Investigation and mitigation of Mott neuronal oscillation fluctuation in spiking neural network

Mott devices, featuring low hardware cost and high energy efficiency, have been demonstrated as a key oscillatory element in artificial neurons to enable spiking neural networks (SNNs) such as conversion-based SNNs (CSNNs). However, there will be inevitably non-ideal fluctuation in the oscillation behavior, causing the accuracy degradation of networks. In this paper, we investigate the Mott neuronal oscillation fluctuation (NOF) through experiments and modeling. The results show that the NOF phenomenon conforms to Gaussian distribution and originates from thermal fluctuation induced switching voltage variations. We construct a two-layer CSNN for image recognition tasks to study the NOF effect and propose the activation function boundary (AFB) method to strengthen the stability of the network. The results indicate that AFB can improve the accuracy of CSNN by up to 15.5% by tightening output distribution.