A compact skyrmionic leaky–integrate–fire spiking neuron device

Abstract

Neuromorphic computing, which relies on a combination of a large number of neurons massively interconnected by an even larger number of synapses, has been actively studied for its characteristics such as energy efficiency, intelligence, and adaptability. To date, while the development of artificial synapses has shown great progress with the introduction of emerging nanoelectronic devices, e.g., memristive devices, the implementation of artificial neurons, however, depends mostly on semiconductor-based circuits via integrating many transistors, sacrificing energy efficiency and integration density. Here, we present a novel compact neuron device that exploits the current-driven magnetic skyrmion dynamics in a wedge-shaped nanotrack. Under the coaction of the exciting current pulse and the repulsive force exerted by the nanotrack edges, the dynamic behavior of the proposed skyrmionic artificial neuron device is in analogy to the leaky-integrate-fire (LIF) spiking function of a biological neuron. The tunable temporary location of the skyrmion in our artificial neuron behaves like the analog membrane potential of a biological neuron. The neuronal dynamics and the related physical interpretations of the proposed skyrmionic neuron device are carefully investigated via micromagnetic and theoretical methods. Such a compact artificial neuron enables energy-efficient and high-density implementation of neuromorphic computing hardware.

Memristive Synapses and Neurons for Bioinspired Computing

“…Therefore, it is not necessary to add a capacitor to implement the integration function of LIF neurons, at least for some memristive devices, meaning that an LIF neuron can be realized with a single component. [219] In addition to memristive devices, some magnetic devices [226][227][228] were also exploited to realize some bioplausible functions of neurons, which is beyond the scope of this article. The sample shown in this figure is an MIT device based on GaTa 4 Se 8 with threshold switching behaviors.…”

“…Thus, threshold switching observed in many oxides has been exploited to realize bioplausible neurons with a compact structure . In addition to memristive devices, some magnetic devices were also exploited to realize some bioplausible functions of neurons, which is beyond the scope of this article.…”

Prospect of Spin-Orbitronic Devices and Their Applications

“…In parallel to synapses development, an artificial neuron receives and outputs stimulus signals from/to its multiple surrounding artificial synapses and adjusts their synaptic weights synchronously. As to the conventional ANN systems, the role of artificial neurons could even be replaced by setting nonlinear activation functions, whereas the SNN for unsupervised learning requires artificial neurons with leaky integrate-and-fire (LIF) ( Stoliar et al., 2017 ; Chen et al., 2018d ) behaviors that response occurs only at the integrated stimulus above a certain threshold. Electrically controlled spin-orbitronic devices with a clear critical magnetization switching current are therefore capable of mimicking such artificial neurons in principle ( Diep et al., 2014 ; Sengupta et al., 2015b ; Kurenkov et al., 2019 ), but the magnetization states of such nonvolatile neurons may have to be initialized after each fire.…”

Shape-Based Magnetic Domain Wall Drift for an Artificial Spintronic Leaky Integrate-and-Fire Neuron

“…However, the historydependent nature of much synapse and neuron behavior inspire the use of non-volatile devices for increased efficiency. To that end, non-volatile devices such as memristors [7], magnetic skyrmion tracks [8], and three-terminal magnetic tunnel junctions (3T-MTJs) [9], [10] have been used that thoroughly mimic the functionalities of biological synapses. However, replicating the complex integrative and temporal behaviors occurring within a neuron's cell body (soma) has been a greater challenge.…”