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Magnetic skyrmions are nanoscale whirls of magnetism that can be propagated with electrical currents. The repulsion between skyrmions inspires their use for reversible computing based on the elastic billiard ball collisions proposed for conservative logic in 1982. Here we evaluate the logical and physical reversibility of this skyrmion logic paradigm, as well as the limitations that must be addressed before dissipation-free computation can be realized.

Spintronic devices, especially those based on motion of a domain wall (DW) through a ferromagnetic track, have received a significant amount of interest in the field of neuromorphic computing because of their non-volatility and intrinsic current integration capabilities. Many spintronic neurons using this technology have already been proposed, but they also require external circuitry or additional device layers to implement other important neuronal behaviors. Therefore, they result in an increase in fabrication complexity and/or energy consumption. In this work, we discuss three neurons that implement these functions without the use of additional circuitry or material layers.

The domain wall-magnetic tunnel junction (DW-MTJ) is a spintronic device that enables efficient logic circuit design because of its low energy consumption, small size, and non-volatility. Furthermore, the DW-MTJ is one of the few spintronic devices for which a direct cascading mechanism is experimentally demonstrated without any extra buffers; this enables potential design and fabrication of a large-scale DW-MTJ logic system. However, DW-MTJ logic relies on the conversion between electrical signals and magnetic states which is sensitive to process imperfection. Therefore, it is important to analyze the robustness of such DW-MTJ devices to anticipate the system reliability before fabrication. Here we propose a new DW-MTJ model that integrates the impacts of process variation to enable the analysis and optimization of DW-MTJ logic. This will allow circuit and device design that enhances the robustness of DW-MTJ logic and advances the development of energy-efficient spintronic computing systems.

Neuromorphic computing captures the quintessential neural behaviors of the brain and is a promising candidate for the beyond-von Neumann computer architectures, featuring low power consumption and high parallelism. The neuronal lateral inhibition feature, closely associated with the biological receptive eld, is crucial to neuronal competition in the nervous system as well as its neuromorphic hardware counterpart. The domain wall - magnetic tunnel junction (DW-MTJ) neuron is an emerging spintronic arti cial neuron device exhibiting intrinsic lateral inhibition. This work discusses lateral inhibition mechanism of the DW-MTJ neuron and shows by micromagnetic simulation that lateral inhibition is eciently enhanced by the Dzyaloshinskii-Moriya interaction (DMI).

Due to their non-volatility and intrinsic current integration capabilities, spintronic devices that rely on domain wall (DW) motion through a free ferromagnetic track have garnered significant interest in the field of neuromorphic computing. Although a number of such devices have already been proposed, they require the use of external circuitry to implement several important neuronal behaviors. As such, they are likely to result in either a decrease in energy efficiency, an increase in fabrication complexity, or even both. To resolve this issue, we have proposed three individual neurons that are capable of performing these functionalities without the use of any external circuitry. To implement leaking, the first neuron uses a dipolar coupling field, the second uses an anisotropy gradient, and the third uses shape variations of the DW track.

Advances in machine intelligence have sparked interest in hardware accelerators to implement these algorithms, yet embedded electronics have stringent power, area budgets, and speed requirements that may limit nonvolatile memory (NVM) integration. In this context, the development of fast nanomagnetic neural networks using minimal training data is attractive. Here, we extend an inference-only proposal using the intrinsic physics of domain-wall MTJ (DW-MTJ) neurons for online learning to implement fully unsupervised pattern recognition operation, using winner-take-all networks that contain either random or plastic synapses (weights). Meanwhile, a read-out layer trains in a supervised fashion. We find our proposed design can approach state-of-the-art success on the task relative to competing memristive neural network proposals, while eliminating much of the area and energy overhead that would typically be required to build the neuronal layers with CMOS devices.