Using machine learning for anomaly detection on a system-on-chip under gamma radiation
Using machine learning for anomaly detection on a system-on-chip under gamma radiation
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.
Anomaly detection, Field programmable gate arrays. TID, Gamma radiation, Machine learning
3985-3995
Wachter, Eduardo Weber
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Kasap, Server
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Kolozali, Sefki
23ec37c0-4c78-4dfe-ab34-191f5f2d9709
Zhai, Xiaojun
93ee3dbb-e10e-472b-adec-78acfcd4cbc7
Ehsan, Shoaib
ae8922f0-dbe0-4b22-8474-98e84d852de7
McDonald-Maier, Klaus D.
d35c2e77-744a-4318-9d9d-726459e64db9
3 November 2022
Wachter, Eduardo Weber
bdacc537-b1ac-4241-a6fc-b67f1e6a6ce8
Kasap, Server
e49310e0-96aa-42e1-8259-6ad34cc1b025
Kolozali, Sefki
23ec37c0-4c78-4dfe-ab34-191f5f2d9709
Zhai, Xiaojun
93ee3dbb-e10e-472b-adec-78acfcd4cbc7
Ehsan, Shoaib
ae8922f0-dbe0-4b22-8474-98e84d852de7
McDonald-Maier, Klaus D.
d35c2e77-744a-4318-9d9d-726459e64db9
Wachter, Eduardo Weber, Kasap, Server, Kolozali, Sefki, Zhai, Xiaojun, Ehsan, Shoaib and McDonald-Maier, Klaus D.
(2022)
Using machine learning for anomaly detection on a system-on-chip under gamma radiation.
Nuclear Engineering and Technology, 54 (11), .
(doi:10.1016/j.net.2022.06.028).
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) 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.
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Accepted/In Press date: 26 June 2022
e-pub ahead of print date: 30 June 2022
Published date: 3 November 2022
Additional Information:
Funding Information:
This work is supported by the U.K. Engineering and Physical Sciences Research Council through grants EP/R02572X/1 , EP/P017487/1 , EP/V000462/1 , and EP/V034111/1 .
Funding Information:
We acknowledge the support of The University of Manchester’s Dalton Cumbrian Facility (DCF) , a partner in the National Nuclear User Facility, the EPSRC UK National Ion Beam Centre and the Henry Royce Institute . We recognise Kevin Warren for their assistance during the experiments.
Funding Information:
This work is supported by the U.K. Engineering and Physical Sciences Research Council through grants EP/R02572X/1, EP/P017487/1, EP/V000462/1, and EP/V034111/1.We acknowledge the support of The University of Manchester's Dalton Cumbrian Facility (DCF), a partner in the National Nuclear User Facility, the EPSRC UK National Ion Beam Centre and the Henry Royce Institute. We recognise Kevin Warren for their assistance during the experiments.
Publisher Copyright:
© 2022 Korean Nuclear Society
Keywords:
Anomaly detection, Field programmable gate arrays. TID, Gamma radiation, Machine learning
Identifiers
Local EPrints ID: 473504
URI: http://eprints.soton.ac.uk/id/eprint/473504
ISSN: 1738-5733
PURE UUID: cd93cacd-0831-4c73-8c1f-cc07dc55110d
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Date deposited: 20 Jan 2023 18:02
Last modified: 17 Mar 2024 04:16
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Contributors
Author:
Eduardo Weber Wachter
Author:
Server Kasap
Author:
Sefki Kolozali
Author:
Xiaojun Zhai
Author:
Shoaib Ehsan
Author:
Klaus D. McDonald-Maier
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