Download or read book In Memory Computing Hardware Accelerators for Data Intensive Applications written by Baker Mohammad and published by Springer Nature. This book was released on 2023-10-27 with total page 145 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the state-of-the-art of technology and research on In-Memory Computing Hardware Accelerators for Data-Intensive Applications. The authors discuss how processing-centric computing has become insufficient to meet target requirements and how Memory-centric computing may be better suited for the needs of current applications. This reveals for readers how current and emerging memory technologies are causing a shift in the computing paradigm. The authors do deep-dive discussions on volatile and non-volatile memory technologies, covering their basic memory cell structures, operations, different computational memory designs and the challenges associated with them. Specific case studies and potential applications are provided along with their current status and commercial availability in the market.

Download or read book Big Data Computing written by Rajendra Akerkar and published by CRC Press. This book was released on 2013-12-05 with total page 562 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to market forces and technological evolution, Big Data computing is developing at an increasing rate. A wide variety of novel approaches and tools have emerged to tackle the challenges of Big Data, creating both more opportunities and more challenges for students and professionals in the field of data computation and analysis. Presenting a mix

Download or read book Enabling Non Volatile Memory for Data intensive Applications written by Xiao Liu and published by . This book was released on 2021 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: The emerging Non-Volatile Memory (NVM) technologies are reforming the computer architecture. NVM holds advantages includes a byte-addressable interface, low latency, high capacity, and in-memory computing capability. However, data-intensive applications today demand compound features rather than just better performance. For instance, big data applications would require high availability and reliability. The neural network applications require scalability and power efficiency. Despite all the advantages of NVM, simply attaching the NVM to the memory hierarchy are unable to meet these demands. The decoupled reliability schemes among NVM and other devices fail to provide sufficient reliability. The vulnerability against overheating and hardware underutilization limit the performance and scalability of the in-memory computing NVM.Using the NVM for the data-intensive application requires redesign and customization. In this thesis, we focus on discussing the architecture designs that enable NVM for data-intensive applications. Our study includes two major types of data-intensive applications--big data applications and neural network applications. We first conduct a characteristic study against the persistent memory applications. Persistent memory implements over the NVM-based main memory and guarantees crash consistency. We explore the performance interaction across applications, persistent memory system software, and hardware components. Based on our characterization results, we provide a set of implications and recommendations for optimizing persistent memory designs. Second, we propose Binary Star for the generic data-intensive applications, which coordinates the reliability schemes and consistent cache writeback between 3D-stacked DRAM last-level cache and NVM main memory to maintain the reliability of the memory hierarchy. Binary Star significantly reduces the performance and storage overhead of consistent cache writeback by coordinating it with NVM wear leveling. For neural network applications, our first design explores the thermal effect over one representative NVM--resistive memory (RRAM). We find heat-induced interference decreases the computational accuracy in the RRAM-based neural network accelerator. We propose HR3AM, a heat resilience design, which improves accuracy and optimizes the thermal distribution. Results show that HR3AM improves classification accuracy and decreases both the maximum and average chip temperatures. Lastly, we present Mirage to improve parallelism and flexibility for pipeline-enabled RRAM-based accelerators. Mirage is a hardware/software co-design that addresses the data dependencies and inflexibility issues of existing accelerators. Our evaluation shows that Mirage achieves low inference latency and high throughput compared to state-of-the-art RRAM-based accelerators.

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 Computing with Memory for Energy Efficient Robust Systems written by Somnath Paul and published by Springer Science & Business Media. This book was released on 2013-09-07 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book analyzes energy and reliability as major challenges faced by designers of computing frameworks in the nanometer technology regime. The authors describe the existing solutions to address these challenges and then reveal a new reconfigurable computing platform, which leverages high-density nanoscale memory for both data storage and computation to maximize the energy-efficiency and reliability. The energy and reliability benefits of this new paradigm are illustrated and the design challenges are discussed. Various hardware and software aspects of this exciting computing paradigm are described, particularly with respect to hardware-software co-designed frameworks, where the hardware unit can be reconfigured to mimic diverse application behavior. Finally, the energy-efficiency of the paradigm described is compared with other, well-known reconfigurable computing platforms.

Download or read book ReRAM based Machine Learning written by Hao Yu and published by IET. This book was released on 2021-03-05 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: Serving as a bridge between researchers in the computing domain and computing hardware designers, this book presents ReRAM techniques for distributed computing using IMC accelerators, ReRAM-based IMC architectures for machine learning (ML) and data-intensive applications, and strategies to map ML designs onto hardware accelerators.

Download or read book Computing Big data Applications Near Flash written by Shuotao Xu and published by . This book was released on 2021 with total page 183 pages. Available in PDF, EPUB and Kindle. Book excerpt: Current systems produce a large and growing amount of data, which is often referred to as Big Data. Providing valuable insights from this data requires new computing systems to store and process it efficiently. For a fast response time, Big Data typically relies on in-memory computing, which requires a cluster of machines with enough aggregate DRAM to accommodate the entire datasets for the duration of the computation. Big Data typically exceeds several terabytes, therefore this approach can incur significant overhead in power, space and equipment. If the amount of DRAM is not sufficient to hold the working-set of a query, the performance deteriorates catastrophically. Although NAND flash can provide high-bandwidth data access and has higher capacity density and lower cost per bit than DRAM, flash storage has dramatically different characteristics than DRAM, such as large access granularity and longer access latency. Therefore, there are many challenges for Big-Data applications to enable flash-centric computing and achieve performance comparable to that of in-memory computing. This thesis presents flash-centric hardware architectures that provide high processing throughput for data-intensive applications while hiding long flash access latency. Specifically we describe two novel flash-centric hardware accelerators, BlueCache and AQUOMAN. These systems lower the cost of two common data-center workloads, key-value cache and SQL analytics. We have built BlueCache and AQUOMAN using FPGAs and flash storage, and show that they can provide competitive performance of computing Big-Data applications with multi-terabyte datasets. BlueCache provides a 10-100X cheaper key-value cache than DRAM-based solution, and can outperform DRAM-based system when the latter has more than 7.4% misses for a read-intensive workloads. A desktop-class machine with single instance of 1TB AQUOMAN disk can achieve performance similar to that of a dual-socket general-purpose server with off-the-shelf SSDs. We believe BlueCache and AQUOMAN can bring down the cost of acquiring and operating high-performance computing systems for data-center-scale Big-Data applications dramatically.

Download or read book FPGA BASED Hardware Accelerators written by Iouliia Skliarova and published by Springer. This book was released on 2019-05-30 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book suggests and describes a number of fast parallel circuits for data/vector processing using FPGA-based hardware accelerators. Three primary areas are covered: searching, sorting, and counting in combinational and iterative networks. These include the application of traditional structures that rely on comparators/swappers as well as alternative networks with a variety of core elements such as adders, logical gates, and look-up tables. The iterative technique discussed in the book enables the sequential reuse of relatively large combinational blocks that execute many parallel operations with small propagation delays. For each type of network discussed, the main focus is on the step-by-step development of the architectures proposed from initial concepts to synthesizable hardware description language specifications. Each type of network is taken through several stages, including modeling the desired functionality in software, the retrieval and automatic conversion of key functions, leading to specifications for optimized hardware modules. The resulting specifications are then synthesized, implemented, and tested in FPGAs using commercial design environments and prototyping boards. The methods proposed can be used in a range of data processing applications, including traditional sorting, the extraction of maximum and minimum subsets from large data sets, communication-time data processing, finding frequently occurring items in a set, and Hamming weight/distance counters/comparators. The book is intended to be a valuable support material for university and industrial engineering courses that involve FPGA-based circuit and system design.

Download or read book Hardware Accelerators in Data Centers written by Christoforos Kachris and published by Springer. This book was released on 2018-08-21 with total page 279 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides readers with an overview of the architectures, programming frameworks, and hardware accelerators for typical cloud computing applications in data centers. The authors present the most recent and promising solutions, using hardware accelerators to provide high throughput, reduced latency and higher energy efficiency compared to current servers based on commodity processors. Readers will benefit from state-of-the-art information regarding application requirements in contemporary data centers, computational complexity of typical tasks in cloud computing, and a programming framework for the efficient utilization of the hardware accelerators.

Download or read book Your Genes Your Choices written by Catherine Baker and published by . This book was released on 1996 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: Program discusses the Human Genome Project, the science behind it, and the ethical, legal and social issues raised by the project.

Download or read book Hardware and Computer Organization written by Arnold S. Berger and published by Elsevier. This book was released on 2005-06-08 with total page 513 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hardware and Computer Organization is a practical introduction to the architecture of modern microprocessors. This book from the bestselling author explains how PCs work and how to make them work for you. It is designed to take students "under the hood" of a PC and provide them with an understanding of the complex machine that has become such a pervasive part of everyday life. It clearly explains how hardware and software cooperatively interact to accomplish real-world tasks. Unlike other textbooks on this topic, Dr. Berger’s book takes the software developer’s point-of-view. Instead of simply demonstrating how to design a computer’s hardware, it provides an understanding of the total machine, highlighting strengths and weaknesses, explaining how to deal with memory and how to write efficient assembly code that interacts directly with, and takes best advantage of the underlying hardware. The book is divided into three major sections: Part 1 covers hardware and computer fundamentals, including logical gates and simple digital design. Elements of hardware development such as instruction set architecture, memory and I/O organization and analog to digital conversion are examined in detail, within the context of modern operating systems. Part 2 discusses the software at the lowest level ̧ assembly language, while Part 3 introduces the reader to modern computer architectures and reflects on future trends in reconfigurable hardware. This book is an ideal reference for ECE/software engineering students as well as embedded systems designers, professional engineers needing to understand the fundamentals of computer hardware, and hobbyists. The renowned author's many years in industry provide an excellent basis for the inclusion of extensive real-world references and insights Several modern processor architectures are covered, with examples taken from each, including Intel, Motorola, MIPS, and ARM

Download or read book Trusted Computing Platforms written by Sean W. Smith and published by Springer Science & Business Media. This book was released on 2006-06-16 with total page 246 pages. Available in PDF, EPUB and Kindle. Book excerpt: From early prototypes and proposed applications, this book surveys the longer history of amplifying small amounts of hardware security into broader system security Including real case study experience with security architecture and applications on multiple types of platforms. Examines the theory, design, implementation of the IBM 4758 secure coprocessor platform and discusses real case study applications that exploit the unique capabilities of this platform. Examines more recent cutting-edge experimental work in this area. Written for security architects, application designers, and the general computer scientist interested in the evolution and use of this emerging technology.

Download or read book Green Computing with Emerging Memory written by Takayuki Kawahara and published by Springer Science & Business Media. This book was released on 2012-09-26 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes computing innovation, using non-volatile memory for a sustainable world. It appeals to both computing engineers and device engineers by describing a new means of lower power computing innovation, without sacrificing performance over conventional low-voltage operation. Readers will be introduced to methods of design and implementation for non-volatile memory which allow computing equipment to be turned off normally when not in use and to be turned on instantly to operate with full performance when needed.

Download or read book Reconfigurable Computing written by Joao Cardoso and published by Springer Science & Business Media. This book was released on 2011-08-17 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: As the complexity of modern embedded systems increases, it becomes less practical to design monolithic processing platforms. As a result, reconfigurable computing is being adopted widely for more flexible design. Reconfigurable Computers offer the spatial parallelism and fine-grained customizability of application-specific circuits with the postfabrication programmability of software. To make the most of this unique combination of performance and flexibility, designers need to be aware of both hardware and software issues. FPGA users must think not only about the gates needed to perform a computation but also about the software flow that supports the design process. The goal of this book is to help designers become comfortable with these issues, and thus be able to exploit the vast opportunities possible with reconfigurable logic.

Download or read book High Performance Computing Using FPGAs written by Wim Vanderbauwhede and published by Springer Science & Business Media. This book was released on 2013-08-23 with total page 798 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-Performance Computing using FPGA covers the area of high performance reconfigurable computing (HPRC). This book provides an overview of architectures, tools and applications for High-Performance Reconfigurable Computing (HPRC). FPGAs offer very high I/O bandwidth and fine-grained, custom and flexible parallelism and with the ever-increasing computational needs coupled with the frequency/power wall, the increasing maturity and capabilities of FPGAs, and the advent of multicore processors which has caused the acceptance of parallel computational models. The Part on architectures will introduce different FPGA-based HPC platforms: attached co-processor HPRC architectures such as the CHREC’s Novo-G and EPCC’s Maxwell systems; tightly coupled HRPC architectures, e.g. the Convey hybrid-core computer; reconfigurably networked HPRC architectures, e.g. the QPACE system, and standalone HPRC architectures such as EPFL’s CONFETTI system. The Part on Tools will focus on high-level programming approaches for HPRC, with chapters on C-to-Gate tools (such as Impulse-C, AutoESL, Handel-C, MORA-C++); Graphical tools (MATLAB-Simulink, NI LabVIEW); Domain-specific languages, languages for heterogeneous computing(for example OpenCL, Microsoft’s Kiwi and Alchemy projects). The part on Applications will present case from several application domains where HPRC has been used successfully, such as Bioinformatics and Computational Biology; Financial Computing; Stencil computations; Information retrieval; Lattice QCD; Astrophysics simulations; Weather and climate modeling.

Download or read book Data Intensive Computing written by Ian Gorton and published by Cambridge University Press. This book was released on 2013 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: Describes principles of the emerging field of data-intensive computing, along with methods for designing, managing and analyzing the big data sets of today.

Download or read book High Performance Computing for Big Data written by Chao Wang and published by CRC Press. This book was released on 2017-10-16 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: High-Performance Computing for Big Data: Methodologies and Applications explores emerging high-performance architectures for data-intensive applications, novel efficient analytical strategies to boost data processing, and cutting-edge applications in diverse fields, such as machine learning, life science, neural networks, and neuromorphic engineering. The book is organized into two main sections. The first section covers Big Data architectures, including cloud computing systems, and heterogeneous accelerators. It also covers emerging 3D IC design principles for memory architectures and devices. The second section of the book illustrates emerging and practical applications of Big Data across several domains, including bioinformatics, deep learning, and neuromorphic engineering. Features Covers a wide range of Big Data architectures, including distributed systems like Hadoop/Spark Includes accelerator-based approaches for big data applications such as GPU-based acceleration techniques, and hardware acceleration such as FPGA/CGRA/ASICs Presents emerging memory architectures and devices such as NVM, STT- RAM, 3D IC design principles Describes advanced algorithms for different big data application domains Illustrates novel analytics techniques for Big Data applications, scheduling, mapping, and partitioning methodologies Featuring contributions from leading experts, this book presents state-of-the-art research on the methodologies and applications of high-performance computing for big data applications. About the Editor Dr. Chao Wang is an Associate Professor in the School of Computer Science at the University of Science and Technology of China. He is the Associate Editor of ACM Transactions on Design Automations for Electronics Systems (TODAES), Applied Soft Computing, Microprocessors and Microsystems, IET Computers & Digital Techniques, and International Journal of Electronics. Dr. Chao Wang was the recipient of Youth Innovation Promotion Association, CAS, ACM China Rising Star Honorable Mention (2016), and best IP nomination of DATE 2015. He is now on the CCF Technical Committee on Computer Architecture, CCF Task Force on Formal Methods. He is a Senior Member of IEEE, Senior Member of CCF, and a Senior Member of ACM.