Автор: Krishna Tushar, Kwon Hyoukjun, Parashar Angshuman Название: Data Orchestration in Deep Learning Accelerators ISBN: 1681738694 ISBN-13(EAN): 9781681738697 Издательство: Mare Nostrum (Eurospan) Цена: 9979.00 р. Наличие на складе: Поставка под заказ.
Описание: 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.
Автор: Huang, Xiaobiao Название: Beam-based correction and optimization for accelerators ISBN: 1138353167 ISBN-13(EAN): 9781138353169 Издательство: Taylor&Francis Рейтинг: Цена: 17609.00 р. Наличие на складе: Нет в наличии.
Описание: This book provides systematic coverage of the beam based techniques that accelerator physicists use to improve the performance of large particle accelerators, including synchrotrons and linacs.
Описание: 1 Overview of data architecture on Microsoft Azure Introduction Technologies: everyone touched in the book plus some other edge technologies just mentioned. We explain the scenarios of the book.This chapter will be written during the whole process, updating it with the relevant content of the scenarios developed in the chapters. 2 Working with Relational DBs on Azure Relational DBs scenarios Technologies: VMs, Backup, Storage, SQL Server DR and GEO-DR, (Oracle, MySQL)We would like to cover the best practices to deploy standard RDBMSs while using Azure VMs and networking. 3 Working with Azure SQL Database Azure DB scenarios Technologies: Azure SQL DB, Stretch DB, Database Pools, Sharding data, Migration from other RDBMSs to Azure SQL DBThis chapter is about the SQL Database PaaS, with some tricks for advanced usage. We cover the services and the connected services, how to scale with relational DBs and how to write multi-tenant applications. 4 Working with NoSQL alternatives NoSQL scenarios Technologies: Storage, DocumentDB, Redis, Azure SearchThis chapter enforces the polyglot persistence idea, where different technologies and data sources address different needs. The NoSQL alternatives can fill the gap of modern applications in terms of performance and feature set. 5 Orchestrate data with Azure Data Factory Integration scenarios Technologies: Azure Data FactoryIn this chapter we talk about integration of different data sources and advanced pipelines of data transformation. We explore some scenarios to lower the complexity of the Data Factory service and we see how to setup existing solutions to fit it. 6 Advanced analysis with Azure Data Lake Analysis scenarios Technologies: Data Lake Store/Analytics, U-SQLThis is the first chapter about ingestion of big data. This is focused on ingestion of native data, to be prepared, enriched and evaluated/analyzed in a second step. 7 Real-time Ingestion, Processing and Prediction Real-time scenarios Technologies: Machine Learning, Stream Analytics, Event HubThis is instead focused in ingestion of well-known and structured data with the aim to process it in real-time. In addition a step of prediction is added to react (in real time too) to certain events. 8 Working with Big Data with Azure Batch and Map/Reduce Big data scenarios Technologies: HDInsight, Hadoop, Spark, R Server, Storm, Azure BatchThis is the last chapter about Big Data, exploring the industry standard to perform data operations, while executing those engines on Azure. 9 APPENDIX (tbd yet, adding some of the topics below to one of the other chapter still has to be defined) Other Technologies: Azure Analysis Services (chapter 3?), Power BI (chapter 6?), Azure SQL DataWarehouse (chapter 6?), Azure Data Catalog (chapter 5?)Those technologies should fit the existing chapters, but we do not know where they best fit at the time being.
Автор: Randolph Jason Название: The Orchestration of the Failure of Society ISBN: 0985682639 ISBN-13(EAN): 9780985682637 Издательство: Неизвестно Рейтинг: Цена: 1952.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Siddiqui Adnan Ahmed Название: OpenStack Orchestration ISBN: 1783551658 ISBN-13(EAN): 9781783551651 Издательство: Неизвестно Рейтинг: Цена: 8091.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Berlioz Название: A Treatise upon Modern Instrumentation and Orchestration ISBN: 1108021166 ISBN-13(EAN): 9781108021166 Издательство: Cambridge Academ Рейтинг: Цена: 4910.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This is a translation of the second (1858) edition of Berlioz`s treatise by Mary Cowden Clarke, daughter of music publisher Vincent Novello. The work was quick to establish itself as a standard text, and reflects Berlioz`s keen understanding of instrumentation and the orchestra as both composer and conductor.
Автор: McGuire, Sam , Mateju, Zbynek Название: The Art of Digital Orchestration ISBN: 0367362740 ISBN-13(EAN): 9780367362744 Издательство: Taylor&Francis Рейтинг: Цена: 7195.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The Art of Digital Orchestration explores how to replicate traditional orchestration techniques using computer technology, with a focus on respecting the music and understanding when using real performers is still the best choice.
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