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自旋类脑神经形态计算

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类脑计算旨在模拟和实现大脑的信息处理和学习能力,以解决复杂的计算问题,其关键思想之一是模拟生物神经元和突触行为来实现信息传输、处理和存储.自旋电子学器件的非易失性、高速低功耗、几乎无限的耐用性及固有非线性等特点,使其在类脑计算上已有广泛尝试和出色表现.基于对自旋电子学中的各类磁电阻效应、自旋转移力矩和自旋轨道力矩效应、电压调控磁各向异性效应以及磁化动力学的非线性效应进行介绍和总结,以各类自旋器件在储备池计算、伊辛机、脉冲神经网络以及真随机数生成器上的应用为实例,展望自旋类脑神经形态计算硬件在未来人工智能芯片领域的发展前景与趋势.
Spin-based Brain-like Neuromorphic Computing
Brain-like neuromorphic computing aims to simulate and implement the brain's information processing and learning capa-bilities to address complex computational problems.One of the key ideas is to mimic the behavior of biological neurons and synapses to achieve information transmission,processing,and storage.With their non-volatile nature,high speed,low power consumption,near-infinite durability,and inherent nonlinearity,spintronic devices have been widely explored and shown remarkable performance in neu-romorphic computing.Based on various magnetoresistance effects,spin-transfer and spin-orbit torques,voltage-controlled magnetic ani-sotropy,and nonlinear magnetization dynamics,this review provides an overview of the application of different spintronic devices in res-ervoir computing,Ising machines,spiking neural networks,and true random number generators.These examples are just the tip of the iceberg,but they demonstrate the promising potential of spin-based brain-like neuromorphic computing hardware in artificial intelli-gence chips.

spintronicsneural networkneuromorphic computingbrain-inspired artificial intelligence chip

张帅、陈丽娜、刘荣华

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南京大学物理学院江苏省纳米技术重点实验室,江苏南京 210023

南京邮电大学理学院,江苏南京 210023

自旋电子学 神经网络 神经形态计算 类脑人工智能芯片

2025

四川师范大学学报(自然科学版)
四川师范大学(中国 成都)

四川师范大学学报(自然科学版)

影响因子:0.494
ISSN:1001-8395
年,卷(期):2025.48(2)