Artificial Neural Networks Based on Memristive Devices: From Device to System

• Published in: Advanced intelligent systems Vol. 2; no. 12
• Main Authors: Huang, He-Ming, Wang, Zhe, Wang, Tong, Xiao, Yu, Guo, Xin
• Format: Journal Article
• Language: English
• Published: Weinheim John Wiley & Sons, Inc 01.12.2020
• Subjects: Algorithms; Arrays; Artificial neural networks; Brain; CMOS; Computers; Connectivity; Energy consumption; in situ/ex situ training; Memory; Memory devices; memristive devices; Natural language processing; Neural networks; neuron; Neurons; Optimization; Synapses; Transistors
• ISSN: 2640-4567; 2640-4567
• DOI: 10.1002/aisy.202000149

Memristive devices are essential for artificial neural networks (ANNs) due to their similarity to biological synapses and neurons in structure, dynamics, and electrical behaviors. By building a crossbar array, memristive devices can be used to conduct in‐memory computing efficiently. Herein, approaches to realize memristive neural networks (memNNs) from the device level to the system level are introduced with state‐of‐art experimental demonstrations. First, algorithm fundamentals for networks and device fundamentals for synapses and neurons are briefly given to provide guidance for developing ANNs based on memristive devices; second, recent advances in memristive synapses are discussed on the device level, including the optimization of device, the emulation of biological functions and the array integration; third, artificial neurons based on complement metal‐oxide‐semiconductor (CMOS) transistors and memristive devices are described; then, systemic demonstrations and latest developments of memNNs are elaborated; finally, summary and perspective on memristive devices and memNNs are presented. Artificial neural networks based on memristive devices are intensively developed in recent years to accelerate ANNs or demonstrate brain‐inspired computing. State‐of‐art artificial synapses and neurons based on memristive devices are reviewed, and the approaches to realize memristive neural networks are elaborated herein.