@inproceedings{0a1bcb249c1a49b5bd2bc39667797f68,
title = "Classification Performances due to Asymmetric Nonlinear Weight Updates in Analog Artificial Synapse-Based Hardware Neural Networks",
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.",
keywords = "Artificial synapse, Memristor, Neural network, Neuromorphic system",
author = "Yeon Pyo and Sahn Nahm and Jichai Jeong",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 10th International Winter Conference on Brain-Computer Interface, BCI 2022 ; Conference date: 21-02-2022 Through 23-02-2022",
year = "2022",
doi = "10.1109/BCI53720.2022.9734968",
language = "English",
series = "International Winter Conference on Brain-Computer Interface, BCI",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "10th International Winter Conference on Brain-Computer Interface, BCI 2022",
}