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Simulating and detecting radiation-induced errors for onboard machine learning

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dc.contributor.author Wagstaff, Kiri L.
dc.contributor.author Bornstein, Benjamin
dc.contributor.author Granat, Robert
dc.contributor.author Tang, Benyang
dc.contributor.author Turmon, Michael
dc.date.accessioned 2014-08-26T23:20:52Z
dc.date.available 2014-08-26T23:20:52Z
dc.date.issued 2009-07-19
dc.identifier.citation Third IEEE International Conference on Space Mission Challenges for Information Technology, Pasadena, California, July 19-23, 2009 en_US
dc.identifier.clearanceno 09-1474
dc.identifier.uri http://hdl.handle.net/2014/44653
dc.description.abstract 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 found ABFT error detection for matrix multiplication is very successful, while error detection for Gaussian kernel computation still has room for improvement. en_US
dc.description.sponsorship NASA/JPL en_US
dc.language.iso en_US en_US
dc.publisher Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2009 en_US
dc.subject data analysis en_US
dc.title Simulating and detecting radiation-induced errors for onboard machine learning en_US
dc.type Presentation en_US


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