Impact of imprecise programming of memristor on building hardware neural network

The application of memristor in building hardware neural network has accepted widespread interests, and may bring novel opportunities to neural computing. However, due to the limitation of programming precision, the conductance of memristor which represents stored information may deviate from theoretical value, and thus bring error to the neural computing results. In this paper, we analyze the impact of imprecise programming on building hardeware neural network through Monte Carlo simulation on feedback layer model. The results show that the fault-tolerance ability of neural network could well adapt to these errors, which further proves the potential of building neural networks using memristors.