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    A Model-Based Soft Errors Risks Minimization Approach

    135322_david1.pdf (252.7Kb)
    Access Status
    Open access
    Authors
    Sadi, M.
    Myers, Doug
    Ortega-Sanchez, Cesar
    Date
    2009
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Sadi, M. and Myers, Doug and Ortega-Sanchez, Cesar. 2009. A Model-Based Soft Errors Risks Minimization Approach, in Yu Hen Hu (ed), 4th International Conference on Embedded and Multimedia Computing, 2009, Dec 10 2009, pp. 1-6. Jeju, South Korea: IEEE.
    Source Title
    Proceedings of 2009 Fourth International Conference on Embedded and Multimedia Computing
    Source Conference
    4th International Conference on Embedded and Multimedia Computing, 2009
    Additional URLs
    http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=05402994
    ISBN
    978-1-4244-4995-8
    Faculty
    Department of Electrical and Computer Engineering
    School of Engineering
    Faculty of Science and Engineering
    Remarks

    Copyright © 2009 IEEEThis material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

    URI
    http://hdl.handle.net/20.500.11937/28588
    Collection
    • Curtin Research Publications
    Abstract

    Minimizing the risk of system failure in any computer structure requires identifying those components whose failure is likely to impact on system functionality. Clearly, the degree of protection or prevention required against faults is not the same for all components. Tolerating soft errors can be much improved if critical components can be identified at an early design phase and measures are taken to lower their criticalities at that stage. This improvement is achieved by presenting a criticality ranking (among the components) formed by combining a prediction of faults, consequences of them, and a propagation of errors at the system modeling phase; and pointing out ways to apply changes in the model to minimize the risk of degradation of desired functionalities. Case study results are given to validate the approach.

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