TY - JOUR
T1 - An ultra-compact leaky integrate-and-fire neuron with long and tunable time constant utilizing pseudo resistors for spiking neural networks
AU - Chen, Xiangyu
AU - Yajima, Takeaki
AU - Inoue, Isao H.
AU - Iizuka, Tetsuya
N1 - Funding Information:
This work was supported by JST, CREST Grant No. JPMJCR19K2, Japan.
Publisher Copyright:
© 2022 The Author(s). Published on behalf of The Japan Society of Applied Physics by IOP Publishing Ltd.
PY - 2022
Y1 - 2022
N2 - 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 μm2. The fabricated LIF neuron has a power consumption of 6 μW 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.
AB - 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 μm2. The fabricated LIF neuron has a power consumption of 6 μW 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.
UR - http://www.scopus.com/inward/record.url?scp=85125870284&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85125870284&partnerID=8YFLogxK
U2 - 10.35848/1347-4065/ac43e4
DO - 10.35848/1347-4065/ac43e4
M3 - Article
AN - SCOPUS:85125870284
VL - 61
JO - Japanese Journal of Applied Physics
JF - Japanese Journal of Applied Physics
SN - 0021-4922
M1 - SC1051
ER -