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

TL;DR

This paper demonstrates that machine learning algorithms, particularly One-Class SVMs, can effectively detect anomalies in FPGA boards under gamma radiation, enabling preemptive replacement and improving reliability in radiation environments.

Contribution

It introduces a novel approach using machine learning on consumer FPGAs to detect TID effects caused by gamma radiation, reducing reliance on expensive radiation-hardened devices.

Findings

One-Class SVM achieved an average recall of 0.95.

Anomalies were detected before FPGA failure.

Significant correlation between gamma radiation levels and measurements.

Abstract

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) effects often 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 the 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 Support Vector Machine with Radial Basis Function Kernel has an average Recall score of 0.95. Also, all anomalies can be detected before the boards stop working.