Dataarkitektur og logisk design

Understanding and implementing the brain's computational paradigm is the one true grand challenge facing computer researchers. Not only are the brain's computational capabilities far beyond those of conventional computers, its energy efficiency is truly remarkable. This book, written from the perspective of a computer designer and targeted at computer researchers, is intended to give both background and lay out a course of action for studying the brain's computational paradigm. It contains a mix of concepts and ideas drawn from computational neuroscience, combined with those of the author.As background, relevant biological features are described in terms of their computational and communication properties. The brain's neocortex is constructed of massively interconnected neurons that compute and communicate via voltage spikes, and a strong argument can be made that precise spike timing is an essential element of the paradigm. Drawing from the biological features, a mathematics-based computational paradigm is constructed. The key feature is spiking neurons that perform communication and processing in space-time, with emphasis on time. In these paradigms, time is used as a freely available resource for both communication and computation.Neuron models are first discussed in general, and one is chosen for detailed development. Using the model, single-neuron computation is first explored. Neuron inputs are encoded as spike patterns, and the neuron is trained to identify input pattern similarities. Individual neurons are building blocks for constructing larger ensembles, referred to as "e;columns"e;. These columns are trained in an unsupervised manner and operate collectively to perform the basic cognitive function of pattern clustering. Similar input patterns are mapped to a much smaller set of similar output patterns, thereby dividing the input patterns into identifiable clusters. Larger cognitive systems are formed by combining columns into a hierarchical architecture. These higher level architectures are the subject of ongoing study, and progress to date is described in detail in later chapters. Simulation plays a major role in model development, and the simulation infrastructure developed by the author is described.

This book targets engineers and researchers familiar with basic computer architecture concepts who are interested in learning about on-chip networks. This work is designed to be a short synthesis of the most critical concepts in on-chip network design. It is a resource for both understanding on-chip network basics and for providing an overview of state of-the-art research in on-chip networks. We believe that an overview that teaches both fundamental concepts and highlights state-of-the-art designs will be of great value to both graduate students and industry engineers. While not an exhaustive text, we hope to illuminate fundamental concepts for the reader as well as identify trends and gaps in on-chip network research. With the rapid advances in this field, we felt it was timely to update and review the state of the art in this second edition. We introduce two new chapters at the end of the book. We have updated the latest research of the past years throughout the book and also expanded our coverage of fundamental concepts to include several research ideas that have now made their way into products and, in our opinion, should be textbook concepts that all on-chip network practitioners should know. For example, these fundamental concepts include message passing, multicast routing, and bubble flow control schemes.

Performance evaluation is at the foundation of computer architecture research and development. Contemporary microprocessors are so complex that architects cannot design systems based on intuition and simple models only. Adequate performance evaluation methods are absolutely crucial to steer the research and development process in the right direction. However, rigorous performance evaluation is non-trivial as there are multiple aspects to performance evaluation, such as picking workloads, selecting an appropriate modeling or simulation approach, running the model and interpreting the results using meaningful metrics. Each of these aspects is equally important and a performance evaluation method that lacks rigor in any of these crucial aspects may lead to inaccurate performance data and may drive research and development in a wrong direction. The goal of this book is to present an overview of the current state-of-the-art in computer architecture performance evaluation, with a special emphasis on methods for exploring processor architectures. The book focuses on fundamental concepts and ideas for obtaining accurate performance data. The book covers various topics in performance evaluation, ranging from performance metrics, to workload selection, to various modeling approaches including mechanistic and empirical modeling. And because simulation is by far the most prevalent modeling technique, more than half the book's content is devoted to simulation. The book provides an overview of the simulation techniques in the computer designer's toolbox, followed by various simulation acceleration techniques including sampled simulation, statistical simulation, parallel simulation and hardware-accelerated simulation. Table of Contents: Introduction / Performance Metrics / Workload Design / Analytical Performance Modeling / Simulation / Sampled Simulation / Statistical Simulation / Parallel Simulation and Hardware Acceleration / Concluding Remarks

For many years, most computer architects have pursued one primary goal: performance. Architects have translated the ever-increasing abundance of ever-faster transistors provided by Moore's law into remarkable increases in performance. Recently, however, the bounty provided by Moore's law has been accompanied by several challenges that have arisen as devices have become smaller, including a decrease in dependability due to physical faults. In this book, we focus on the dependability challenge and the fault tolerance solutions that architects are developing to overcome it. The two main purposes of this book are to explore the key ideas in fault-tolerant computer architecture and to present the current state-of-the-art - over approximately the past 10 years - in academia and industry. Table of Contents: Introduction / Error Detection / Error Recovery / Diagnosis / Self-Repair / The Future

Design for security is an essential aspect of the design of future computers. However, security is not well understood by the computer architecture community. Many important security aspects have evolved over the last several decades in the cryptography, operating systems, and networking communities. This book attempts to introduce the computer architecture student, researcher, or practitioner to the basic concepts of security and threat-based design. Past work in different security communities can inform our thinking and provide a rich set of technologies for building architectural support for security into all future computers and embedded computing devices and appliances. I have tried to keep the book short, which means that many interesting topics and applications could not be included. What the book focuses on are the fundamental security concepts, across different security communities, that should be understood by any computer architect trying to design or evaluate security-aware computer architectures.

Multithreaded architectures now appear across the entire range of computing devices, from the highest-performing general purpose devices to low-end embedded processors. Multithreading enables a processor core to more effectively utilize its computational resources, as a stall in one thread need not cause execution resources to be idle. This enables the computer architect to maximize performance within area constraints, power constraints, or energy constraints. However, the architectural options for the processor designer or architect looking to implement multithreading are quite extensive and varied, as evidenced not only by the research literature but also by the variety of commercial implementations. This book introduces the basic concepts of multithreading, describes a number of models of multithreading, and then develops the three classic models (coarse-grain, fine-grain, and simultaneous multithreading) in greater detail. It describes a wide variety of architectural and software design tradeoffs, as well as opportunities specific to multithreading architectures. Finally, it details a number of important commercial and academic hardware implementations of multithreading. Table of Contents: Introduction / Multithreaded Execution Models / Coarse-Grain Multithreading / Fine-Grain Multithreading / Simultaneous Multithreading / Managing Contention / New Opportunities for Multithreaded Processors / Experimentation and Metrics / Implementations of Multithreaded Processors / Conclusion

From driving, flying, and swimming, to digging for unknown objects in space exploration, autonomous robots take on varied shapes and sizes. In part, autonomous robots are designed to perform tasks that are too dirty, dull, or dangerous for humans. With nontrivial autonomy and volition, they may soon claim their own place in human society. These robots will be our allies as we strive for understanding our natural and man-made environments and build positive synergies around us. Although we may never perfect replication of biological capabilities in robots, we must harness the inevitable emergence of robots that synchronizes with our own capacities to live, learn, and grow. This book is a snapshot of motivations and methodologies for our collective attempts to transform our lives and enable us to cohabit with robots that work with and for us. It reviews and guides the reader to seminal and continual developments that are the foundations for successful paradigms. It attempts to demystify the abilities and limitations of robots. It is a progress report on the continuing work that will fuel future endeavors. Table of Contents: Part I: Preliminaries/Agency, Motion, and Anatomy/Behaviors / Architectures / Affect/Sensors / Manipulators/Part II: Mobility/Potential Fields/Roadmaps / Reactive Navigation / Multi-Robot Mapping: Brick and Mortar Strategy / Part III: State of the Art / Multi-Robotics Phenomena / Human-Robot Interaction / Fuzzy Control / Decision Theory and Game Theory / Part IV: On the Horizon / Applications: Macro and Micro Robots / References / Author Biography / Discussion

The advent of multicore processors has renewed interest in the idea of incorporating transactions into the programming model used to write parallel programs. This approach, known as transactional memory, offers an alternative, and hopefully better, way to coordinate concurrent threads. The ACI (atomicity, consistency, isolation) properties of transactions provide a foundation to ensure that concurrent reads and writes of shared data do not produce inconsistent or incorrect results. At a higher level, a computation wrapped in a transaction executes atomically - either it completes successfully and commits its result in its entirety or it aborts. In addition, isolation ensures the transaction produces the same result as if no other transactions were executing concurrently. Although transactions are not a parallel programming panacea, they shift much of the burden of synchronizing and coordinating parallel computations from a programmer to a compiler, to a language runtime system, or to hardware. The challenge for the system implementers is to build an efficient transactional memory infrastructure. This book presents an overview of the state of the art in the design and implementation of transactional memory systems, as of early spring 2010. Table of Contents: Introduction / Basic Transactions / Building on Basic Transactions / Software Transactional Memory / Hardware-Supported Transactional Memory / Conclusions

Having hit power limitations to even more aggressive out-of-order execution in processor cores, many architects in the past decade have turned to single-instruction-multiple-data (SIMD) execution to increase single-threaded performance. SIMD execution, or having a single instruction drive execution of an identical operation on multiple data items, was already well established as a technique to efficiently exploit data parallelism. Furthermore, support for it was already included in many commodity processors. However, in the past decade, SIMD execution has seen a dramatic increase in the set of applications using it, which has motivated big improvements in hardware support in mainstream microprocessors. The easiest way to provide a big performance boost to SIMD hardware is to make it wider-i.e., increase the number of data items hardware operates on simultaneously. Indeed, microprocessor vendors have done this. However, as we exploit more data parallelism in applications, certain challenges can negatively impact performance. In particular, conditional execution, non-contiguous memory accesses, and the presence of some dependences across data items are key roadblocks to achieving peak performance with SIMD execution. This book first describes data parallelism, and why it is so common in popular applications. We then describe SIMD execution, and explain where its performance and energy benefits come from compared to other techniques to exploit parallelism. Finally, we describe SIMD hardware support in current commodity microprocessors. This includes both expected design tradeoffs, as well as unexpected ones, as we work to overcome challenges encountered when trying to map real software to SIMD execution.

As conventional memory technologies such as DRAM and Flash run into scaling challenges, architects and system designers are forced to look at alternative technologies for building future computer systems. This synthesis lecture begins by listing the requirements for a next generation memory technology and briefly surveys the landscape of novel non-volatile memories. Among these, Phase Change Memory (PCM) is emerging as a leading contender, and the authors discuss the material, device, and circuit advances underlying this exciting technology. The lecture then describes architectural solutions to enable PCM for main memories. Finally, the authors explore the impact of such byte-addressable non-volatile memories on future storage and system designs. Table of Contents: Next Generation Memory Technologies / Architecting PCM for Main Memories / Tolerating Slow Writes in PCM / Wear Leveling for Durability / Wear Leveling Under Adversarial Settings / Error Resilience in Phase Change Memories / Storage and System Design With Emerging Non-Volatile Memories

A key determinant of overall system performance and power dissipation is the cache hierarchy since access to off-chip memory consumes many more cycles and energy than on-chip accesses. In addition, multi-core processors are expected to place ever higher bandwidth demands on the memory system. All these issues make it important to avoid off-chip memory access by improving the efficiency of the on-chip cache. Future multi-core processors will have many large cache banks connected by a network and shared by many cores. Hence, many important problems must be solved: cache resources must be allocated across many cores, data must be placed in cache banks that are near the accessing core, and the most important data must be identified for retention. Finally, difficulties in scaling existing technologies require adapting to and exploiting new technology constraints. The book attempts a synthesis of recent cache research that has focused on innovations for multi-core processors. It is an excellent starting point for early-stage graduate students, researchers, and practitioners who wish to understand the landscape of recent cache research. The book is suitable as a reference for advanced computer architecture classes as well as for experienced researchers and VLSI engineers. Table of Contents: Basic Elements of Large Cache Design / Organizing Data in CMP Last Level Caches / Policies Impacting Cache Hit Rates / Interconnection Networks within Large Caches / Technology / Concluding Remarks

Compiling for parallelism is a longstanding topic of compiler research. This book describes the fundamental principles of compiling "e;regular"e; numerical programs for parallelism. We begin with an explanation of analyses that allow a compiler to understand the interaction of data reads and writes in different statements and loop iterations during program execution. These analyses include dependence analysis, use-def analysis and pointer analysis. Next, we describe how the results of these analyses are used to enable transformations that make loops more amenable to parallelization, and discuss transformations that expose parallelism to target shared memory multicore and vector processors. We then discuss some problems that arise when parallelizing programs for execution on distributed memory machines. Finally, we conclude with an overview of solving Diophantine equations and suggestions for further readings in the topics of this book to enable the interested reader to delve deeper into the field. Table of Contents: Introduction and overview / Dependence analysis, dependence graphs and alias analysis / Program parallelization / Transformations to modify and eliminate dependences / Transformation of iterative and recursive constructs / Compiling for distributed memory machines / Solving Diophantine equations / A guide to further reading

Datacenter networks provide the communication substrate for large parallel computer systems that form the ecosystem for high performance computing (HPC) systems and modern Internet applications. The design of new datacenter networks is motivated by an array of applications ranging from communication intensive climatology, complex material simulations and molecular dynamics to such Internet applications as Web search, language translation, collaborative Internet applications, streaming video and voice-over-IP. For both Supercomputing and Cloud Computing the network enables distributed applications to communicate and interoperate in an orchestrated and efficient way. This book describes the design and engineering tradeoffs of datacenter networks. It describes interconnection networks from topology and network architecture to routing algorithms, and presents opportunities for taking advantage of the emerging technology trends that are influencing router microarchitecture. With the emergence of "e;many-core"e; processor chips, it is evident that we will also need "e;many-port"e; routing chips to provide a bandwidth-rich network to avoid the performance limiting effects of Amdahl's Law. We provide an overview of conventional topologies and their routing algorithms and show how technology, signaling rates and cost-effective optics are motivating new network topologies that scale up to millions of hosts. The book also provides detailed case studies of two high performance parallel computer systems and their networks. Table of Contents: Introduction / Background / Topology Basics / High-Radix Topologies / Routing / Scalable Switch Microarchitecture / System Packaging / Case Studies / Closing Remarks

Dynamic binary modification tools form a software layer between a running application and the underlying operating system, providing the powerful opportunity to inspect and potentially modify every user-level guest application instruction that executes. Toolkits built upon this technology have enabled computer architects to build powerful simulators and emulators for design-space exploration, compiler writers to analyze and debug the code generated by their compilers, software developers to fully explore the features, bottlenecks, and performance of their software, and even end-users to extend the functionality of proprietary software running on their computers. Several dynamic binary modification systems are freely available today that place this power into the hands of the end user. While these systems are quite complex internally, they mask that complexity with an easy-to-learn API that allows a typical user to ramp up fairly quickly and build any of a number of powerful tools. Meanwhile, these tools are robust enough to form the foundation for software products in use today. This book serves as a primer for researchers interested in dynamic binary modification systems, their internal design structure, and the wide range of tools that can be built leveraging these systems. The hands-on examples presented throughout form a solid foundation for designing and constructing more complex tools, with an appreciation for the techniques necessary to make those tools robust and efficient. Meanwhile, the reader will get an appreciation for the internal design of the engines themselves. Table of Contents: Dynamic Binary Modification: Overview / Using a Dynamic Binary Modifier / Program Analysis and Debugging / Active Program Modification / Architectural Exploration / Advanced System Internals / Historical Perspectives / Summary and Observations

Quantum computers can (in theory) solve certain problems far faster than a classical computer running any known classical algorithm. While existing technologies for building quantum computers are in their infancy, it is not too early to consider their scalability and reliability in the context of the design of large-scale quantum computers. To architect such systems, one must understand what it takes to design and model a balanced, fault-tolerant quantum computer architecture. The goal of this lecture is to provide architectural abstractions for the design of a quantum computer and to explore the systems-level challenges in achieving scalable, fault-tolerant quantum computation. In this lecture, we provide an engineering-oriented introduction to quantum computation with an overview of the theory behind key quantum algorithms. Next, we look at architectural case studies based upon experimental data and future projections for quantum computation implemented using trapped ions. While we focus here on architectures targeted for realization using trapped ions, the techniques for quantum computer architecture design, quantum fault-tolerance, and compilation described in this lecture are applicable to many other physical technologies that may be viable candidates for building a large-scale quantum computing system. We also discuss general issues involved with programming a quantum computer as well as a discussion of work on quantum architectures based on quantum teleportation. Finally, we consider some of the open issues remaining in the design of quantum computers. Table of Contents: Introduction / Basic Elements for Quantum Computation / Key Quantum Algorithms / Building Reliable and Scalable Quantum Architectures / Simulation of Quantum Computation / Architectural Elements / Case Study: The Quantum Logic Array Architecture / Programming the Quantum Architecture / Using the QLA for Quantum Simulation: The Transverse Ising Model / Teleportation-Based Quantum Architectures / Concluding Remarks

Chip multiprocessors - also called multi-core microprocessors or CMPs for short - are now the only way to build high-performance microprocessors, for a variety of reasons. Large uniprocessors are no longer scaling in performance, because it is only possible to extract a limited amount of parallelism from a typical instruction stream using conventional superscalar instruction issue techniques. In addition, one cannot simply ratchet up the clock speed on today's processors, or the power dissipation will become prohibitive in all but water-cooled systems. Compounding these problems is the simple fact that with the immense numbers of transistors available on today's microprocessor chips, it is too costly to design and debug ever-larger processors every year or two. CMPs avoid these problems by filling up a processor die with multiple, relatively simpler processor cores instead of just one huge core. The exact size of a CMP's cores can vary from very simple pipelines to moderately complex superscalar processors, but once a core has been selected the CMP's performance can easily scale across silicon process generations simply by stamping down more copies of the hard-to-design, high-speed processor core in each successive chip generation. In addition, parallel code execution, obtained by spreading multiple threads of execution across the various cores, can achieve significantly higher performance than would be possible using only a single core. While parallel threads are already common in many useful workloads, there are still important workloads that are hard to divide into parallel threads. The low inter-processor communication latency between the cores in a CMP helps make a much wider range of applications viable candidates for parallel execution than was possible with conventional, multi-chip multiprocessors; nevertheless, limited parallelism in key applications is the main factor limiting acceptance of CMPs in some types of systems. After a discussion of the basic pros and cons of CMPs when they are compared with conventional uniprocessors, this book examines how CMPs can best be designed to handle two radically different kinds of workloads that are likely to be used with a CMP: highly parallel, throughput-sensitive applications at one end of the spectrum, and less parallel, latency-sensitive applications at the other. Throughput-sensitive applications, such as server workloads that handle many independent transactions at once, require careful balancing of all parts of a CMP that can limit throughput, such as the individual cores, on-chip cache memory, and off-chip memory interfaces. Several studies and example systems, such as the Sun Niagara, that examine the necessary tradeoffs are presented here. In contrast, latency-sensitive applications - many desktop applications fall into this category - require a focus on reducing inter-core communication latency and applying techniques to help programmers divide their programs into multiple threads as easily as possible. This book discusses many techniques that can be used in CMPs to simplify parallel programming, with an emphasis on research directions proposed at Stanford University. To illustrate the advantages possible with a CMP using a couple of solid examples, extra focus is given to thread-level speculation (TLS), a way to automatically break up nominally sequential applications into parallel threads on a CMP, and transactional memory. This model can greatly simplify manual parallel programming by using hardware - instead of conventional software locks - to enforce atomic code execution of blocks of instructions, a technique that makes parallel coding much less error-prone. Contents: The Case for CMPs / Improving Throughput / Improving Latency Automatically / Improving Latency using Manual Parallel Programming / A Multicore World: The Future of CMPs

In the last few years, power dissipation has become an important design constraint, on par with performance, in the design of new computer systems. Whereas in the past, the primary job of the computer architect was to translate improvements in operating frequency and transistor count into performance, now power efficiency must be taken into account at every step of the design process. While for some time, architects have been successful in delivering 40% to 50% annual improvement in processor performance, costs that were previously brushed aside eventually caught up. The most critical of these costs is the inexorable increase in power dissipation and power density in processors. Power dissipation issues have catalyzed new topic areas in computer architecture, resulting in a substantial body of work on more power-efficient architectures. Power dissipation coupled with diminishing performance gains, was also the main cause for the switch from single-core to multi-core architectures and a slowdown in frequency increase. This book aims to document some of the most important architectural techniques that were invented, proposed, and applied to reduce both dynamic power and static power dissipation in processors and memory hierarchies. A significant number of techniques have been proposed for a wide range of situations and this book synthesizes those techniques by focusing on their common characteristics. Table of Contents: Introduction / Modeling, Simulation, and Measurement / Using Voltage and Frequency Adjustments to Manage Dynamic Power / Optimizing Capacitance and Switching Activity to Reduce Dynamic Power / Managing Static (Leakage) Power / Conclusions

General-purpose graphics processing units (GPGPU) have emerged as an important class of shared memory parallel processing architectures, with widespread deployment in every computer class from high-end supercomputers to embedded mobile platforms. Relative to more traditional multicore systems of today, GPGPUs have distinctly higher degrees of hardware multithreading (hundreds of hardware thread contexts vs. tens), a return to wide vector units (several tens vs. 1-10), memory architectures that deliver higher peak memory bandwidth (hundreds of gigabytes per second vs. tens), and smaller caches/scratchpad memories (less than 1 megabyte vs. 1-10 megabytes). In this book, we provide a high-level overview of current GPGPU architectures and programming models. We review the principles that are used in previous shared memory parallel platforms, focusing on recent results in both the theory and practice of parallel algorithms, and suggest a connection to GPGPU platforms. We aim to provide hints to architects about understanding algorithm aspect to GPGPU. We also provide detailed performance analysis and guide optimizations from high-level algorithms to low-level instruction level optimizations. As a case study, we use n-body particle simulations known as the fast multipole method (FMM) as an example. We also briefly survey the state-of-the-art in GPU performance analysis tools and techniques. Table of Contents: GPU Design, Programming, and Trends / Performance Principles / From Principles to Practice: Analysis and Tuning / Using Detailed Performance Analysis to Guide Optimization

This lecture presents a study of the microarchitecture of contemporary microprocessors. The focus is on implementation aspects, with discussions on their implications in terms of performance, power, and cost of state-of-the-art designs. The lecture starts with an overview of the different types of microprocessors and a review of the microarchitecture of cache memories. Then, it describes the implementation of the fetch unit, where special emphasis is made on the required support for branch prediction. The next section is devoted to instruction decode with special focus on the particular support to decoding x86 instructions. The next chapter presents the allocation stage and pays special attention to the implementation of register renaming. Afterward, the issue stage is studied. Here, the logic to implement out-of-order issue for both memory and non-memory instructions is thoroughly described. The following chapter focuses on the instruction execution and describes the different functional units that can be found in contemporary microprocessors, as well as the implementation of the bypass network, which has an important impact on the performance. Finally, the lecture concludes with the commit stage, where it describes how the architectural state is updated and recovered in case of exceptions or misspeculations. This lecture is intended for an advanced course on computer architecture, suitable for graduate students or senior undergrads who want to specialize in the area of computer architecture. It is also intended for practitioners in the industry in the area of microprocessor design. The book assumes that the reader is familiar with the main concepts regarding pipelining, out-of-order execution, cache memories, and virtual memory. Table of Contents: Introduction / Caches / The Instruction Fetch Unit / Decode / Allocation / The Issue Stage / Execute / The Commit Stage / References / Author Biographies

As Moore's Law and Dennard scaling trends have slowed, the challenges of building high-performance computer architectures while maintaining acceptable power efficiency levels have heightened. Over the past ten years, architecture techniques for power efficiency have shifted from primarily focusing on module-level efficiencies, toward more holistic design styles based on parallelism and heterogeneity. This work highlights and synthesizes recent techniques and trends in power-efficient computer architecture. Table of Contents: Introduction / Voltage and Frequency Management / Heterogeneity and Specialization / Communication and Memory Systems / Conclusions / Bibliography / Authors' Biographies

To date, the most common form of simulators of computer systems are software-based running on standard computers. One promising approach to improve simulation performance is to apply hardware, specifically reconfigurable hardware in the form of field programmable gate arrays (FPGAs). This manuscript describes various approaches of using FPGAs to accelerate software-implemented simulation of computer systems and selected simulators that incorporate those techniques. More precisely, we describe a simulation architecture taxonomy that incorporates a simulation architecture specifically designed for FPGA accelerated simulation, survey the state-of-the-art in FPGA-accelerated simulation, and describe in detail selected instances of the described techniques. Table of Contents: Preface / Acknowledgments / Introduction / Simulator Background / Accelerating Computer System Simulators with FPGAs / Simulation Virtualization / Categorizing FPGA-based Simulators / Conclusion / Bibliography / Authors' Biographies

Shrinking feature size and diminishing supply voltage are making circuits sensitive to supply voltage fluctuations within the microprocessor, caused by normal workload activity changes. If left unattended, voltage fluctuations can lead to timing violations or even transistor lifetime issues that degrade processor robustness. Mechanisms that learn to tolerate, avoid, and eliminate voltage fluctuations based on program and microarchitectural events can help steer the processor clear of danger, thus enabling tighter voltage margins that improve performance or lower power consumption. We describe the problem of voltage variation and the factors that influence this variation during processor design and operation. We also describe a variety of runtime hardware and software mitigation techniques that either tolerate, avoid, and/or eliminate voltage violations. We hope processor architects will find the information useful since tolerance, avoidance, and elimination are generalizable constructs that can serve as a basis for addressing other reliability challenges as well. Table of Contents: Introduction / Modeling Voltage Variation / Understanding the Characteristics of Voltage Variation / Traditional Solutions and Emerging Solution Forecast / Allowing and Tolerating Voltage Emergencies / Predicting and Avoiding Voltage Emergencies / Eliminiating Recurring Voltage Emergencies / Future Directions on Resiliency

Today, computer-system optimization, at both the hardware and software levels, must consider the details of the memory system in its analysis; failing to do so yields systems that are increasingly inefficient as those systems become more complex. This lecture seeks to introduce the reader to the most important details of the memory system; it targets both computer scientists and computer engineers in industry and in academia. Roughly speaking, computer scientists are the users of the memory system and computer engineers are the designers of the memory system. Both can benefit tremendously from a basic understanding of how the memory system really works: the computer scientist will be better equipped to create algorithms that perform well and the computer engineer will be better equipped to design systems that approach the optimal, given the resource limitations. Currently, there is consensus among architecture researchers that the memory system is "the bottleneck," and this consensus has held for over a decade. Somewhat inexplicably, most of the research in the field is still directed toward improving the CPU to better tolerate a slow memory system, as opposed to addressing the weaknesses of the memory system directly. This lecture should get the bulk of the computer science and computer engineering population up the steep part of the learning curve. Not every CS/CE researcher/developer needs to do work in the memory system, but, just as a carpenter can do his job more efficiently if he knows a little of architecture, and an architect can do his job more efficiently if he knows a little of carpentry, giving the CS/CE worlds better intuition about the memory system should help them build better systems, both software and hardware. Table of Contents: Primers / It Must Be Modeled Accurately / ...\ and It Will Change Soon

Since the 1970¿s, microprocessor-based digital platforms have been riding Moore¿s law, allowing for doubling of density for the same area roughly every two years. However, whereas microprocessor fabrication has focused on increasing instruction execution rate, memory fabrication technologies have focused primarily on an increase in capacity with negligible increase in speed. This divergent trend in performance between the processors and memory has led to a phenomenon referred to as the ¿Memory Wall.¿ To overcome the memory wall, designers have resorted to a hierarchy of cache memory levels, which rely on the principal of memory access locality to reduce the observed memory access time and the performance gap between processors and memory. Unfortunately, important workload classes exhibit adverse memory access patterns that baffle the simple policies built into modern cache hierarchies to move instructions and data across cache levels. As such, processors often spend much time idling upon a demand fetch of memory blocks that miss in higher cache levels. Prefetching¿predicting future memory accesses and issuing requests for the corresponding memory blocks in advance of explicit accesses¿is an effective approach to hide memory access latency. There have been a myriad of proposed prefetching techniques, and nearly every modern processor includes some hardware prefetching mechanisms targeting simple and regular memory access patterns. This primer offers an overview of the various classes of hardware prefetchers for instructions and data proposed in the research literature, and presents examples of techniques incorporated into modern microprocessors.

This Synthesis Lecture focuses on techniques for efficient data orchestration within DNN accelerators. The End of Moore's Law, coupled with the increasing growth in deep learning and other AI applications has led to the emergence of custom Deep Neural Network (DNN) accelerators for energy-efficient inference on edge devices. Modern DNNs have millions of hyper parameters and involve billions of computations; this necessitates extensive data movement from memory to on-chip processing engines. It is well known that the cost of data movement today surpasses the cost of the actual computation; therefore, DNN accelerators require careful orchestration of data across on-chip compute, network, and memory elements to minimize the number of accesses to external DRAM. The book covers DNN dataflows, data reuse, buffer hierarchies, networks-on-chip, and automated design-space exploration. It concludes with data orchestration challenges with compressed and sparse DNNs and future trends. The target audience is students, engineers, and researchers interested in designing high-performance and low-energy accelerators for DNN inference.

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.

This book describes warehouse-scale computers (WSCs), the computing platforms that power cloud computing and all the great web services we use every day. It discusses how these new systems treat the datacenter itself as one massive computer designed at warehouse scale, with hardware and software working in concert to deliver good levels of internet service performance. The book details the architecture of WSCs and covers the main factors influencing their design, operation, and cost structure, and the characteristics of their software base. Each chapter contains multiple real-world examples, including detailed case studies and previously unpublished details of the infrastructure used to power Google's online services. Targeted at the architects and programmers of today's WSCs, this book provides a great foundation for those looking to innovate in this fascinating and important area, but the material will also be broadly interesting to those who just want to understandthe infrastructure powering the internet.The third edition reflects four years of advancements since the previous edition and nearly doubles the number of pictures and figures. New topics range from additional workloads like video streaming, machine learning, and public cloud to specialized silicon accelerators, storage and network building blocks, and a revised discussion of data center power and cooling, and uptime. Further discussions of emerging trends and opportunities ensure that this revised edition will remain an essential resource for educators and professionals working on the next generation of WSCs.

This book provides a thorough overview of the state-of-the-art field-programmable gate array (FPGA)-based robotic computing accelerator designs and summarizes their adopted optimized techniques. This book consists of ten chapters, delving into the details of how FPGAs have been utilized in robotic perception, localization, planning, and multi-robot collaboration tasks. In addition to individual robotic tasks, this book provides detailed descriptions of how FPGAs have been used in robotic products, including commercial autonomous vehicles and space exploration robots.

Many modern computer systems, including homogeneous and heterogeneous architectures, support shared memory in hardware. In a shared memory system, each of the processor cores may read and write to a single shared address space. For a shared memory machine, the memory consistency model defines the architecturally visible behavior of its memory system. Consistency definitions provide rules about loads and stores (or memory reads and writes) and how they act upon memory. As part of supporting a memory consistency model, many machines also provide cache coherence protocols that ensure that multiple cached copies of data are kept up-to-date. The goal of this primer is to provide readers with a basic understanding of consistency and coherence. This understanding includes both the issues that must be solved as well as a variety of solutions. We present both high-level concepts as well as specific, concrete examples from real-world systems.This second edition reflects a decade of advancements since the first edition and includes, among other more modest changes, two new chapters: one on consistency and coherence for non-CPU accelerators (with a focus on GPUs) and one that points to formal work and tools on consistency and coherence.

This book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep learning models for commercial applications. The exponential growth in computational power is slowing at a time when the amount of compute consumed by state-of-the-art deep learning (DL) workloads is rapidly growing. Model size, serving latency, and power constraints are a significant challenge in the deployment of DL models for many applications. Therefore, it is imperative to codesign algorithms, compilers, and hardware to accelerate advances in this field with holistic system-level and algorithm solutions that improve performance, power, and efficiency. Advancing DL systems generally involves three types of engineers: (1) data scientists that utilize and develop DL algorithms in partnership with domain experts, such as medical, economic, or climate scientists; (2) hardware designers that develop specialized hardware to accelerate the components in the DL models; and (3) performance and compiler engineers that optimize software to run more efficiently on a given hardware. Hardware engineers should be aware of the characteristics and components of production and academic models likely to be adopted by industry to guide design decisions impacting future hardware. Data scientists should be aware of deployment platform constraints when designing models. Performance engineers should support optimizations across diverse models, libraries, and hardware targets. The purpose of this book is to provide a solid understanding of (1) the design, training, and applications of DL algorithms in industry; (2) the compiler techniques to map deep learning code to hardware targets; and (3) the critical hardware features that accelerate DL systems. This book aims to facilitate co-innovation for the advancement of DL systems. It is written for engineers working in one or more of these areas who seek to understand the entire system stack in order to better collaborate with engineers working in other parts of the system stack. The book details advancements and adoption of DL models in industry, explains the training and deployment process, describes the essential hardware architectural features needed for today's and future models, and details advances in DL compilers to efficiently execute algorithms across various hardware targets. Unique in this book is the holistic exposition of the entire DL system stack, the emphasis on commercial applications, and the practical techniques to design models and accelerate their performance. The author is fortunate to work with hardware, software, data scientist, and research teams across many high-technology companies with hyperscale data centers. These companies employ many of the examples and methods provided throughout the book.