In Memory Computing Hardware Accelerators For Data Intensive Applications
Download In Memory Computing Hardware Accelerators For Data Intensive Applications full books in PDF, epub, and Kindle. Read online free In Memory Computing Hardware Accelerators For Data Intensive Applications ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!
| Author | : Baker Mohammad |
| Publisher | : Springer Nature |
| Total Pages | : 145 |
| Release | : 2023-10-27 |
| Genre | : Technology & Engineering |
| ISBN | : 303134233X |
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.
| Author | : Takayuki Kawahara |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 214 |
| Release | : 2012-09-26 |
| Genre | : Technology & Engineering |
| ISBN | : 1461408121 |
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.
| Author | : Ian Gorton |
| Publisher | : Cambridge University Press |
| Total Pages | : 299 |
| Release | : 2013 |
| Genre | : Computers |
| ISBN | : 0521191955 |
Describes principles of the emerging field of data-intensive computing, along with methods for designing, managing and analyzing the big data sets of today.
| Author | : Ashutosh Mishra |
| Publisher | : Springer Nature |
| Total Pages | : 358 |
| Release | : 2023-03-15 |
| Genre | : Technology & Engineering |
| ISBN | : 3031221702 |
This book explores new methods, architectures, tools, and algorithms for Artificial Intelligence Hardware Accelerators. The authors have structured the material to simplify readers’ journey toward understanding the aspects of designing hardware accelerators, complex AI algorithms, and their computational requirements, along with the multifaceted applications. Coverage focuses broadly on the hardware aspects of training, inference, mobile devices, and autonomous vehicles (AVs) based AI accelerators
| Author | : Anupam Chattopadhyay |
| Publisher | : Springer |
| Total Pages | : 332 |
| Release | : 2017-04-19 |
| Genre | : Technology & Engineering |
| ISBN | : 3319548409 |
This book describes the current state of the art in big-data analytics, from a technology and hardware architecture perspective. The presentation is designed to be accessible to a broad audience, with general knowledge of hardware design and some interest in big-data analytics. Coverage includes emerging technology and devices for data-analytics, circuit design for data-analytics, and architecture and algorithms to support data-analytics. Readers will benefit from the realistic context used by the authors, which demonstrates what works, what doesn’t work, and what are the fundamental problems, solutions, upcoming challenges and opportunities. Provides a single-source reference to hardware architectures for big-data analytics; Covers various levels of big-data analytics hardware design abstraction and flow, from device, to circuits and systems; Demonstrates how non-volatile memory (NVM) based hardware platforms can be a viable solution to existing challenges in hardware architecture for big-data analytics.
| Author | : João Manuel Paiva Cardoso |
| Publisher | : Morgan Kaufmann |
| Total Pages | : 322 |
| Release | : 2017-06-13 |
| Genre | : Computers |
| ISBN | : 0128041994 |
Embedded Computing for High Performance: Design Exploration and Customization Using High-level Compilation and Synthesis Tools provides a set of real-life example implementations that migrate traditional desktop systems to embedded systems. Working with popular hardware, including Xilinx and ARM, the book offers a comprehensive description of techniques for mapping computations expressed in programming languages such as C or MATLAB to high-performance embedded architectures consisting of multiple CPUs, GPUs, and reconfigurable hardware (FPGAs). The authors demonstrate a domain-specific language (LARA) that facilitates retargeting to multiple computing systems using the same source code. In this way, users can decouple original application code from transformed code and enhance productivity and program portability. After reading this book, engineers will understand the processes, methodologies, and best practices needed for the development of applications for high-performance embedded computing systems. - Focuses on maximizing performance while managing energy consumption in embedded systems - Explains how to retarget code for heterogeneous systems with GPUs and FPGAs - Demonstrates a domain-specific language that facilitates migrating and retargeting existing applications to modern systems - Includes downloadable slides, tools, and tutorials
| Author | : Sundarapandian Vaidyanathan |
| Publisher | : Springer |
| Total Pages | : 513 |
| Release | : 2017-02-15 |
| Genre | : Technology & Engineering |
| ISBN | : 3319517244 |
This book reports on the latest advances in and applications of memristors, memristive devices and systems. It gathers 20 contributed chapters by subject experts, including pioneers in the field such as Leon Chua (UC Berkeley, USA) and R.S. Williams (HP Labs, USA), who are specialized in the various topics addressed in this book, and covers broad areas of memristors and memristive devices such as: memristor emulators, oscillators, chaotic and hyperchaotic memristive systems, control of memristive systems, memristor-based min-max circuits, canonic memristors, memristive-based neuromorphic applications, implementation of memristor-based chaotic oscillators, inverse memristors, linear memristor devices, delayed memristive systems, flux-controlled memristive emulators, etc. Throughout the book, special emphasis is given to papers offering practical solutions and design, modeling, and implementation insights to address current research problems in memristors, memristive devices and systems. As such, it offers a valuable reference book on memristors and memristive devices for graduate students and researchers with a basic knowledge of electrical and control systems engineering.
| Author | : Yakun Sophia Shao |
| Publisher | : Morgan & Claypool Publishers |
| Total Pages | : 101 |
| Release | : 2015-11-01 |
| Genre | : Computers |
| ISBN | : 162705832X |
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.
| Author | : Daichi Fujiki |
| Publisher | : Springer Nature |
| Total Pages | : 124 |
| Release | : 2022-05-31 |
| Genre | : Technology & Engineering |
| ISBN | : 3031017722 |
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.
| Author | : Nicola Bombieri |
| Publisher | : Springer |
| Total Pages | : 281 |
| Release | : 2019-06-25 |
| Genre | : Computers |
| ISBN | : 3030234258 |
This book contains extended and revised versions of the best papers presented at the 26th IFIP WG 10.5/IEEE International Conference on Very Large Scale Integration, VLSI-SoC 2018, held in Verona, Italy, in October 2018. The 13 full papers included in this volume were carefully reviewed and selected from the 27 papers (out of 106 submissions) presented at the conference. The papers discuss the latest academic and industrial results and developments as well as future trends in the field of System-on-Chip (SoC) design, considering the challenges of nano-scale, state-of-the-art and emerging manufacturing technologies. In particular they address cutting-edge research fields like heterogeneous, neuromorphic and brain-inspired, biologically-inspired, approximate computing systems.
