Improved integrate-and-fire neuron models for inference acceleration of spiking neural networks

Abstract

We study the effects of different bio-synaptic membrane potential mechanisms on the inference speed of both spiking feed-forward neural networks and spiking convolutional neural networks. These mechanisms are inspired by biological neuron phenomena include electronic conduction in neurons and chemical neurotransmitter attenuation between presynaptic and postsynaptic neurons. In the area of spiking neural networks, we model some biological neural membrane potential updating strategies based on integrate-and-fire (I&F) spiking neurons. These include the spiking neuron model with membrane potential decay (MemDec), the spiking neuron model with synaptic input current superposition at spiking time (SynSup), and the spiking neuron model with synaptic input current accumulation (SynAcc). Experiment results show that compared with the general I&F model (one of the most commonly used spiking neuron models), SynSup and SynAcc can effectively improve the spiking inference speed of spiking feed-forward neural networks and spiking convolutional neural networks.