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Titlebook: Near Threshold Computing; Technology, Methods Michael Hübner,Cristina Silvano Book 2016 Springer International Publishing Switzerland 2016

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书目名称Near Threshold Computing
副标题Technology, Methods
编辑Michael Hübner,Cristina Silvano
视频video
概述Provides an introduction to near-threshold computing, enabling reader with a variety of tools to face the challenges of the power/utilization wall.Demonstrates how to design efficient voltage regulati
图书封面Titlebook: Near Threshold Computing; Technology, Methods  Michael Hübner,Cristina Silvano Book 2016 Springer International Publishing Switzerland 2016
描述This book explores near-threshold computing (NTC), a design-space using techniques to run digital chips (processors) near the lowest possible voltage. Readers will be enabled with specific techniques to design chips that are extremely robust; tolerating variability and resilient against errors. Variability-aware voltage and frequency allocation schemes will be presented that will provide performance guarantees, when moving toward near-threshold manycore chips.. ·         Provides an introduction to near-threshold computing, enabling reader with a variety of tools to face the challenges of the power/utilization wall;.·         Demonstrates how to design efficient voltage regulation, so that each region of the chip can operate at the most efficient voltage and frequency point;.·         Investigates how performance guarantees can be ensured when moving towards NTC manycores through variability-aware voltage and frequency allocation schemes..
出版日期Book 2016
关键词Dark Silicon; Low-voltage Memory Design; Manycore System-on-chip; Near Threshold Computing; Power Wall; U
版次1
doihttps://doi.org/10.1007/978-3-319-23389-5
isbn_softcover978-3-319-37209-9
isbn_ebook978-3-319-23389-5
copyrightSpringer International Publishing Switzerland 2016
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Josep Torrellase array of applications, particularly web-based ones. However, the dynamic and unpredictable nature of workloads in web applications poses considerable challenges in designing effective strategies for cloud auto-scaling. Existing research primarily relies on single-step prediction methods or focuses
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