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- W2807750997 abstract "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." @default.
- W2807750997 created "2018-06-21" @default.
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- W2807750997 date "2018-06-19" @default.
- W2807750997 modified "2023-10-15" @default.
- W2807750997 title "Efficient and self-adaptive in-situ learning in multilayer memristor neural networks" @default.
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- W2807750997 doi "https://doi.org/10.1038/s41467-018-04484-2" @default.
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