Fault Classification and Diagnosis Approach Using FFT-CNN for FPGA-Based CORDIC Processor

Yu Xie, He Chen*, Yin Zhuang, Yizhuang Xie

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

6 引用 (Scopus)

摘要

Within the realm of digital signal processing and communication systems, FPGA-based CORDIC (Coordinate Rotation Digital Computer) processors play pivotal roles, applied in trigonometric calculations and vector operations. However, soft errors have become one of the major threats in high-reliability FPGA-based applications, potentially degrading performance and causing system failures. This paper proposes a fault classification and diagnosis method for FPGA-based CORDIC processors, leveraging Fast Fourier Transform (FFT) and Convolutional Neural Networks (CNNs). The approach involves constructing fault classification datasets, optimizing features extraction through FFT to shorten the time of diagnosis and improve the diagnostic accuracy, and employing CNNs for training and testing of faults diagnosis. Different CNN architectures are tested to explore and construct the optimal fault classifier. Experimental results encompassing simulation and implementation demonstrate the improved accuracy and efficiency in fault classification and diagnosis. The proposed method provides fault prediction with an accuracy of more than 98.6% and holds the potential to enhance the reliability and performance of FPGA-based CORDIC circuit systems, surpassing traditional fault diagnosis methods such as Sum of Square (SoS).

源语言英语
文章编号72
期刊Electronics (Switzerland)
13
1
DOI
出版状态已出版 - 1月 2024

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Xie, Y., Chen, H., Zhuang, Y., & Xie, Y. (2024). Fault Classification and Diagnosis Approach Using FFT-CNN for FPGA-Based CORDIC Processor. Electronics (Switzerland), 13(1), 文章 72. https://doi.org/10.3390/electronics13010072