Mixed-mapping optimization strategy for deploying convolutional network based on memristor arrays

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. Because of the dual memristor mapping way, it still needs a lot of hardware resources mapping the neural network model after being compressed. For this problem, the method to deploy convolutional network that by using single memristor and dual memristor simultaneously in a way of mixed mapping is a good solution, which can reduce resources. However, there is a contingency in manual setting, the accuracy of the mapped network model is uncontrollable. To solve this new problem, a resource-constrained particle swarm algorithm is proposed to distinguish the importance of convolutional network parameters, and optimize the mixed-mapping deployment. In order to get a better accuracy, the parameters which mapped on the same word line of memristor array are used as a fine granularity search unit. To ensure reasonableness of the solutions, the accuracy of network 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.