Spiking neurons communicate with other neurons using sparse and binary signals in a human brain, so they can achieve real-time processing of the information with ultra-low power consumption[1,2]. Thereby, spiking neurons are essential elements for building an energy-efficient biomimetic spatiotemporal system. Recently, to emulate the behavior of biological neuron, many researches for memristive device-based neurons with peripheral circuits (i.e., sense-amplifier or reset circuit)[3] and complementary metal-oxide-semiconductor (CMOS) neurons with capacitors have been reported[4]. Most of the reported memristive device-based neurons required a high operation voltage (>1.2 V) for emulating integrate function of a biological neuron. In addition, complementary metal-oxide-semiconductor-based neurons could not achieve high neuronal density due to using a capacitor in emulating integrate function.

In this study, therefore, we propose an electrochemical metallization cell based memristive neuron chip fabricated with 28-nm CMOS process having a low operation voltage (<0.7 V) for emulating integrate function. In addition, since the proposed electrochemical metallization cell based memristive neuron chip does not require a capacitor for emulating integrate function, it can realize a high neuronal density. The memristive neuron chip exhibited a typical integrate-and-fire function; particularly, the frequency of generating a spiking output signal exponentially increased with the input voltage amplitude. Moreover, a spiking neural network was designed using the memristive neuron chip and the software program consisting of a crossbar synaptic memristor array, simplified spike-timing-dependent-plasticity learning rule, and current-to-voltage converters. Using the co-designed spiking neural network with software and hardware, real-time unsupervised learning was realized. Finally, using the trained spiking neural network, a real-time classification for the MNIST hand-written image taken by a live webcam was successfully performed in an inference process.

Acknowledgement

This research was supported by National R&D Program through the National Research Foundation of Korea(NRF) funded by Ministry of Science and ICT(2021M3F3A2A01037733).

Reference

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