Journal
NATURE COMMUNICATIONS
Volume 9, Issue -, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41467-018-04484-2
Keywords
-
Categories
Funding
- Air Force Research Laboratory (AFRL) [FA8750-15-2-0044]
- Intelligence Advanced Research Projects Activity (IARPA) [2014-14080800008]
- Research Experience for Undergraduates (REU) supplement grant from NSF [ECCS-1253073]
- Chinese Scholarship Council (CSC) [201606160074]
- NSF
- Center for Nanoscale Systems (CNS), NSF National Nanotechnology Infrastructure Network (NNIN) at Harvard University [ECS-0335765]
Ask authors/readers for more resources
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 foundrymade 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available