Voltage-Driven Adaptive Spintronic Neuron for Energy-Efficient Neuromorphic Computing

• 作者: Bo Wei Cheng Li Huan-Qing Cui Jia-Hao Liu Li Cai Ming-Xu Song Tao Yan Xiao-Kuo Yang Ya-Bo Chen
• 来源期刊: 中国物理快报（英文版）
• 年，卷（期）: 2020,（7）
• 所属期刊栏目: CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY
• 页码范围: 125-129
• 页数: 5页
• 语种: 英文
• DOI: 10.1088/0256-307X/37/7/078501

摘要

A spintronics neuron device based on voltage-induced strain is proposed.The stochastic switching behavior,which can mimic the firing behavior of neurons,is obtained by using two voltage signals to control the in-plane magnetization of a free layer of magneto-tunneling junction.One voltage signal is used as the input,and the other voltage signal can be used to tune the activation function (Sigmoid-like) of spin neurons.Therefore,this voltage-driven tunable spin neuron does not necessarily use energy-inefficient Oersted fields and spin-polarized current.Moreover,a voltage-control reading operation is presented,which can achieve the transition of activation function from Sigmoid-like to ReLU-like.A three-layer artificial neural network based on the voltage-driven spin neurons is constructed to recognize the handwritten digits from the MNIST dataset.For the MNIST handwritten dataset,the design achieves 97.75％ recognition accuracy.The present results indicate that the voltage-driven adaptive spintronic neuron has the potential to realize energy-efficient well-adapted neuromorphic computing.