A Comparative Study of Local Net Modeling Using Machine Learning

ABSTRACT

Local nets are by default ignored during global routing but can contribute to a high percentage (up to 30%) of total number of nets in the design. Prior work proposed simple models for how local nets are routed and showed benefits such as better congestion analysis post-placement, integration with global routing, and better track assignment. In this work we study local net modeling using machine learning. Our model predicts utilization by local routes inside each global cell. We model this as a regression problem and as reference use local route utilization data from the detailed routing stage using a commercial tool. To solve the problem we identify suitable machine learning algorithms. Within our modeling, we study and rank different features which utilize various layout attributes. We identify the most beneficial features and show our model performs superior to prior work which were based on pin density and Steiner tree models. Our model also performs better for the subset of local nets which are routed in more than one global cell.

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Index Terms

• Computing methodologies

  • Machine learning

    • Learning paradigms

      • Supervised learning

• Hardware

  • Electronic design automation

    • Modeling and parameter extraction

    • Physical design (EDA)

  • Very large scale integration design

    • Design rules