AI Bibliography
AI Bibliography |
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Li, C., Belkin, D., Li, Y., Yan, P., Hu, M., & Ge, N., et al.. (2018). Efficient and self-adaptive in-situ learning in multilayer memristor neural networks. Nature communications, 9(1), 1–8. |
| Resource type: Journal Article BibTeX citation key: Li2018a View all bibliographic details  |
Categories: Artificial Intelligence, Computer Science, Data Sciences, Engineering, General, Military Science Subcategories: Autonomous systems, Big data, Cloud computing, Cyber, Drones, Edge AI, Internet of things, JADC2, Machine learning, Military research, Mosaic warfare, Networked forces, Neural nets Creators: Belkin, Ge, Hu, Jiang, Li, Li, Lin, Montgomery, others, Wang, Yan Publisher: Collection: Nature communications |
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Memristors with tunable resistance states are emerging building blocks of artificial neural networks. However, in situ learning on a large-scale multiple-layer memristor network has yet to be demonstrated because of challenges in device property engineering and circuit integration. Here we monolithically integrate hafnium oxide-based memristors with a foundry-made transistor array into a multiple-layer neural network. We experimentally demonstrate in situ learning capability and achieve competitive classification accuracy on a standard machine learning dataset, which further confirms that the training algorithm allows the network to adapt to hardware imperfections. Our simulation using the experimental parameters suggests that a larger network would further increase the classification accuracy. The memristor neural network is a promising hardware platform for artificial intelligence with high speed-energy efficiency.
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