Review of Analog Neuron Devices for Hardware-based Spiking Neural Networks

Abstract

To process data operations more efficiently in deep neural networks (DNNs), studies on spiking neural networks (SNNs) have been conducted. In the reported literature, CMOS neuron circuits that mimic the biological behavior of an integrate-and-fire function of neurons have been mainly studied. Because conventional neuronal circuits need to be improved in terms of area and energy consumption, neuron devices with memory functions such as resistive random access memory (RRAM), phase-change random access memory (PCRAM), magnetic random access memory (MRAM), floating body FETs, and ferroelectric FETs have been emerged to replace a membrane capacitor and trigger device in the conventional neuron circuits. In this review article, neuron devices that can increase the integration density of conventional neuronal circuits and reduce power consumption are reviewed. These devices are expected to play an important role in future neuromorphic systems.