NEURAL NETWORK INFERENCE STRUCTURE OPTIMIZATION METHOD AND DEVICE

摘要

A neural network inference structure optimization method, comprising: when an Mth network layer and a (M+2)th network layer of a neural network inference structure are both normalization layers, the (M+1)th network layer is a convolutional layer or a fully connected layer, and an output of the (M+1)th network layer is only connected to the (M+2)th network layer (501), invoking a first preset algorithm to process the (M+1)th network layer, so as to merge the (M+2)th network layer into the (M+1)th network layer to obtain a first optimized network layer of the (M+1)th network layer (502); and invoking a second preset algorithm to process the first optimized network layer of the (M+1)th network layer, so as to merge the Mth network layer into the first optimized network layer of the (M+1)th network layer (503). The present invention can reduce the calculation amount and processing delay in neural network inference to the greatest extent, achieving the purpose of improving the inference efficiency of a neural network model.