Brain-inspired computing with memristors: Challenges in devices, circuits, and systems
Abstract
This article provides a review of current development and challenges in brain-inspired computing with memristors. We review the mechanisms of various memristive devices that can mimic synaptic and neuronal functionalities and survey the progress of memristive spiking and artificial neural networks. Different architectures are compared, including spiking neural networks, fully connected artificial neural networks, convolutional neural networks, and Hopfield recurrent neural networks. Challenges and strategies for nanoelectronic brain-inspired computing systems, including device variations, training, and testing algorithms, are also discussed.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Jan 16, 2020
- Source ID
- 10.1063/1.5124027
Entities
People
- Jiadi Zhu
- Jianhua Joshua Yang
- Linlin Shen
- Menglin Cui
- Mingyi Rao
- Ru Huang
- Wenhao Song
- Xumeng Zhang
- Yang Zhang
- Ye Zhuo
- Yuchao Yang
- Zhongrui Wang
Organizations
- Air Force Research Laboratory
- China Postdoctoral Science Foundation
- Institute of Microelectronics
- National Natural Science Foundation of China
- Shenzhen University
- University of Chinese Academy of Sciences
- University of Massachusetts
- University of Nottingham