Modern CMOS technologies such as FDSOI are affected by severe aging effects that do not only depend on physical issues related to nanoscale technologies, but also on the circuit environment and its run-time activity. Therefore it is extremely difficult to reliably establish a-priori guard bands for Critical Path estimations, usually leading to both large delay penalties (and therefore loss of performances) or too short operating lifetime. In this paper, we propose an approach that uses Machine Learning techniques to obtain reliable predictions of the aging of the Near Critical Paths. Starting from a limited set of measurements and simulation data, our framework is able to accurately estimate Critical Path delay degradation in time depending on physical parameters, environment conditions and circuit activity. Further to that, the corresponding regression models are applied to obtain dynamic aging-aware Operating Performance Point selection strategies.

FDSOI 等现代 CMOS 技术受到严重老化效应的影响，老化效应不仅取决于与纳米级技术相关的物理问题，还取决于电路环境及其运行时活动。因此，为关键路径估计可靠地建立先验保护频带极其困难，通常会导致较大的延迟惩罚（并因此导致性能损失）或工作寿命太短。在本文中，我们提出了一种使用机器学习技术来获得对近关键路径老化的可靠预测的方法。从一组有限的测量和仿真数据开始，我们的框架能够根据物理参数、环境条件和电路活动及时准确地估计关键路径延迟退化。除此之外，