Convolutional networks with short-term memory effects

Abstract

The scale expansion and depth increase of convolutional neural network lead to the increase in the number of devices and chip size, which makes the hardware implementation of neural network more and more difficult. To solve this problem, we design a convolutional neural network with short-term memory to save the number of devices and reduce the complexity of the hardware implementation. Memristor, as a natural nanoscale synapse, is one of the important devices to realize convolutional neural network chip. However, most current memristor-based neural networks use non-volatile memristors. Non-volatile memristors can only store long-term weights because of the stability of resistance, but volatile memristor can store short-term and long-term weights due to its forgetting property. This paper designs a LeNet-5 convolutional neural network by using forgetting memristor bridges composed of volatile memristors. Due to the short-term memory of volatile memristors, 49% of the number of memristors can be effectively saved compared to the neural networks using non-volatile memristors. By writing different weights for the forgetting memristor bridges, a recognition accuracy of about 97% is achieved on the MNIST dataset, and a good recognition accuracy is still achieved at 40,000 images if a recovery signal is given. What is more, in the FASHION-MNIST and ORL datasets, we also achieve 85% and 91% recognition accuracy respectively with the same network.