欧洲XFEL装置中FPGA传播延迟的退化研究
Studying the Degradation of Propagation Delay on FPGAs at the European XFEL
AI总结：
针对高可靠系统中商用FPGA的硬件老化问题，本文通过对欧洲XFEL中298块自然老化FPGA的实测研究，明确辐射剂量与退化的相关性，并验证了用机器学习模型基于环境与历史数据估算器件开关频率的可行性。
摘要：
越来越多未做加固处理的商用现货嵌入式设备被部署在恶劣运行条件下的高可靠系统中。由于影响这些设备的硬件退化机制，老化检测与监测对于预防关键故障至关重要。本文对部署在欧洲XFEL粒子加速器中的298块自然老化的FPGA器件的传播延迟开展了实证研究。基于现场测量结果，我们发现运行中的器件开关频率显著低于未使用的芯片，且伽马与中子辐射剂量的增加与硬件退化程度的升高存在相关性。此外，我们验证了开发机器学习模型的可行性，这类模型可基于历史数据与环境数据估算器件的开关频率。
英文摘要：
An increasing number of unhardened commercial-off-the-shelf embedded devices are deployed under harsh operating conditions and in highly-dependable systems. Due to the mechanisms of hardware degradation that affect these devices, ageing detection and monitoring are crucial to prevent critical failures. In this paper, we empirically study the propagation delay of 298 naturally-aged FPGA devices that are deployed in the European XFEL particle accelerator. Based on in-field measurements, we find that operational devices show significantly slower switching frequencies than unused chips, and that increased gamma and neutron radiation doses correlate with increased hardware degradation. Furthermore, we demonstrate the feasibility of developing machine learning models that estimate the switching frequencies of the devices based on historical and environmental data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
