CIRM-SNN: Certainty Interval Reset Mechanism Spiking Neuron for Enabling High Accuracy Spiking Neural Network

Spiking neural network (SNN) based on sparse trigger and event-driven information processing has the advantages of ultra-low power consumption and hardware friendliness. As a new generation of neural networks, SNN is widely concerned. At present, the most effective way to realize deep SNN is through artificial neural network (ANN) conversion. Compared with the original ANN, the converted SNN suffers from performance loss. This paper adjusts the spike firing rate of spiking neurons to minimize the performance loss of SNN in the conversion process. We map the ANN weights to the corresponding SNN after continuous normalization, which ensures that the spike firing rate of the neuron is in the normal range. We propose a certainty interval reset mechanism (CIRM), which effectively reduces the loss of membrane potential and avoids the problem of neuronal over-activation. In the experiment, we added a modulation factor to the CIRM to further adjust the spike firing rate of neurons. The accuracy of the converted SNN on CIFAR-10 is 1.026% higher than that of the original ANN. The algorithm not only achieves the lossless conversion of ANN, but also reduces the network energy consumption. Our algorithm also effectively improves the accuracy of SNN (VGG-15) on CIFAR-100 and decreases the network delay. The work of this paper is of great significance for developing high-precision depth SNN.