Brain-inspired computing with memristors: Challenges in devices, circuits, and systems

Yang Zhang, Zhongrui Wang, Jiadi Zhu, Yuchao Yang, Mingyi Rao, Wenhao Song, Ye Zhuo, Xumeng Zhang, Menglin Cui, Linlin Shen, Ru Huang, J. Joshua Yang

2020Applied Physics Reviews368 citationsDOI

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.

Topics & Concepts

MemristorComputer sciencePhysical neural networkArtificial neural networkArtificial intelligenceConvolutional neural networkBiological neural networkDeep learningSpiking neural networkComputer architectureNervous system network modelsTypes of artificial neural networksRecurrent neural networkMachine learningElectronic engineeringEngineeringAdvanced Memory and Neural ComputingFerroelectric and Negative Capacitance DevicesNeuroscience and Neural Engineering