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Using Machine Learning To Automate Debug Of Simulation Regression Results

How verification engineers can more efficiently analyze, bin, triage, probe, and discover the root causes of regression failures.

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. Machine Vision Measurement

Using Machine Learning To Automate Debug Of Simulation Regression Results

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 VerdiĀ® 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.

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Using Machine Learning To Automate Debug Of Simulation Regression Results

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