Neuromorphic Spintronics

By Julie Grollier, Damien Querlioz, Kerem Y Çamsarı, Karin Everschor-Sitte, Shunsuke Fukami, Mark D. Stiles. Nature Electronics, 2020

896 citations recorded by the source corpus.

Neuromorphic computing uses brain-inspired principles to design circuits that can perform computational tasks with superior power efficiency to conventional computers. Approaches that use traditional electronic devices to create artificial neurons and synapses are, however, currently limited by the energy and area requirements of these components. Spintronic nanodevices, which exploit both the magnetic and electrical properties of electrons, can increase the energy efficiency and decrease the area of these circuits, and magnetic tunnel junctions are of particular interest as neuromorphic computing elements because they are compatible with standard integrated circuits and can support multiple functionalities. Here, we review the development of spintronic devices for neuromorphic computing. We examine how magnetic tunnel junctions can serve as synapses and neurons, and how magnetic textures, such as domain walls and skyrmions, can function as neurons. We also explore spintronics-based implementations of neuromorphic computing tasks, such as pattern recognition in an associative memory, and discuss the challenges that exist in scaling up these systems. This Review Article examines the development of spintronic devices for neuromorphic computing, exploring how magnetic tunnel junctions and magnetic textures can act as artificial neurons and synapses, as well as considering the challenges that exist in scaling up current systems.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/b8a8d9aedc7e2321e7a9a11a821218339ded1487

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Citations in the corpus

Physics for neuromorphic computing (2020, background): “This is problematic at the nanoscale, as fluctuations and drift effects often arise due to the small volume of devices 70 .”
Deep physical neural networks trained with backpropagation (2021, background): “…of constructing computers by directly training physical transformations has ancestry in evolved computing materials 18 , it is today emerging in various fields, including optics 14,15,17,20 , spintronic nano-oscillators 10,37 , nanoelectronic devices 13,19 and small-scale quantum computers 38 .”