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.

翻译：越来越多的未加固商用现成嵌入式设备被部署在恶劣操作环境和高可靠性系统中。由于影响这些设备的硬件退化机制，老化检测与监控对于预防关键故障至关重要。本文通过实证研究，分析了部署于欧洲XFEL粒子加速器的298个自然老化FPGA器件的传播延迟特性。基于现场测量数据，我们发现运行中器件的开关频率显著低于未使用芯片，且增加的伽马射线与中子辐射剂量与硬件退化程度呈正相关。此外，我们证明了基于历史与环境数据构建机器学习模型以估算器件开关频率的可行性。