Abstract:
Random forest has been successfully applied in various fields due to its advantages of fast training speed, difficult over fitting and easy realization. In order to solve the problem that the power consumption test of different memory unit sizes, voltages and temperatures is needed in the post simulation stage of chip design, and the test time is very long, a power prediction method based on the combination of Sparrow Search Algorithm (SSA) and Random Forest (RF) is proposed. Firstly, the unit library after 14 nm SRAM is characterized to find out the appropriate feature variables and obtain the feature data to build the training test set. Secondly, the characteristic variables are analyzed by the characteristic importance, and sorted according to the characteristic importance. Finally, the random forest model is used for regression prediction, and the sparrow search algorithm is introduced to find the model parameters with the smallest root mean square error. Compared with linear regression model, support vector regression model and other models, SSA-RF has higher convergence accuracy, faster training speed. The R
2 value of SSA-RF model is about 0.97. In addition, in the case of less data, the R
2 value can also reach about 0.95. A better prediction model is constructed, which provides a feasible scheme for reducing the power consumption test time, and can leave more time for designers to optimize the circuit.