Artificial van der Waals hybrid synapse and its application to acoustic pattern recognition

Brain-inspired parallel computing, which is typically performed using a hardware neural-network platform consisting of numerous artificial synapses, is a promising technology for effectively handling large amounts of informational data. However, the reported nonlinear and asymmetric conductance-upda...

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Bibliographic Details
Published in:Nature communications 2020-08, Vol.11 (1), p.3936-3936, Article 3936
Main Authors: Seo, Seunghwan, Kang, Beom-Seok, Lee, Je-Jun, Ryu, Hyo-Jun, Kim, Sungjun, Kim, Hyeongjun, Oh, Seyong, Shim, Jaewoo, Heo, Keun, Oh, Saeroonter, Park, Jin-Hong
Format: Article
Language:English
Subjects:
140/133
140/146
142/126
147/137
147/3
639/301/1005/1007
639/925/927/1007
Acoustics
Computer programs
Computer simulation
Conductance
Energy efficiency
Feasibility
Hardware
Humanities and Social Sciences
Inference
Molybdenum
Molybdenum disulfide
multidisciplinary
Neural networks
Pattern recognition
Resistance
Science
Science (multidisciplinary)
Software
Synapses
Training
Tungsten
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Summary:Brain-inspired parallel computing, which is typically performed using a hardware neural-network platform consisting of numerous artificial synapses, is a promising technology for effectively handling large amounts of informational data. However, the reported nonlinear and asymmetric conductance-update characteristics of artificial synapses prevent a hardware neural-network from delivering the same high-level training and inference accuracies as those delivered by a software neural-network. Here, we developed an artificial van-der-Waals hybrid synapse that features linear and symmetric conductance-update characteristics. Tungsten diselenide and molybdenum disulfide channels were used selectively to potentiate and depress conductance. Subsequently, via training and inference simulation, we demonstrated the feasibility of our hybrid synapse toward a hardware neural-network and also delivered high recognition rates that were comparable to those delivered using a software neural-network. This simulation involving the use of acoustic patterns was performed with a neural network that was theoretically formed with the characteristics of the hybrid synapses. Designing high-performance and energy efficient neural network hardware remains a challenge. Here, the authors develop a van der Waals hybrid synaptic device that features linear and symmetric conductance-update characteristics and demonstrate the feasibility for hardware neural network performing acoustic pattern recognition.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-020-17849-3