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.

published proceedings

  • Nat Commun

altmetric score

  • 5.5

author list (cited authors)

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

citation count

  • 358

complete list of authors

  • Li, Can||Belkin, Daniel||Li, Yunning||Yan, Peng||Hu, Miao||Ge, Ning||Jiang, Hao||Montgomery, Eric||Lin, Peng||Wang, Zhongrui||Song, Wenhao||Strachan, John Paul||Barnell, Mark||Wu, Qing||Williams, R Stanley||Yang, J Joshua||Xia, Qiangfei

publication date

  • June 2018