Using machine learning for anomaly detection on a system-on-chip under gamma radiation

Wächter Eduardo Weber; Kasap Server; Kolozali Şefki; Zhai Xiaojun; Ehsan Shoaib; McDonald-Maier Klaus D.

doi:10.1016/j.net.2022.06.028

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자료유형: 학술저널

저자정보: Wächter Eduardo Weber (University of Essex) Kasap Server (School of Computing, Electronics and Mathematics, Coventry University) Kolozali Şefki (University of Essex) Zhai Xiaojun (University of Essex) Ehsan Shoaib (University of Essex) McDonald-Maier Klaus D. (University of Essex)

저널정보: 한국원자력학회 Nuclear Engineering and Technology Nuclear Engineering and Technology 제54권 제11호

발행연도: 2022.11

수록면: 3,985 - 3,995 (11page)

DOI: 10.1016/j.net.2022.06.028

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The emergence of new nanoscale technologies has imposed significant challenges to designing reliable electronic systems in radiation environments. A few types of radiation like Total Ionizing Dose (TID) can cause permanent damages on such nanoscale electronic devices, and current state-of-the-art technologies to tackle TID make use of expensive radiation-hardened devices. This paper focuses on a novel and different approach: using machine learning algorithms on consumer electronic level Field Programmable Gate Arrays (FPGAs) to tackle TID effects and monitor them to replace before they stop working. This condition has a research challenge to anticipate when the board results in a total failure due to TID effects. We observed internal measurements of FPGA boards under gamma radiation and used three different anomaly detection machine learning (ML) algorithms to detect anomalies in the sensor measurements in a gamma-radiated environment. The statistical results show a highly significant relationship between the gamma radiation exposure levels and the board measurements. Moreover, our anomaly detection results have shown that a One-Class SVM with Radial Basis Function Kernel has an average recall score of 0.95. Also, all anomalies can be detected before the boards are entirely inoperative, i.e. voltages drop to zero and confirmed with a sanity check.

#Gamma radiation #Machine learning #Anomaly detection #Field programmable gate arrays. TID

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