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.
aElliot.he@knights.ucf.edu
bDfan@ufc.edu
ABSTRACT
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.
