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

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.