Thumbnail
Access Restriction
Subscribed

Author Rodopoulos, Dimitrios ♦ Psychou, Georgia ♦ Sabry, Mohamed M. ♦ Catthoor, Francky ♦ Papanikolaou, Antonis ♦ Soudris, Dimitrios ♦ Noll, Tobias G. ♦ Atienza, David
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2015
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Reliability analysis ♦ Classification framework ♦ Error ♦ Failure ♦ Fault
Abstract Technology downscaling is expected to amplify a variety of reliability concerns in future digital systems. A good understanding of reliability threats is crucial for the creation of efficient mitigation techniques. This survey performs a systematic classification of the state of the art on the analysis and modeling of such threats, which are caused by physical mechanisms to digital systems. The purpose of this article is to provide a classification tool that can aid with the navigation across the entire landscape of reliability analysis and modeling. A classification framework is constructed in a top-down fashion from complementary categories, each one addressing an approach on reliability analysis and modeling. In comparison to other classifications, the proposed methodology approaches the target research domain in a complete way, without suppressing hybrid works that fall under multiple categories. To substantiate the usability of the classification framework, representative works from the state of the art are mapped to each appropriate category and are briefly analyzed. Thus, research trends and opportunities for novel approaches can be identified.
ISSN 03600300
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2015-02-01
Publisher Place New York
e-ISSN 15577341
Journal ACM Computing Surveys (CSUR)
Volume Number 47
Issue Number 3
Page Count 33
Starting Page 1
Ending Page 33


Open content in new tab

   Open content in new tab
Source: ACM Digital Library