Abstract
Today’s machine learning and artificial neural networks rely heavily on conventional electronic circuits. Progress in machine learning models and algorithms will eventually be limited by issues such as high power dissipation and scaling challenges posed by CMOS, and it is necessary to resolve these through a bottom-up approach. Here, we discuss how spintronic devices can overcome energy efficiency and scalability, and serve as artificial synaptic and neuronal devices using materials with large spin-orbit coupling, as well as magnetic textures such as chiral domain walls and skyrmions. We also explore the how these spintronic devices can mimic the biological brain-inspired neuronal and synaptic behaviours, to develop beyond-CMOS neuromorphic hardware for more efficient computation.
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Lim, G.J., Ang, C.C., Lew, W.S. (2021). Spintronics for Neuromorphic Engineering. In: Lew, W.S., Lim, G.J., Dananjaya, P.A. (eds) Emerging Non-volatile Memory Technologies. Springer, Singapore. https://doi.org/10.1007/978-981-15-6912-8_9
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