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

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-Unear 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-Unear 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 (>100n]/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.

Publication Date

5-11-2017

Publication Title

Proceedings of the 2017 Design, Automation and Test in Europe, DATE 2017

Number of Pages

350-355

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.23919/DATE.2017.7927015

Socpus ID

85020169317 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85020169317

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