An ultra-compact leaky integrate-and-fire neuron with long and tunable time constant utilizing pseudo resistors for spiking neural networks. (1st May 2022)

• Record Type: Journal Article
• Authors: Chen, Xiangyu; Yajima, Takeaki; Inoue, Isao H.; Iizuka, Tetsuya
• Is Part Of: Japanese journal of applied physics. Volume 61:Number SC(2022)
• Publication Date: 2022-05-01
• Subjects: Ultra-compact -- Leaky integrate-and-fire (LIF) neuron -- Long and tunable time constant -- Pseudo-resistor (PR) -- Spiking neural network (SNN) -- Op-amp; Physics -- Periodicals; 621.05
• DOI: 10.35848/1347-4065/ac43e4
• Languages: English
• ISSNs: 0021-4922

Abstract

Abstract: Spiking neural networks (SNNs) inspired by biological neurons enable a more realistic mimicry of the human brain. To realize SNNs similar to large-scale biological networks, neuron circuits with high area efficiency are essential. In this paper, we propose a compact leaky integrate-and-fire (LIF) neuron circuit with a long and tunable time constant, which consists of a capacitor and two pseudo resistors (PRs). The prototype chip was fabricated with TSMC 65 nm CMOS technology, and it occupies a die area of 1392 μ m 2 . The fabricated LIF neuron has a power consumption of 6 μ W and a leak time constant of up to 1.2 ms (the resistance of PR is up to 600 MΩ). In addition, the time constants are tunable by changing the bias voltage of PRs. Overall, this proposed neuron circuit facilitates the very-large-scale integration of adaptive SNNs, which is crucial for the implementation of bio-scale brain-inspired computing.