Soft Error Reliability Using Virtual Platforms - Early Evaluation of Multicore Systems (Hardcover, 1st ed. 2020)

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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.

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Product Description

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.

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Product Details

General

Imprint

Springer Nature Switzerland AG

Country of origin

Switzerland

Release date

November 2020

Availability

Expected to ship within 10 - 15 working days

First published

2020

Authors

, ,

Dimensions

235 x 155mm (L x W)

Format

Hardcover

Pages

136

Edition

1st ed. 2020

ISBN-13

978-3-03-055703-4

Barcode

9783030557034

Categories

LSN

3-03-055703-0



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