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Titlebook: Soft Error Reliability Using Virtual Platforms; Early Evaluation of Felipe Rocha da Rosa,Luciano Ost,Ricardo Reis Book 2020 The Editor(s)

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發(fā)表于 2025-3-21 16:58:16 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Soft Error Reliability Using Virtual Platforms
副標(biāo)題Early Evaluation of
編輯Felipe Rocha da Rosa,Luciano Ost,Ricardo Reis
視頻videohttp://file.papertrans.cn/871/870556/870556.mp4
概述Describes the most suitable and efficient virtual platforms to include fault injection capabilities, aiming to support the soft error analysis of state-of-the-art processor models.Includes analysis an
圖書封面Titlebook: Soft Error Reliability Using Virtual Platforms; Early Evaluation of  Felipe Rocha da Rosa,Luciano Ost,Ricardo Reis Book 2020 The Editor(s)
描述.This book describes the benefits and drawbacks inherent in the use of virtual platforms (VPs) to perform fast and early soft error assessment of multicore systems. The authors show that VPs provide engineers with appropriate means to investigate new and more efficient fault injection and mitigation techniques. Coverage also includes the use of machine learning techniques (e.g., linear regression) to speed-up the soft error evaluation process by pinpointing parameters (e.g., architectural) with the most substantial impact on the software stack dependability.? This book provides valuable information and insight through more than 3 million individual scenarios and 2 million simulation-hours. Further, this book explores machine learning techniques usage to navigate large fault injection datasets..
出版日期Book 2020
關(guān)鍵詞software reliability; soft error analysis; Fault Injection; Machine Learning Applied to Soft Error Asse
版次1
doihttps://doi.org/10.1007/978-3-030-55704-1
isbn_softcover978-3-030-55706-5
isbn_ebook978-3-030-55704-1
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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發(fā)表于 2025-3-21 22:58:52 | 只看該作者
https://doi.org/10.1007/978-3-030-55704-1software reliability; soft error analysis; Fault Injection; Machine Learning Applied to Soft Error Asse
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發(fā)表于 2025-3-22 01:43:46 | 只看該作者
Felipe Rocha da Rosa,Luciano Ost,Ricardo ReisDescribes the most suitable and efficient virtual platforms to include fault injection capabilities, aiming to support the soft error analysis of state-of-the-art processor models.Includes analysis an
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Introduction,ing (HPC) domains. The progressively growing computing capacity of multicore-based systems enables efficiently compute complex application workloads at a lower power consumption compared to traditional single-core solutions. The number of computing components employed in large-scale HPC systems alre
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Performance and Accuracy Assessment of Fault Injection Frameworks Based on VPs,stigation considers an extensive comparison between two distinct virtual platforms (i.e., instruction- vs. cycle-accurate) regarding simulation performance, soft error analysis credibility/accuracy, and fault injection flexibility. To accomplish these three objectives, this chapter comprises 3,344,0
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