Voltage-Driven Adaptive Spintronic Neuron for Energy-Efficient Neuromorphic Computing
Ya-Bo Chen1, Xiao-Kuo Yang1*, Tao Yan2, Bo Wei1, Huan-Qing Cui1, Cheng Li3, Jia-Hao Liu3, Ming-Xu Song1, and Li Cai1
1Department of Basic Sciences, Air Force Engineering University, Xi'an 710051, China 2School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150006, China 3College of Computer, National University of Defense Technology, Changsha 410005, China
Abstract：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.
2020, Vol. 37 
