In-Memory Computing
In-Memory Computing

Author: Saeideh Shirinzadeh

Publisher: Springer

Published: 2019-05-22

Total Pages: 121

ISBN-13: 3030180263

DOWNLOAD EBOOK

This book describes a comprehensive approach for synthesis and optimization of logic-in-memory computing hardware and architectures using memristive devices, which creates a firm foundation for practical applications. Readers will get familiar with a new generation of computer architectures that potentially can perform faster, as the necessity for communication between the processor and memory is surpassed. The discussion includes various synthesis methodologies and optimization algorithms targeting implementation cost metrics including latency and area overhead as well as the reliability issue caused by short memory lifetime. Presents a comprehensive synthesis flow for the emerging field of logic-in-memory computing; Describes automated compilation of programmable logic-in-memory computer architectures; Includes several effective optimization algorithm also applicable to classical logic synthesis; Investigates unbalanced write traffic in logic-in-memory architectures and describes wear leveling approaches to alleviate it.

In-/Near-Memory Computing
In-/Near-Memory Computing

Author: Daichi Fujiki

Publisher: Springer Nature

Published: 2022-05-31

Total Pages: 124

ISBN-13: 3031017722

DOWNLOAD EBOOK

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.

In-Memory Data Management
In-Memory Data Management

Author: Hasso Plattner

Publisher: Springer Science & Business Media

Published: 2011-03-08

Total Pages: 245

ISBN-13: 3642193633

DOWNLOAD EBOOK

In the last 50 years the world has been completely transformed through the use of IT. We have now reached a new inflection point. Here we present, for the first time, how in-memory computing is changing the way businesses are run. Today, enterprise data is split into separate databases for performance reasons. Analytical data resides in warehouses, synchronized periodically with transactional systems. This separation makes flexible, real-time reporting on current data impossible. Multi-core CPUs, large main memories, cloud computing and powerful mobile devices are serving as the foundation for the transition of enterprises away from this restrictive model. We describe techniques that allow analytical and transactional processing at the speed of thought and enable new ways of doing business. The book is intended for university students, IT-professionals and IT-managers, but also for senior management who wish to create new business processes by leveraging in-memory computing.

Data Analytics with Hadoop
Data Analytics with Hadoop

Author: Benjamin Bengfort

Publisher: "O'Reilly Media, Inc."

Published: 2016-06

Total Pages: 288

ISBN-13: 1491913762

DOWNLOAD EBOOK

Ready to use statistical and machine-learning techniques across large data sets? This practical guide shows you why the Hadoop ecosystem is perfect for the job. Instead of deployment, operations, or software development usually associated with distributed computing, you’ll focus on particular analyses you can build, the data warehousing techniques that Hadoop provides, and higher order data workflows this framework can produce. Data scientists and analysts will learn how to perform a wide range of techniques, from writing MapReduce and Spark applications with Python to using advanced modeling and data management with Spark MLlib, Hive, and HBase. You’ll also learn about the analytical processes and data systems available to build and empower data products that can handle—and actually require—huge amounts of data. Understand core concepts behind Hadoop and cluster computing Use design patterns and parallel analytical algorithms to create distributed data analysis jobs Learn about data management, mining, and warehousing in a distributed context using Apache Hive and HBase Use Sqoop and Apache Flume to ingest data from relational databases Program complex Hadoop and Spark applications with Apache Pig and Spark DataFrames Perform machine learning techniques such as classification, clustering, and collaborative filtering with Spark’s MLlib

The Apache Ignite Book
The Apache Ignite Book

Author: Michael Zheludkov

Publisher: Lulu.com

Published: 2019-02-25

Total Pages: 642

ISBN-13: 0359439373

DOWNLOAD EBOOK

Apache Ignite is one of the most widely used open source memory-centric distributed, caching, and processing platform. This allows the users to use the platform as an in-memory computing framework or a full functional persistence data stores with SQL and ACID transaction support. On the other hand, Apache Ignite can be used for accelerating existing Relational and NoSQL databases, processing events & streaming data or developing Microservices in fault-tolerant fashion. This book addressed anyone interested in learning in-memory computing and distributed database. This book intends to provide someone with little to no experience of Apache Ignite with an opportunity to learn how to use this platform effectively from scratch taking a practical hands-on approach to learning. Please see the table of contents for more details.

Artificial Intelligence Hardware Design
Artificial Intelligence Hardware Design

Author: Albert Chun-Chen Liu

Publisher: John Wiley & Sons

Published: 2021-08-23

Total Pages: 244

ISBN-13: 1119810477

DOWNLOAD EBOOK

ARTIFICIAL INTELLIGENCE HARDWARE DESIGN Learn foundational and advanced topics in Neural Processing Unit design with real-world examples from leading voices in the field In Artificial Intelligence Hardware Design: Challenges and Solutions, distinguished researchers and authors Drs. Albert Chun Chen Liu and Oscar Ming Kin Law deliver a rigorous and practical treatment of the design applications of specific circuits and systems for accelerating neural network processing. Beginning with a discussion and explanation of neural networks and their developmental history, the book goes on to describe parallel architectures, streaming graphs for massive parallel computation, and convolution optimization. The authors offer readers an illustration of in-memory computation through Georgia Tech’s Neurocube and Stanford’s Tetris accelerator using the Hybrid Memory Cube, as well as near-memory architecture through the embedded eDRAM of the Institute of Computing Technology, the Chinese Academy of Science, and other institutions. Readers will also find a discussion of 3D neural processing techniques to support multiple layer neural networks, as well as information like: A thorough introduction to neural networks and neural network development history, as well as Convolutional Neural Network (CNN) models Explorations of various parallel architectures, including the Intel CPU, Nvidia GPU, Google TPU, and Microsoft NPU, emphasizing hardware and software integration for performance improvement Discussions of streaming graph for massive parallel computation with the Blaize GSP and Graphcore IPU An examination of how to optimize convolution with UCLA Deep Convolutional Neural Network accelerator filter decomposition Perfect for hardware and software engineers and firmware developers, Artificial Intelligence Hardware Design is an indispensable resource for anyone working with Neural Processing Units in either a hardware or software capacity.

Green Computing with Emerging Memory
Green Computing with Emerging Memory

Author: Takayuki Kawahara

Publisher: Springer Science & Business Media

Published: 2012-09-26

Total Pages: 214

ISBN-13: 1461408121

DOWNLOAD EBOOK

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.

In-Memory Computing Hardware Accelerators for Data-Intensive Applications
In-Memory Computing Hardware Accelerators for Data-Intensive Applications

Author: Baker Mohammad

Publisher: Springer Nature

Published: 2023-10-27

Total Pages: 145

ISBN-13: 303134233X

DOWNLOAD EBOOK

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.

In-Memory Data Management
In-Memory Data Management

Author: Hasso Plattner

Publisher: Springer Science & Business Media

Published: 2012-04-17

Total Pages: 286

ISBN-13: 3642295754

DOWNLOAD EBOOK

In the last fifty years the world has been completely transformed through the use of IT. We have now reached a new inflection point. This book presents, for the first time, how in-memory data management is changing the way businesses are run. Today, enterprise data is split into separate databases for performance reasons. Multi-core CPUs, large main memories, cloud computing and powerful mobile devices are serving as the foundation for the transition of enterprises away from this restrictive model. This book provides the technical foundation for processing combined transactional and analytical operations in the same database. In the year since we published the first edition of this book, the performance gains enabled by the use of in-memory technology in enterprise applications has truly marked an inflection point in the market. The new content in this second edition focuses on the development of these in-memory enterprise applications, showing how they leverage the capabilities of in-memory technology. The book is intended for university students, IT-professionals and IT-managers, but also for senior management who wish to create new business processes.

High Performance in-memory computing with Apache Ignite
High Performance in-memory computing with Apache Ignite

Author: Shamim bhuiyan

Publisher: Lulu.com

Published: 2017-04-08

Total Pages: 360

ISBN-13: 1365732355

DOWNLOAD EBOOK

This book covers a verity of topics, including in-memory data grid, highly available service grid, streaming (event processing for IoT and fast data) and in-memory computing use cases from high-performance computing to get performance gains. The book will be particularly useful for those, who have the following use cases: 1) You have a high volume of ACID transactions in your system. 2) You have database bottleneck in your application and want to solve the problem. 3) You want to develop and deploy Microservices in a distributed fashion. 4) You have an existing Hadoop ecosystem (OLAP) and want to improve the performance of map/reduce jobs without making any changes in your existing map/reduce jobs. 5) You want to share Spark RDD directly in-memory (without storing the state into the disk) 7) You are planning to process continuous never-ending streams and complex events of data. 8) You want to use distributed computations in parallel fashion to gain high performance.