Download or read book Computing in Accelerator Design and Operation written by and published by . This book was released on 1983 with total page 130 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Computing in Accelerator Design and Operation written by W. Busse and published by Springer. This book was released on 1984-12 with total page 596 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Computing in Accelerator Design and Operation written by Roman Zelazny and published by . This book was released on 1983 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Research Infrastructures for Hardware Accelerators written by Yakun Sophia Shao and published by Springer Nature. This book was released on 2022-05-31 with total page 85 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hardware acceleration in the form of customized datapath and control circuitry tuned to specific applications has gained popularity for its promise to utilize transistors more efficiently. Historically, the computer architecture community has focused on general-purpose processors, and extensive research infrastructure has been developed to support research efforts in this domain. Envisioning future computing systems with a diverse set of general-purpose cores and accelerators, computer architects must add accelerator-related research infrastructures to their toolboxes to explore future heterogeneous systems. This book serves as a primer for the field, as an overview of the vast literature on accelerator architectures and their design flows, and as a resource guidebook for researchers working in related areas.

Download or read book Computing in Accelerator Design and Operation Freie Universit t Berlin September 20 23 1983 written by and published by . This book was released on 1983 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Particle Accelerator Design Computer Programs written by John Colonias and published by Elsevier. This book was released on 2012-12-02 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: Particle Accelerator Design: Computer Programs describes some of the most important computer programs applicable to the design of particle accelerators. Computer programs that calculate magnetic and electric fields are considered, along with programs that calculate orbits of particles in a magnetic and/or electric field. Some representative programs useful in the design of linear accelerator-type cavities are also discussed. This book is comprised of six chapters and begins with a review of two-dimensional magnetostatic programs, including TRIM, LINDA, NUTCRACKER, MAREC, GRACY, and COILS. The University of Colorado's magnet program is also examined. The next chapter is devoted to programs capable of solving problems relating to the calculation of electrostatic fields in two-dimensional geometries. The reader is also introduced to programs that perform calculations of three-dimensional linear and nonlinear problems, along with programs that employ matrix formalism and integration of equations of motion. The final chapter looks at programs for linear accelerator-type cavities, including CURE, JESSY, MESSYMESH, and AZTEC. This monograph will be a useful resource for physical scientists, engineers, and computer programmers.

Download or read book Hardware Accelerator Systems for Artificial Intelligence and Machine Learning written by and published by Academic Press. This book was released on 2021-03-28 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into arti?cial Intelligence and the growth it has seen with the advent of Deep Neural Networks (DNNs) and Machine Learning. Updates in this release include chapters on Hardware accelerator systems for artificial intelligence and machine learning, Introduction to Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Deep Learning with GPUs, Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures, Architecture of NPU for DNN, Hardware Architecture for Convolutional Neural Network for Image Processing, FPGA based Neural Network Accelerators, and much more. - Updates on new information on the architecture of GPU, NPU and DNN - Discusses In-memory computing, Machine intelligence and Quantum computing - Includes sections on Hardware Accelerator Systems to improve processing efficiency and performance

Download or read book Use of the IBM PC Computer in Accelerator Design Calculations written by and published by . This book was released on 1986 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: For many reasons it is desirable to develop a ''stand-alone'' means of carrying out preliminary calculations in the various accelerator design fields. Work is in progress to allow the use of modified or subset versions of several well-known and widely used codes on the IBM-PC. Codes in preliminary operation include TRANSPORT, for beam-line design; the SUPERFISH code series, for cavity design; and RAYTRACE, for magnets. These codes are surpassing expectations in capabilities, speed of execution, and accuracy.

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 280 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 Computer Control of Large Accelerators Design Concepts and Methods written by and published by . This book was released on 1984 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlike most of the specialities treated in this volume, control system design is still an art, not a science. These lectures are an attempt to produce a primer for prospective practitioners of this art. A large modern accelerator requires a comprehensive control system for commissioning, machine studies and day-to-day operation. Faced with the requirement to design a control system for such a machine, the control system architect has a bewildering array of technical devices and techniques at his disposal, and it is our aim in the following chapters to lead him through the characteristics of the problems he will have to face and the practical alternatives available for solving them. We emphasize good system architecture using commercially available hardware and software components, but in addition we discuss the actual control strategies which are to be implemented since it is at the point of deciding what facilities shall be available that the complexity of the control system and its cost are implicitly decided. 19 references.

Download or read book Design of FPGA Based Computing Systems with OpenCL written by Hasitha Muthumala Waidyasooriya and published by Springer. This book was released on 2017-10-24 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides wide knowledge about designing FPGA-based heterogeneous computing systems, using a high-level design environment based on OpenCL (Open Computing language), which is called OpenCL for FPGA. The OpenCL-based design methodology will be the key technology to exploit the potential of FPGAs in various applications such as low-power embedded applications and high-performance computing. By understanding the OpenCL-based design methodology, readers can design an entire FPGA-based computing system more easily compared to the conventional HDL-based design, because OpenCL for FPGA takes care of computation on a host, data transfer between a host and an FPGA, computation on an FPGA with a capable of accessing external DDR memories. In the step-by-step way, readers can understand followings: how to set up the design environment how to write better codes systematically considering architectural constraints how to design practical applications

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 257 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 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.

Download or read book Improving Emerging Systems Efficiency with Hardware Accelerators written by Henrique Fingler and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The constant growth of datacenters and cloud computing comes with an increase of power consumption. With the end of Dennard scaling and Moore's law, computing no longer grows at the same ratio as transistor count and density grows. This thesis explores ideas to increase computing efficiency, which is defined as the ratio of processing power per energy spent. Hardware acceleration is an established technique to improve computing efficiency by specializing hardware to a subset of operations or application domains. While accelerators have fueled the success of some application domains such as machine learning, accelerator programming interfaces and runtimes have significant limitations that collectively form barriers to adoption in many settings. There are great opportunities for extending hardware acceleration interfaces to more application domains and other platforms. First, this thesis presents DGSF, a framework that enables serverless platforms to access disaggregated accelerators (GPUs). DGSF uses virtualization techniques to provide serverless platforms with GPUs, with the abstraction of a local GPU that can be backed by a local or a remote physical GPU. Through optimizations specific to serverless platforms, applications that use a GPU can have a lower end-to-end execution time than if they were run natively, using a local physical GPU. DGSF extends hardware acceleration accessibility to an existing serverless platforms which currently does not support accelerators, showing the flexibility and ease of deployment of the DGSF framework. Next, this thesis presents LAKE, a framework that introduces accelerator and machine learning support to operating system kernels. I believe there is great potential to replace operating system resource management heuristics with machine learning, for example, I/O and process scheduling. Accelerators are vital to support efficient, low latency inference for kernels that makes frequent use of ML techniques. Unfortunately, operating systems can not access hardware acceleration. LAKE uses GPU virtualization techniques to efficiently enable accelerator accessibility in operating systems. However, allowing operating systems to use hardware acceleration introduces problems unique to this scenario. User and kernel applications can contend for resources such as CPU or accelerators. Unmanaged resource contention can harm the performance of applications. Machine learning-based kernel subsystems can produce unsatisfactory results. There need to be guardrails, mechanisms that prevent machine learning models to output solutions with quality below a threshold, to avoid poor decisions and performance pathologies. LAKE proposes customizable, developer written policies that can control contention, modulate execution and provide guardrails to machine learning. Finally, this thesis proposes LFR, a feature registry that augments LAKE to provide a shared feature and model registry framework to support future ML-in-the-kernel applications, removing the need of ad hoc designs. The learnings from LAKE showed that machine learning in operating systems can increase computing efficiency and revealed the absence of a shared framework. Such framework is a required component in future research and production of machine learning driven operating systems. LFR introduces an in-kernel feature registry that provides machine learning-based kernel subsystems with a common API to store, capture and manage models and feature vectors, and facilitates the insertion of inference hooks into the kernel. This thesis studies the application of LFR, and evaluates the performance critical parts, such as capturing and storing features

Download or read book High level Synthesis for Efficient Accelerator Design written by and published by . This book was released on 2015 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt: For several decades, the research of general-purpose microprocessors has been central to the organization of efficient computing systems. However, computer architects face the limitations of innovation and research alternative techniques to keep improving computing systems. Today, a custom hardware implementation and specialized accelerators are becoming a widely-used approach to overcome these limitations. While hardware accelerators are useful for efficient computing, its entry barrier due to design complexity is higher than software implementation for a general-purpose microprocessor. High-Level Synthesis (HLS) is a well- known custom hardware design base on the software approach to relaxed design complexity. In this work, we propose new HLS approaches to generate hardware accelerators, depending on the characteristics of the implemented software: SoftWare Synthesis for network Loop (SWSL) and cache-coherent High-Level Synthesis (ccHLS). Depending on the properties of the target software, the implementation method should be different and it requires different HLS techniques to meet the requirement of these goals. Data structure lookups are among the most expensive operations on a router and its function is to achieve high throughput with low latency and power to support massive input packet processing. Hence, accelerators from HLS for network lookup should consider ways to minimize latency and maximize throughput. SWSL accepts a specialized programming model for a specialized architecture called PLUG and retains its simple programming model. SWSL generates entire lookup chains performing aggressive pipelining to achieve high throughput. On the other hand, conventional programs normally focus on loops as accelerating regions which requires more computation performance than non-accelerating regions. However, this approach requires the host processor to execute non-accelerating code region and the accelerator's memory as cache because the host processor and accelerators are running separately. Thus, the execution model of most accelerators is 1) read data from the accelerator's memory to inject into the accelerator hardware and 2) write data to the accelerator's memory to update the computation result. Its mechanism leads to some cache coherency between the cache in a host processor and the accelerator's memory. In addition, it is not cost-effective because it requires additional memory for accelerators. ccHLS generates fixed-function accelerators sharing data cache with the host processor starting with C/C++ source code. It is basically organized as two accelerators following a decoupled access/execute model, which are the fixed-function compute accelerator and the fixed-function memory access accelerator. The former has a task to speed up computation of a target source code and the latter has the role of data movement including cache accesses in the host processor directly. The fixed-function memory access accelerator is organized by an address generation unit for address computation and FSM to control memory access. The host processor, except the data cache, is turned off during acceleration. In this dissertation, we observed following: First, SWSL gives 2 ~4× lower latency and 3 ~4× reduced chip area with reasonable power consumption compared with a previously proposed solution. Second, ccHLS provides reasonable factors by using fixed-function accelerators from software implementation of applications. We expect cost-effective and performance/energy improvement design for acceleration. Based on our simulation result, compared to 2-wide and 4-wide out-of-order processors, we observe 2.55× and 1.57× speedup and 3.96× and 7.20× energy reduction.

Download or read book Energy Research Abstracts written by and published by . This book was released on 1992-12 with total page 474 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Accelerators for Convolutional Neural Networks written by Arslan Munir and published by John Wiley & Sons. This book was released on 2023-10-31 with total page 308 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accelerators for Convolutional Neural Networks Comprehensive and thorough resource exploring different types of convolutional neural networks and complementary accelerators Accelerators for Convolutional Neural Networks provides basic deep learning knowledge and instructive content to build up convolutional neural network (CNN) accelerators for the Internet of things (IoT) and edge computing practitioners, elucidating compressive coding for CNNs, presenting a two-step lossless input feature maps compression method, discussing arithmetic coding -based lossless weights compression method and the design of an associated decoding method, describing contemporary sparse CNNs that consider sparsity in both weights and activation maps, and discussing hardware/software co-design and co-scheduling techniques that can lead to better optimization and utilization of the available hardware resources for CNN acceleration. The first part of the book provides an overview of CNNs along with the composition and parameters of different contemporary CNN models. Later chapters focus on compressive coding for CNNs and the design of dense CNN accelerators. The book also provides directions for future research and development for CNN accelerators. Other sample topics covered in Accelerators for Convolutional Neural Networks include: How to apply arithmetic coding and decoding with range scaling for lossless weight compression for 5-bit CNN weights to deploy CNNs in extremely resource-constrained systems State-of-the-art research surrounding dense CNN accelerators, which are mostly based on systolic arrays or parallel multiply-accumulate (MAC) arrays iMAC dense CNN accelerator, which combines image-to-column (im2col) and general matrix multiplication (GEMM) hardware acceleration Multi-threaded, low-cost, log-based processing element (PE) core, instances of which are stacked in a spatial grid to engender NeuroMAX dense accelerator Sparse-PE, a multi-threaded and flexible CNN PE core that exploits sparsity in both weights and activation maps, instances of which can be stacked in a spatial grid for engendering sparse CNN accelerators For researchers in AI, computer vision, computer architecture, and embedded systems, along with graduate and senior undergraduate students in related programs of study, Accelerators for Convolutional Neural Networks is an essential resource to understanding the many facets of the subject and relevant applications.