Efficient and self-adaptive in-situ learning in multilayer memristor neural networks Academic Article uri icon

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.

altmetric score

  • 5.25

author list (cited authors)

  • Li, C., Belkin, D., Li, Y., Yan, P., Hu, M., Ge, N., ... Xia, Q.

citation count

  • 255

publication date

  • June 2018