Improved integrate-and-fire neuron models for inference acceleration of spiking neural networks
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Improved integrate-and-fire neuron models for inference acceleration of spiking neural networks Yongcheng Zhou1 · Anguo Zhang2,3 Accepted: 9 October 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract We study the effects of different bio-synaptic membrane potential mechanisms on the inference speed of both spiking feedforward 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. Keywords Spiking neural network · Inference acceleration · Neural plasticity
1 Introduction The development of biologically inspired artificial intelligence algorithms has been an increasingly attractive topic in recent decades. Examples of these include particle swarm optimization (PSO) [12], which originates from the predation behavior of flocks; the ant colony algorithm, which learns from the behaviors of ants finding paths during food searching; the genetic algorithm (GA), which simulates the natural evolution of Darwin’s biological evolution theory and the genetic mechanism of the evolutionary process; and artificial neural networks (ANNs), which refers to the connective structures of animal neural systems and how information is transmitted and processed.
Anguo Zhang
[email protected] 1
Research Institute of Ruijie, Ruijie Networks Co., Ltd., Fuzhou, 350002, China
2
College of Physics and Information Engineering, Fuzhou University, Fuzhou, 350108, China
3
Key Laboratory of Medical Instrumentation and Pharmaceutical Technology of Fujian Province, Fuzhou, 350116, China
Of these algorithms, ANNs have been considered to be the most promising ones when it comes to the realization of “true” artificial intelligence. They have also been widely applied in various applications such as face recognition, object detection, vehicle automation, data prediction, and so on. Currently, almost all of these mature engineering applications have been developed based on second-generation ANN models (also called “ratebased neural networks”), such as traditional BP networks, convolutional neural networks (CNNs), and long-short term memory (LSTM). However, although the above-mentioned ANNs are historically thought to be brain-inspired, there are fu
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