| Autor: |
Mengyuan Duan, Jiesong Liu, Zhengjie Li, Xiaoyong Jia, Guanghong Yang, Weifeng Zhang, Caihong Jia |
| Zdroj: |
Journal of Materials Chemistry C; 5/28/2024, Vol. 12 Issue 20, p7377-7385, 9p |
| Abstrakt: |
Organic small molecule memristors show great potential in the application of low-energy neuromorphic computing such as artificial synapses. In this study, based on the small molecule 1,2-diphenylacetylene (DPA), various biological synaptic functions have been imitated with subfemtojoule energy consumption (B1.2 fJ), multilevel conductance states and highly linear conductance updates. Based on spike-ratedependent plasticity (SRDP) and Bienenstock-Cooper-Munro (BCM) learning rules, the image edge detection has been simulated, which is helpful for real-time image processing. An accuracy rate of 94.7% is obtained when performing the classification task on the fashion-MNIST dataset, demonstrating high accuracy and low-energy consumption in brain-like pattern recognition for neuromorphic computing. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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