High-performance convolutional neural network emulation via FPGA-integrated memristive circuitry

Although memristors address the potential data flow and memory access bottlenecks in traditional von Neumann architectures by moving computational operations into the storage elements, thereby improving computational efficiency and reducing energy consumption, the current costs of memristors are relatively high, and they face issues such as low reliability and short lifespan. Therefore, this paper proposes a simulation circuit model based on FPGA, which can provide a low-cost solution for rapid prototyping design and exploration of the design space. The memristor simulation circuit is deployed on the Xilinx ZYNO-7000 FPGA XC7Z020 with a resource utilization rate of less than 1%, and it has been successfully integrated and verified. Furthermore, this paper also constructs a convolutional neural network for MNIST image recognition using a memristor model. With a basis of 512 parameters, it achieves an accuracy of 90.22%.