Scalable Machine Learning to Estimate the Impact of Aging on Circuits Under Workload Dependency

• Published in: IEEE transactions on circuits and systems. I, Regular papers Regular papers, 2022-05, Vol.69 (5), p.2142-2155
• Main Authors: Klemme, Florian, Amrouch, Hussam
• Format: Article
• Language: English
• Subjects: Accuracy; Aging; Aging (artificial); cell library characterization; Circuits; Degradation; Libraries; Machine learning; ML-CAD; reliability; Scalability; Static timing analysis; Timing; transistor aging; Transistors; Workload; Workloads
• ISSN: 1549-8328; 1558-0806
• DOI: 10.1109/TCSI.2022.3147587

To ensure the correct functionality of a chip throughout its entire lifetime, preliminary circuit analysis with respect to aging-induced degradation is indispensable. However, state-of-the-art techniques only allow for the consideration of uniformly applied degradations, despite the fact that different workloads will lead to different degradations due to their distinct induced activities. This imposes over-pessimism when estimating the required timing guardbands, resulting in an unnecessary loss of performance and efficiency. In this work, we propose an approach that takes real-world workload dependencies into account and generates workload-specific aging-aware standard cell libraries, allowing for accurate analysis of aging-induced degradations. We employ machine learning techniques to overcome infeasible simulation times for individual transistor aging while sustaining high prediction accuracy. We also demonstrate scalability to previously unknown workloads and discuss multiple approaches to estimate the machine learning accuracy by employing coverage metrics. In our evaluation, we achieve predictions of workload-dependent aging-aware standard cells with an average accuracy (R2 score) of 95.28%. Using predicted cell libraries in static timing analysis, timing guardbands for multiple circuits are reported with an error of less than 0.1% on average. We demonstrate that timing guardband requirements can be reduced by up to 30% when considering specific workloads over worst-case estimations as performed in state-of-the-art tool flows. Even for unknown workloads of different circuits, accurate prediction with relative errors below 1% can be achieved.