Journal
NATURE COMMUNICATIONS
Volume 11, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41467-020-17849-3
Keywords
-
Categories
Funding
- National Research Foundation of Korea (NRF) - Korean government (MSIP) [2020R1A4A2002806, 2019M3F3A1A01074451, 2018R1A2A2A05020475, 2016M3A7B4910426]
- Institute for Information & Communication Technology Planning & Evaluation (IITP), Republic of Korea [2019M3F3A1A01074451] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available