Boolean Computation in Single-Transistor Neuron

Bibliographische Detailangaben
Veröffentlicht in:	Advanced materials (Deerfield Beach, Fla.). - 1998. - 36(2024), 49 vom: 08. Dez., Seite e2409040
1. Verfasser:	Li, Hanxi (VerfasserIn)
Weitere Verfasser:	Hu, Jiayang, Zhang, Yishu, Chen, Anzhe, Lin, Li, Chen, Ge, Chen, Yance, Chai, Jian, He, Qian, Wang, Hailiang, Huang, Shiman, Zhou, Jiachao, Xu, Yang, Yu, Bin
Format:	Online-Aufsatz
Sprache:	English
Veröffentlicht:	2024
Zugriff auf das übergeordnete Werk:	Advanced materials (Deerfield Beach, Fla.)
Schlagworte:	Journal Article Research Support, Non-U.S. Gov't artificial intelligence boolean algebra neural network neuromorphic computing neuron model reconfigurable logic Graphite 7782-42-5

Beschreibung
Zusammenfassung:	© 2024 Wiley‐VCH GmbH. Brain neurons exhibit far more sophisticated and powerful information-processing capabilities than the simple integrators commonly modeled in neuromorphic computing. A biological neuron can in fact efficiently perform Boolean algebra, including linear nonseparable operations. Traditional logic circuits require more than a dozen transistors combined as NOT, AND, and OR gates to implement XOR. Lacking biological competency, artificial neural networks require multilayered solutions to exercise XOR operation. Here, it is shown that a single-transistor neuron, harnessing the intrinsic ambipolarity of graphene and ionic filamentary dynamics, can enable in situ reconfigurable multiple Boolean operations from linear separable to linear nonseparable in an ultra-compact design. By leveraging the spatiotemporal integration of inputs, bio-realistic spiking-dependent Boolean computation is fully realized, rivaling the efficiency of a human brain. Furthermore, a soft-XOR-based neural network via algorithm-hardware co-design, showcasing substantial performance improvement, is demonstrated. These results demonstrate how the artificial neuron, in the ultra-compact form of a single transistor, may function as a powerful platform for Boolean operations. These findings are anticipated to be a starting point for implementing more sophisticated computations at the individual transistor neuron level, leading to super-scalable neural networks for resource-efficient brain-inspired information processing
Beschreibung:	Date Completed 05.12.2024 Date Revised 24.02.2026 published: Print-Electronic Citation Status MEDLINE
ISSN:	1521-4095
DOI:	10.1002/adma.202409040