Design of Memristor-Based Binarized Multi-layer Neural Network with High Robustness
Author:
Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-8132-8_19
Reference22 articles.
1. Yakopcic, C., Hasan, R., Taha, T.: Memristor based neuromorphic circuit for ex-situ training of multi-layer neural network algorithms. In: International Joint Conference on Neural Networks, Killarney, Ireland, pp. 1–7. IEEE (2015)
2. LNCS;G Tanaka,2017
3. Li, C., Belkin, D., Li, Y., et al.: Efficient and self-adaptive in-situ learning in multilayer memristor neural networks. Nature Commun. 9(1), 2385 (2018)
4. Yao, P., Wu, H., Gao, B., et al.: Fully hardware-implemented memristor convolutional neural network. Nature 577(7792), 641–646 (2020)
5. Cao, Z., Sun, B., Zhou, G., et al.: Memristor-based neural networks: a bridge from device to artificial intelligence. Nanoscale Horizons 8(6), 716–745 (2023)
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