UVM Verification Coverage Achievement Acceleration using Gradient Boosting and Hybrid Methods

The article discusses the applicability of machine learning to accelerate the functional verification of microprocessors. This study conducts a comparative analysis of major gradient boosting implementations (XGBoost, Light-GBM, CatBoost), one of the most widely used machine learning methods, in achieving code coverage acceleration through transaction filtering. Based on the results, hybrid models combining the strengths of these implementations are proposed. A comparative evaluation of hybrid methods—blending, stacking, and gradient transfer—is provided, identifying the most efficient approach for maximum coverage achievement with fewer transactions in UVM-based functional verification.