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{ "item_title" : "Soft Error Reliability Using Virtual Platforms", "item_author" : [" Felipe Rocha Da Rosa", "Luciano Ost", "Ricardo Reis "], "item_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.", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/3/03/055/706/3030557065_b.jpg", "price_data" : { "retail_price" : "109.99", "online_price" : "109.99", "our_price" : "109.99", "club_price" : "109.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Soft Error Reliability Using Virtual Platforms|Felipe Rocha Da Rosa

Soft Error Reliability Using Virtual Platforms : Early Evaluation of Multicore Systems

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Overview

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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Details

  • ISBN-13: 9783030557065
  • ISBN-10: 3030557065
  • Publisher: Springer
  • Publish Date: November 2021
  • Dimensions: 9.21 x 6.14 x 0.32 inches
  • Shipping Weight: 0.48 pounds
  • Page Count: 136

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