Spintronic Neuron Using a Magnetic Tunnel Junction for Low-Power Neuromorphic Computing

• Published in: IEEE magnetics letters 2024, Vol.15, p.1-5
• Main Authors: Louis, Steven, Bradley, Hannah, Trevillian, Cody, Slavin, Andrei, Tiberkevich, Vasil
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Biological effects; Biology; Hardware; low-power artificial intelligence; Machine learning; machine learning hardware; magnetic memory; Magnetic tunneling; Magnetization; magnetoresistive random access memory integration; Mathematical models; neuromorphic computing; Neurons; Power consumption; Resistance; spiking neural networks; Spin electronics; Spin valves; spintronic neuron; Spintronics; Torque; Tunnel junctions
• ISSN: 1949-307X; 1949-3088
• DOI: 10.1109/LMAG.2024.3484957

This letter presents a novel spiking artificial neuron design based on a combined spin valve/magnetic tunnel junction (SV/MTJ). Traditional hardware used in artificial intelligence and machine learning faces significant challenges related to high power consumption and scalability. To address these challenges, spintronic neurons, which can mimic biologically inspired neural behaviors, offer a promising solution. We present a model of an SV/MTJ-based neuron that uses technologies that have been successfully integrated with CMOS in commercially available applications. The operational dynamics of the neuron are derived analytically through the Landau-Lifshitz-Gilbert-Slonczewski equation, demonstrating its ability to replicate key spiking characteristics of biological neurons such as response latency and refractive behavior. Simulation results indicate that the proposed neuron design can operate on a timescale of about 1 ns, without any bias current and with power consumption as low as 50 {\mu }W.