Hybrid ML for Lightweight Pre-Route Delay Estimation in Open-Source IC Design

Abstract:Static Timing Analysis (STA) is a critical step in the design flow of digital integrated circuits, however, obtaining accurate delay estimations can represent a challenge when limited information regarding physical design is available. In response, this work presents a hybrid and light-weight machine learning (ML) based approach that combines a decision tree with linear regression to improve pre-routing delay estimations generated by the open-source RTL-to-GDSII tool OpenLane. The proposed model achieves an 80\% reduction in error compared to OpenLane's estimates, demonstrates a 71\% improvement even without utilizing OpenLane-specific parameters. Overall, this method offers an alternative to traditional delay propagation techniques and more complex machine learning models that is not only accurate, but is also over 300 times smaller, 2 times faster and offers a higher explainability.

Machine Learning (cs.LG)

arXiv:2608.17914 [cs.LG]

(or arXiv:2608.17914v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.17914 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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