Download or read book A Primer on Compression in the Memory Hierarchy written by Somayeh Sardashti and published by Springer Nature. This book was released on 2022-05-31 with total page 70 pages. Available in PDF, EPUB and Kindle. Book excerpt: This synthesis lecture presents the current state-of-the-art in applying low-latency, lossless hardware compression algorithms to cache, memory, and the memory/cache link. There are many non-trivial challenges that must be addressed to make data compression work well in this context. First, since compressed data must be decompressed before it can be accessed, decompression latency ends up on the critical memory access path. This imposes a significant constraint on the choice of compression algorithms. Second, while conventional memory systems store fixed-size entities like data types, cache blocks, and memory pages, these entities will suddenly vary in size in a memory system that employs compression. Dealing with variable size entities in a memory system using compression has a significant impact on the way caches are organized and how to manage the resources in main memory. We systematically discuss solutions in the open literature to these problems. Chapter 2 provides the foundations of data compression by first introducing the fundamental concept of value locality. We then introduce a taxonomy of compression algorithms and show how previously proposed algorithms fit within that logical framework. Chapter 3 discusses the different ways that cache memory systems can employ compression, focusing on the trade-offs between latency, capacity, and complexity of alternative ways to compact compressed cache blocks. Chapter 4 discusses issues in applying data compression to main memory and Chapter 5 covers techniques for compressing data on the cache-to-memory links. This book should help a skilled memory system designer understand the fundamental challenges in applying compression to the memory hierarchy and introduce him/her to the state-of-the-art techniques in addressing them.

Download or read book A Primer on Memory Persistency written by Gogte Vaibhav and published by Springer Nature. This book was released on 2022-06-01 with total page 95 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 and program 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.

Download or read book A Primer on Memory Consistency and Cache Coherence Second Edition written by Vijay Nagarajan and published by Springer Nature. This book was released on 2022-05-31 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Embedded Computer Systems Architectures Modeling and Simulation written by Alex Orailoglu and published by Springer Nature. This book was released on 2022-04-26 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 21st International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation, SAMOS 2021, which took place in July 2021. Due to COVID-19 pandemic the conference was held virtually. The 17 full papers presented in this volume were carefully reviewed and selected from 45 submissions. The papers are organized in topics as follows: simulation and design space exploration; the 3Cs - Cache, Cluster and Cloud; heterogeneous SoC; novel CPU architectures and applications; dataflow; innovative architectures and tools for security; next generation computing; insights from negative results.

Download or read book Architectural and Operating System Support for Virtual Memory written by Abhishek Bhattacharjee and published by Springer Nature. This book was released on 2022-05-31 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides computer engineers, academic researchers, new graduate students, and seasoned practitioners an end-to-end overview of virtual memory. We begin with a recap of foundational concepts and discuss not only state-of-the-art virtual memory hardware and software support available today, but also emerging research trends in this space. The span of topics covers processor microarchitecture, memory systems, operating system design, and memory allocation. We show how efficient virtual memory implementations hinge on careful hardware and software cooperation, and we discuss new research directions aimed at addressing emerging problems in this space. Virtual memory is a classic computer science abstraction and one of the pillars of the computing revolution. It has long enabled hardware flexibility, software portability, and overall better security, to name just a few of its powerful benefits. Nearly all user-level programs today take for granted that they will have been freed from the burden of physical memory management by the hardware, the operating system, device drivers, and system libraries. However, despite its ubiquity in systems ranging from warehouse-scale datacenters to embedded Internet of Things (IoT) devices, the overheads of virtual memory are becoming a critical performance bottleneck today. Virtual memory architectures designed for individual CPUs or even individual cores are in many cases struggling to scale up and scale out to today's systems which now increasingly include exotic hardware accelerators (such as GPUs, FPGAs, or DSPs) and emerging memory technologies (such as non-volatile memory), and which run increasingly intensive workloads (such as virtualized and/or "big data" applications). As such, many of the fundamental abstractions and implementation approaches for virtual memory are being augmented, extended, or entirely rebuilt in order to ensure that virtual memory remains viable and performant in the years to come.

Download or read book Innovations in the Memory System written by Rajeev Balasubramonian and published by Springer Nature. This book was released on 2022-05-31 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book In Near Memory Computing written by Daichi Fujiki and published by Springer Nature. This book was released on 2022-05-31 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Robotic Computing on FPGAs written by Shaoshan Liu and published by Springer Nature. This book was released on 2022-05-31 with total page 202 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book AI for Computer Architecture written by Lizhong Chen and published by Springer Nature. This book was released on 2022-05-31 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book General Purpose Graphics Processor Architectures written by Tor M. Aamodt and published by Springer Nature. This book was released on 2022-05-31 with total page 122 pages. Available in PDF, EPUB and Kindle. Book excerpt: Originally developed to support video games, graphics processor units (GPUs) are now increasingly used for general-purpose (non-graphics) applications ranging from machine learning to mining of cryptographic currencies. GPUs can achieve improved performance and efficiency versus central processing units (CPUs) by dedicating a larger fraction of hardware resources to computation. In addition, their general-purpose programmability makes contemporary GPUs appealing to software developers in comparison to domain-specific accelerators. This book provides an introduction to those interested in studying the architecture of GPUs that support general-purpose computing. It collects together information currently only found among a wide range of disparate sources. The authors led development of the GPGPU-Sim simulator widely used in academic research on GPU architectures. The first chapter of this book describes the basic hardware structure of GPUs and provides a brief overview of their history. Chapter 2 provides a summary of GPU programming models relevant to the rest of the book. Chapter 3 explores the architecture of GPU compute cores. Chapter 4 explores the architecture of the GPU memory system. After describing the architecture of existing systems, Chapters 3 and 4 provide an overview of related research. Chapter 5 summarizes cross-cutting research impacting both the compute core and memory system. This book should provide a valuable resource for those wishing to understand the architecture of graphics processor units (GPUs) used for acceleration of general-purpose applications and to those who want to obtain an introduction to the rapidly growing body of research exploring how to improve the architecture of these GPUs.

Download or read book Compiling Algorithms for Heterogeneous Systems written by Steven Bell and published by Springer Nature. This book was released on 2022-05-31 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: Most emerging applications in imaging and machine learning must perform immense amounts of computation while holding to strict limits on energy and power. To meet these goals, architects are building increasingly specialized compute engines tailored for these specific tasks. The resulting computer systems are heterogeneous, containing multiple processing cores with wildly different execution models. Unfortunately, the cost of producing this specialized hardware—and the software to control it—is astronomical. Moreover, the task of porting algorithms to these heterogeneous machines typically requires that the algorithm be partitioned across the machine and rewritten for each specific architecture, which is time consuming and prone to error. Over the last several years, the authors have approached this problem using domain-specific languages (DSLs): high-level programming languages customized for specific domains, such as database manipulation, machine learning, or image processing. By giving up generality, these languages are able to provide high-level abstractions to the developer while producing high-performance output. The purpose of this book is to spur the adoption and the creation of domain-specific languages, especially for the task of creating hardware designs. In the first chapter, a short historical journey explains the forces driving computer architecture today. Chapter 2 describes the various methods for producing designs for accelerators, outlining the push for more abstraction and the tools that enable designers to work at a higher conceptual level. From there, Chapter 3 provides a brief introduction to image processing algorithms and hardware design patterns for implementing them. Chapters 4 and 5 describe and compare Darkroom and Halide, two domain-specific languages created for image processing that produce high-performance designs for both FPGAs and CPUs from the same source code, enabling rapid design cycles and quick porting of algorithms. The final section describes how the DSL approach also simplifies the problem of interfacing between application code and the accelerator by generating the driver stack in addition to the accelerator configuration. This book should serve as a useful introduction to domain-specialized computing for computer architecture students and as a primer on domain-specific languages and image processing hardware for those with more experience in the field.

Download or read book Parallel Processing 1980 to 2020 written by Robert Kuhn and published by Springer Nature. This book was released on 2022-05-31 with total page 166 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Principles of Secure Processor Architecture Design written by Jakub Szefer and published by Springer Nature. This book was released on 2022-06-01 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Deep Learning Systems written by Andres Rodriguez and published by Springer Nature. This book was released on 2022-05-31 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Efficient Processing of Deep Neural Networks written by Vivienne Sze and published by Springer Nature. This book was released on 2022-05-31 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Quantum Computer Systems written by Yongshan Ding and published by Springer Nature. This book was released on 2022-05-31 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Data Orchestration in Deep Learning Accelerators written by Tushar Krishna and published by Springer Nature. This book was released on 2022-05-31 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.