Speed Up Functional Coverage Closure of CORDIC Designs Using Machine Learning Models
Mohamed A. Abd El Ghany, Khaled A. Ismail · 2021
Accurate Machine Learning ML models used for speeding up coverage closure are presented in this paper. Different ML models: Artificial Neural Network ANN, Deep Neural Network DNN, Support Vector Regression SVR and Decision Trees DT are trained to constrain the randomization of a Coordinate Rotation Digital Computer CORDIC design input values to hit the planned coverage items. Used ML models are compared in terms of evaluation metrics such as: Mean Squared Error MSE and R2 score. Training time overhead for each model is also considered. Tested ML models demonstrate an improvement of 55% in the number of transactions required to reach complete coverage closure when compared to traditional open-loop randomization method. Comparative analysis shows that DT is the most effective ML model to be incorporated in a CORDIC functional verification environment, due to its low training time overhead and high prediction accuracy.