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Using Machine Learning for Anomaly Detection on a System-on-Chip under Gamma Radiation

Wachter, Eduardo Weber and Kasap, Server and Kolozali, Sefki and Zhai, Xiaojun and Ehsan, Shoaib and McDonald-Maier, Klaus (2022) 'Using Machine Learning for Anomaly Detection on a System-on-Chip under Gamma Radiation.' Nuclear Engineering and Technology. ISSN 1738-5733

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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.

Item Type: Article
Uncontrolled Keywords: Gamma radiation; Machine learning; Anomaly detection; Field programmable gate arrays. TID
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 29 Jul 2022 14:46
Last Modified: 23 Sep 2022 19:51

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