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Single event effects evaluation on convolution neural network in Xilinx 28 nm system on chip |
| Xu Zhao(赵旭)1, Xuecheng Du(杜雪成)1,†, Xu Xiong(熊旭)1, Chao Ma(马超)1, Weitao Yang(杨卫涛)2, Bo Zheng(郑波)1, and Chao Zhou(周超)1 |
1 School of Nuclear Science and Technology, University of South China, Hengyang 421001, China;
2 School of Microelectronics, Xidian University, Xi'an 710071, China |
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Abstract Convolutional neural networks (CNNs) exhibit excellent performance in the areas of image recognition and object detection, which can enhance the intelligence level of spacecraft. However, in aerospace, energetic particles, such as heavy ions, protons, and alpha particles, can induce single event effects (SEEs) that lead CNNs to malfunction and can significantly impact the reliability of a CNN system. In this paper, the MNIST CNN system was constructed based on a 28 nm system-on-chip (SoC), and then an alpha particle irradiation experiment and fault injection were applied to evaluate the SEE of the CNN system. Various types of soft errors in the CNN system have been detected, and the SEE cross sections have been calculated. Furthermore, the mechanisms behind some soft errors have been explained. This research will provide technical support for the design of radiation-resistant artificial intelligence chips.
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Received: 09 January 2024
Revised: 29 February 2024
Accepted manuscript online: 07 April 2024
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PACS:
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85.30.De
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(Semiconductor-device characterization, design, and modeling)
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61.82.Fk
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(Semiconductors)
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85.35.-p
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(Nanoelectronic devices)
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| Fund: Project supported by the National Natural Science Foundation of China (Grant No. 12305303), the Natural Science Foundation of Hunan Province of China (Grant Nos. 2023JJ40520, 2021JJ40444, and 2019JJ30019), the Research Foundation of Education Bureau of Hunan Province of China (Grant No. 20A430), the Science and Technology Innovation Program of Hunan Province (Grant No. 2020RC3054), the Natural Science Basic Research Plan in the Shaanxi Province of China (Grant No. 2023-JC-QN-0015), and the Doctoral Research Fund of University of South China. |
Corresponding Authors:
Xuecheng Du
E-mail: duxuecheng@usc.edu.cn
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Cite this article:
Xu Zhao(赵旭), Xuecheng Du(杜雪成), Xu Xiong(熊旭), Chao Ma(马超), Weitao Yang(杨卫涛), Bo Zheng(郑波), and Chao Zhou(周超) Single event effects evaluation on convolution neural network in Xilinx 28 nm system on chip 2024 Chin. Phys. B 33 078501
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