Using Machine Learning to Automate Debug of Simulation Regression Results

White Paper

Using Machine Learning to Automate Debug of Simulation Regression Results

Regression failure debug is usually a manual process wherein verification engineers debug hundreds, if not thousands of failing tests. Machine learning (ML) technologies have enabled an automated debug process that not only accelerates debug but also eliminates errors introduced by manual efforts.

This white paper discusses how verification engineers can more efficiently analyze, bin, triage, probe, and discover the root causes of regression failures. The Regression Debug Automation (RDA) capabilities in Synopsys Verdi® Automated Debug System automatically discover the root causes of regression failures, classify as well as analyze raw regression failures using ML and identify root causes of failures in the design and testbench. RDA automation helps the users find, understand, and fix the bugs much faster than manual processes improving overall debug effort by 2X or more.

  • Understand the challenges associated with manual debug and analysis
  • Gain insight into how ML can assist in the regression testing loop to minimize manual effort
  • Learn about the Verdi RDA technology components that maximize debug efficiency

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