A memristor-based supervised neural network algorithm and its circuit design

Abstract

This paper introduces a way to realize the supervised neural network algorithms based on memristive characteristics on Field Programmable Gate Array(FPGA) for the problem that how to take the memristors into artificial neural networks and hardware implement. This design uses memristors module as weight store module in neural network to build supervised learning with error feedback mechanism. The memristive neural networks are used in pattern recognition and their hardware resource and processing speed are optimized. Experiment results show that the performance of pattern recognition is quite good. Further, the hardware resource occupancies and training time are 11 773 logic elements(LEs) and 0.33 ms on Cyclone II：EP2C70F896I8, respectively, and the test time of images is 10 μs, which gives a useful reference for combination of memristors and neural networks.

Keywords

• memristor
• supervised neural network
• Field Programmable Gate Array
• pattern recognition
• resource occupancies