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Progress and Benchmark of Spiking Neuron Devices and Circuits

Fu-Xiang Liang, I-Ting Wang,
Published: 3 June 2021
Advanced Intelligent Systems ; doi:10.1002/aisy.202100007

Abstract: The sustainability of ever more sophisticated artificial intelligence relies on the continual development of highly energy-efficient and compact computing hardware that mimics the biological neural networks. Recently, the neural firing properties have been widely explored in various spiking neuron devices, which could emerge as the fundamental building blocks of future neuromorphic/in-memory computing hardware. By leveraging the intrinsic device characteristics, the device-based spiking neuron has the potential advantage of a compact circuit area for implementing neural networks with high density and high parallelism. However, a comprehensive benchmark that considers not only the device but also the peripheral circuit necessary for realizing complete neural functions is still lacking. Herein, the recent progress of emerging spiking neuron devices and circuits is reviewed. By implementing peripheral analog circuits for supporting various spiking neuron devices in the in-memory computing architecture, the advantages and challenges in area and energy efficiency are discussed by benchmarking various technologies. A small or even no membrane capacitor, a self-reset property, and a high spiking frequency are highly desirable.
Keywords: in-memory computing / neuromorphic computing / spiking neurons

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