A Tunable Magnetic Skyrmion Neuron Cluster for Energy Energy Efficient Artificial Neural Network

Zhezhi Hea and Deliang Fanb
Department of Electrical and Computer Engineering, University of Central Florida, Orlando, Florida.


Artificial neuron is one of the fundamental computing unit in brain-inspired artificial neural network. The standard CMOS based artificial neuron designs to implement non-linear neuron activation function typically consist of large number of transistors, which inevitably causes large area and power consumption. There is a need for novel nanoelectronic device that can intrinsically and efficiently implement such complex non-linear neuron activation function. Magnetic skyrmions are topologically stable chiral spin textures due to Dzyaloshinskii-Moriya interaction in bulk magnets or magnetic thin films. They are promising next-generation information carrier owing to ultra-small size (sub-10nm), high speed ( > 100m/s) with ultra-low depinning current density (MA/cm2) and high defect tolerance compared to conventional magnetic domain wall motion devices. In this work, to the best of our knowledge, we are the first to propose a threshold-tunable artificial neuron based on magnetic skyrmion. Meanwhile, we propose a Skyrmion Neuron Cluster (SNC) to approximate non-linear soft-limiting neuron activation functions, such as the most popular sigmoid function. The device to system simulation indicates that our proposed SNC leads to 98.74% recognition accuracy in deep learning Convolutional Neural Network (CNN) with MNIST handwritten digits dataset. Moreover, the energy consumption of our proposed SNC is only 3.1 fJ/step, which is more than two orders lower than that of CMOS counterpart.

Full Text (PDF)