Neural network optimization method, electronic device and processor

Abstract

The present invention discloses a neural network optimization method. An operator to be replaced is selected from multiple operators in a network layer according to a predetermined condition, and the operator to be replaced is replaced by multiple equivalent operators according to a calculation function corresponding to the operator to be replaced, wherein the multiple equivalent operators include a target operator. Pre-calculating is performed for a first operator among the multiple equivalent operators, and the calculation result is inputted into the target operator. A second operator is identified according to data change conditions of the multiple equivalent operators, and the second operator is combined with the target operator to complete optimization of a neural network model. The present invention can further perform lossless conversion of the operators in the neural network, further improving calculation performance on the basis of a simplified network structure.