Abstract
Artificial synapses are fundamental for neuromorphic computing to overcome the bottleneck of the von Neumann system. In particular, a memristor synapse-based neuromorphic system has been known as an optimal device for effectively implementing a hardware neural network. Here, we propose the memristor synapse which shows potentiation and depression process like biological brain mechanisms and investigate the effects of varying the device parameters of nonlinearity and asymmetry on the classification accuracy. We find that the virtual devices with a nonlinearity of less than 10 can be obtained the classification accuracy up to 80%. Our approach demonstrates a practical neuromorphic system based on virtual device on simulation and measured device on experiment and verifies the feasibility of the hardware neural networks.
Original language | English |
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Title of host publication | 10th International Winter Conference on Brain-Computer Interface, BCI 2022 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781665413374 |
DOIs | |
Publication status | Published - 2022 |
Event | 10th International Winter Conference on Brain-Computer Interface, BCI 2022 - Gangwon-do, Korea, Republic of Duration: 2022 Feb 21 → 2022 Feb 23 |
Publication series
Name | International Winter Conference on Brain-Computer Interface, BCI |
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Volume | 2022-February |
ISSN (Print) | 2572-7672 |
Conference
Conference | 10th International Winter Conference on Brain-Computer Interface, BCI 2022 |
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Country/Territory | Korea, Republic of |
City | Gangwon-do |
Period | 22/2/21 → 22/2/23 |
Bibliographical note
Publisher Copyright:© 2022 IEEE.
Keywords
- Artificial synapse
- Memristor
- Neural network
- Neuromorphic system
ASJC Scopus subject areas
- Artificial Intelligence
- Human-Computer Interaction
- Signal Processing