smart Environments: Successes and Pitfalls provides a comprehensive overview of recent research and open problems in the area of IoT research.Features:Presents cutting edge topics and research in IoTIncludes contributions from leading worldwide researchersFocuses on IoT architectures for smart environmentsExplores security, privacy, and trustCovers data handling and management (accumulation, abstraction, storage, processing, encryption, fast retrieval, security, and privacy) in IoT for smart environmentsThis book covers state-of-the-art problems, presents solutions, and opens research directions for researchers and scholars in both industry and academia.

In this new edition of the Handbook of Signal Processing Systems, many of the chapters from the previous editions have been updated, and several new chapters have been added. The new contributions include chapters on signal processing methods for light field displays, throughput analysis of dataflow graphs, modeling for reconfigurable signal processing systems, fast Fourier transform architectures, deep neural networks, programmable architectures for histogram of oriented gradients processing, high dynamic range video coding, system-on-chip architectures for data analytics, analysis of finite word-length effects in fixed-point systems, and models of architecture. There are more than 700 tables and illustrations; in this edition over 300 are in color. This new edition of the handbook is organized in three parts. Part I motivates representative applications that drive and apply state-of-the art methods for design and implementation of signal processing systems; Part II discusses architectures for implementing these applications; and Part III focuses on compilers, as well as models of computation and their associated design tools and methodologies. 

A Flash memory is a Non Volatile Memory (NVM) whose "e;unit cells"e; are fabricated in CMOS technology and programmed and erased electrically. In 1971, Frohman-Bentchkowsky developed a folating polysilicon gate tran- sistor [1, 2], in which hot electrons were injected in the floating gate and removed by either Ultra-Violet (UV) internal photoemission or by Fowler- Nordheim tunneling. This is the "e;unit cell"e; of EPROM (Electrically Pro- grammable Read Only Memory), which, consisting of a single transistor, can be very densely integrated. EPROM memories are electrically programmed and erased by UV exposure for 20-30 mins. In the late 1970s, there have been many efforts to develop an electrically erasable EPROM, which resulted in EEPROMs (Electrically Erasable Programmable ROMs). EEPROMs use hot electron tunneling for program and Fowler-Nordheim tunneling for erase. The EEPROM cell consists of two transistors and a tunnel oxide, thus it is two or three times the size of an EPROM. Successively, the combination of hot carrier programming and tunnel erase was rediscovered to achieve a single transistor EEPROM, called Flash EEPROM. The first cell based on this concept has been presented in 1979 [3]; the first commercial product, a 256K memory chip, has been presented by Toshiba in 1984 [4]. The market did not take off until this technology was proven to be reliable and manufacturable [5].

N etwork-based computing domain unifies all best research efforts presented from single computer systems to networked systems to render overwhelming computational power for several modern day applications. Although this power is expected to grow with respect to time due to tech- nological advancements, application requirements impose a continuous thrust on network utilization and on the resources to deliver supreme quality of service. Strictly speaking, network-based computing dornain has no confined scope and each element offers considerable challenges. Any modern day networked application strongly thrives on efficient data storage and management system, which is essentially a Database System. There have been nurnber of books-to-date in this domain that discuss fundamental principles of designing a database systern. Research in this dornain is now far matured and rnany researchers are venturing in this dornain continuously due to a wide variety of challenges posed. In this book, our dornain of interest is in exposing the underlying key challenges in designing algorithms to handle unpredictable requests that arrive at a Distributed Database System(DDBS) and evaluating their performance. These requests are otherwise called as on-line requests arriving at a system to process. Transactions in an on-line Banking service, Airline Reservation systern, Video-on-Demand systern, etc, are few examples of on-line requests.

This book describes several techniques to address variation-related design challenges for analog blocks in mixed-signal systems-on-chip. The methods presented are results from recent research works involving receiver front-end circuits, baseband filter linearization, and data conversion. These circuit-level techniques are described, with their relationships to emerging system-level calibration approaches, to tune the performances of analog circuits with digital assistance or control. Coverage also includes a strategy to utilize on-chip temperature sensors to measure the signal power and linearity characteristics of analog/RF circuits, as demonstrated by test chip measurements. Describes a variety of variation-tolerant analog circuit design examples, including from RF front-ends, high-performance ADCs and baseband filters;Includes built-in testing techniques, linked to current industrial trends;Balances digitally-assisted performance tuning with analog performance tuning and mismatch reduction approaches;Describes theoretical concepts as well as experimental results for test chips designed with variation-aware techniques.

FORTE 2001, formerly FORTE/PSTV conference, is a combined conference of FORTE (Formal Description Techniques for Distributed Systems and Communication Protocols) and PSTV (Protocol Specification, Testing and Verification) conferences. This year the conference has a new name FORTE (Formal Techniques for Networked and Distributed Systems). The previous FORTE began in 1989 and the PSTV conference in 1981. Therefore the new FORTE conference actually has a long history of 21 years. The purpose of this conference is to introduce theories and formal techniques applicable to various engineering stages of networked and distributed systems and to share applications and experiences of them. This FORTE 2001 conference proceedings contains 24 refereed papers and 4 invited papers on the subjects. We regret that many good papers submitted could not be published in this volume due to the lack of space. FORTE 2001 was organized under the auspices of IFIP WG 6.1 by Information and Communications University of Korea. It was financially supported by Ministry of Information and Communication of Korea. We would like to thank every author who submitted a paper to FORTE 2001 and thank the reviewers who generously spent their time on reviewing. Special thanks are due to the reviewers who kindly conducted additional reviews for rigorous review process within a very short time frame. We would like to thank Prof. Guy Leduc, the chairman of IFIP WG 6.1, who made valuable suggestions and shared his experiences for conference organization.