Circuit Design of Convolutional Neural Network Based on Memristor Crossbar Arrays

Memristor crossbar array has caused wide attention due to its excellent performance in neuromorphic computing. In this paper, we design a circuit to realize a convolutional neural network (CNN) using memristors and CMOS devices. Firstly, we improve a memristor crossbar array that can store weights and bias accurately. A dot product between two vectors can be calculated after introducing an appropriate encoding scheme. The improved memristor crossbar array is employed for convolution and pooling operations, and a classifier in a CNN. Secondly, we also design a memristive CNN architecture using the improved memristor crossbar array and based on the high fault-tolerance of CNNs to perform a basic CNN algorithm. In the designed architecture, the analog results of convolution operations are sampled and held before a pooling operation rather than using analog digital converters and digital analog converters between convolution and pooling operations in a previous architecture. Experimental results show the designed circuit with the area of 0.8525cm\+2 can achieve a speedup of 1770×compared with a GPU platform. Compared with previous memristor-based architecture with a similar area, our design is 7.7×faster. The average recognition errors performed on the designed circuit are only 0.039% and 0.012% lost than those of software implementation in the cases of a memristor with 6-bit and 8-bit storage capacities, respectively.