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

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.

Automated summary

In order to process data operations more efficiently in deep neural networks, researchers have been studying spiking neural networks. CMOS neuron circuits that mimic the biological behavior of neurons have been the main focus in the reported literature. However, conventional neuronal circuits need improvement in terms of area and energy consumption, leading to the emergence of neuron devices with memory functions. In this review article, neuron devices that can increase integration density and reduce power consumption in conventional neuronal circuits are discussed, and their potential importance in future neuromorphic systems is emphasized.