Spintronic Hodgkin-Huxley-Analogue Neuron Implemented with a Single Magnetic Tunnel Junction
Davi R. Rodrigues, Rayan Moukhader, Yanxiang Luo, Bin Fang, Adrien Pontlevy, A. Hamadeh, Zhongming Zeng, Mario Carpentieri, Giovanni Finocchio
Abstract
Spiking neural networks aim to emulate the brain's properties to achieve similar parallelism and high processing power. A caveat of these neural networks is the high computational cost for emulation, while current proposals for analogue implementations are energy inefficient and not scalable. We propose a device based on a single magnetic tunnel junction to perform neuron firing for spiking neural networks without the need for any resetting procedure. We leverage two areas of physics, magnetism and thermal effects, to obtain biorealistic spiking behavior analogous to the Hodgkin-Huxley model of the neuron. The device is also able to emulate the simpler leaky-integrate-and-fire model. Numerical simulations using experimental-based parameters demonstrate firing frequency in the megahertz to gigahertz range under constant input at room temperature. The compactness, scalability, low cost, CMOS compatibility, and power efficiency of magnetic tunnel junctions advocates for their broad use in hardware implementations of spiking neural networks.