Abstract: With the rapid development of artificial intelligence and the continuous emergence of new operators and hardware,the development and maintenance of operator libraries face enormous challenges.Relying solely on manual optimization can no longer meet the needs of improving AI model performance.Ansor is an operator automatic scheduling technique based on TVM,which can search for the best scheduling schemes for different backend deep learning models or operators,generate high-performance code without the need for users to manually define templates.However,the huge search space results in low search efficiency.Therefore,two optimization schemes are proposed.One is to select the optimal performance sketch based on Reinforcement lear-ning algorithm,and the other is to predict mutation rules based on machine learning models.Two optimization schemes aim to reduce the search time for the optimal scheduling scheme and quickly generate high-performance operators.To evaluate the effectiveness of the optimization plan,three models such as Resnet-50 and three operators such as conv2d are tested and evaluated.The results show that the optimized Ansor can generate target programs with the same or even better performance as before in only 70%～75% search time.Moreover,under the optimal iteration number,the inference speed of the target program can be improved by up to 5%.

Key words: Auto schedule, TVM complier, Optimizing search speed, Machine learning, Reinforcement learning, Deep learning model