TY - JOUR
T1 - Design of a 180 nm CMOS Neuron Circuit with Soft-Reset and Underflow Allowing for Loss-Less Hardware Spiking Neural Networks
AU - Kim, Jaesung
AU - Kim, Jung Nam
AU - Kim, Yoon
AU - Hwang, Sungmin
AU - Koo, Minsuk
N1 - Publisher Copyright:
© 2023 The Authors. Advanced Intelligent Systems published by Wiley-VCH GmbH.
PY - 2024/1
Y1 - 2024/1
N2 - Spiking neural networks (SNNs) have been researched as an alternative to reduce the gap with the human brain in terms of energy efficiency, due to their inherent spare event-driven characteristics from a hardware implementation perspective. However, they still face significant challenges in learning, compared to artificial neural networks (ANNs). Recently, several algorithms have been developed to narrow the performance gap between SNNs and ANNs, including features in spiking neurons that can reduce information loss in the membrane potential. Inspired by these advancements, the current study designs and measures a neuron circuit using 180 nm complementary metal-oxide-semiconductor (CMOS) technology to address this information loss. The proposed circuit successfully implements these features, and their performance is validated through simulation based on the measured data.
AB - Spiking neural networks (SNNs) have been researched as an alternative to reduce the gap with the human brain in terms of energy efficiency, due to their inherent spare event-driven characteristics from a hardware implementation perspective. However, they still face significant challenges in learning, compared to artificial neural networks (ANNs). Recently, several algorithms have been developed to narrow the performance gap between SNNs and ANNs, including features in spiking neurons that can reduce information loss in the membrane potential. Inspired by these advancements, the current study designs and measures a neuron circuit using 180 nm complementary metal-oxide-semiconductor (CMOS) technology to address this information loss. The proposed circuit successfully implements these features, and their performance is validated through simulation based on the measured data.
KW - CMOS integrate-and-fire neuron circuits
KW - overthreshold potential retaining
KW - spiking neural networks
KW - underflow allowing
UR - http://www.scopus.com/inward/record.url?scp=85177474700&partnerID=8YFLogxK
U2 - 10.1002/aisy.202300460
DO - 10.1002/aisy.202300460
M3 - Article
AN - SCOPUS:85177474700
SN - 2640-4567
VL - 6
JO - Advanced Intelligent Systems
JF - Advanced Intelligent Systems
IS - 1
M1 - 2300460
ER -