The memristor array is expected to meet the requirements of edge intelligence for power consumption, storage density, and computing time. However, it is hard to map huge network models with little memristor arrays. To solve this problem, a method to deploy convolutional network that by using single memristor and dual memristor simultaneously in a way of mixed precision is proposed. In order to avoid the contingency of manual setting, a fine-grained mixed-precision network optimization strategy based on particle swarm algorithm is further proposed, which can search the key parameters. To ensure reasonableness of the solutions, the network performance and the number of memristors are both used in the step of fitness calculation, and in order to speed up the search speed, a mixing ratio constraint is added before this step. In addition, the performance and search complexity are compared with other optimization algorithms. For 4-value memristor, the optimized assignment can get 33% higher precision than the manual setting assignment. This work is expected to provide a friendly and feasible non-von Neumann hardware solution by Edge Intelligence.