Spacecraft processors and memory are subjected to high radiation doses and therefore employ radiation-hardened components. However, these components are orders of magnitude more expensive than typical desktop components, and they lag years behind in terms of speed and size. We have integrated algorithm-based fault tolerance (ABFT) methods into onboard data analysis algorithms to detect radiation-induced errors, which ultimately may permit the use of spacecraft memory that need not be fully hardened, reducing cost and increasing capability at the same time. We have also developed a lightweight software radiation simulator, BITFLIPS, that permits evaluation of error detection strategies in a controlled fashion, including the specification of the radiation rate and selective exposure of individual data structures. Using BITFLIPS, we evaluated our error detection methods when using a support vector machine to analyze data collected by the Mars Odyssey spacecraft. We observed good performance from both an existing ABFT method for matrix multiplication and a novel ABFT method for exponentiation. These techniques bring us a step closer to "rad-hard" machine learning algorithms.


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    Titel :

    Simulating and Detecting Radiation-Induced Errors for Onboard Machine Learning


    Beteiligte:
    Granat, Robert (Autor:in) / Wagstaff, Kiri L. (Autor:in) / Bornstein, Benjamin (Autor:in) / Tang, Benyang (Autor:in) / Turmon, Michael (Autor:in)


    Erscheinungsdatum :

    2009-07-01


    Format / Umfang :

    327242 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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