MESO Neuron: A Low-Power and Ultrafast Spin Neuron for Neuromorphic Computing

In this letter, a low-power and ultrafast spin neuron for mimicking biological neurons based on magneto-electric spin-orbit (MESO) neurons is presented. First, the physical model of a MESO neuron based on the Landau-Lifshitz-Gilbert (LLG) equation at room temperature is built for investigating the characteristics. By utilizing these characteristics of the MESO device, a current pulse is used to induce the stochastic switching behaviors. We successfully mimic the behavior of the biological neuron with single activation time down to 0.8 ns. Second, using model-derived device parameters, we further simulate a three-layer fully connected neural network using MESO neurons. Using the Mixed National Institute of Standards and Technology database handwritten pattern dataset, our system achieves a recognition accuracy of 98%. In addition, the influence of pulsewidth and amplitude on activation functions of MESO neurons is researched using HSPICE tools. The results show that as pulsewidth and amplitude are increasing, the power consumption and computing time increase while the energy consumption decreases. Specifically, the power consumption performance of a MESO neuron is about 10 mu W and improved approximately three orders of magnitude compared to a 45 nm CMOS neuron.

Automated summary

This letter presents a low-power and ultrafast spin neuron based on magneto-electric spin-orbit (MESO) neurons for mimicking biological neurons. The characteristics of MESO device are utilized to successfully simulate the behavior of biological neurons with a time down to 0.8 ns. A three-layer fully connected neural network using MESO neurons achieves a recognition accuracy of 98% with model-derived device parameters. The influence of pulsewidth and amplitude on the activation functions of MESO neurons is also investigated.