Gesture recognition with Brownian reservoir computing using geometrically confined skyrmion dynamics

TL;DR

This paper demonstrates a novel skyrmion-based reservoir computing system that recognizes hand gestures in real-time by leveraging the intrinsic dynamics of magnetic skyrmions, offering a low-power, scalable alternative to traditional neural networks.

Contribution

It introduces a time-multiplexed skyrmion reservoir computing approach that matches real-world temporal patterns without complex data rescaling, enabling efficient, real-time gesture recognition.

Findings

Achieved real-time gesture recognition with skyrmion reservoir

Demonstrated competitive performance with energy-efficient hardware

Enabled seamless sensor data integration without temporal conversion

Abstract

Physical reservoir computing (RC) is a beyond von-Neumann computing paradigm that harnesses the dynamical properties of a complex physical system (reservoir) to process information efficiently in tasks such as pattern recognition. This hardware-centered approach drastically reduces training efforts and holds potential for significantly reduced energy consumption operation. Magnetic skyrmions, topological, particle-like spin textures, are considered highly promising candidates for reservoir computing systems due to their non-linear interactions and established mechanisms for low power manipulation combined with thermally excited dynamics. So far spin-based reservoir computing has been used for static detection or has been based on intrinsic magnetization dynamics timescales, that require cumbersome rescaling of typically slower real-world data. Here we harness the power of time-multiplexed skyrmion RC by adjusting the intrinsic timescales of the reservoir to the timescales of real-world temporal patterns: we recognize hand gestures recorded with range-doppler radar on a millisecond timescale and feed the data as a time-dependent voltage excitation directly into our device. We observe the temporal evolution of the skyrmion trajectory using read-out at just one position of the reservoir, which allows for scaling onto the nanometer scale. We find that our hardware solution exhibits competitive performance compared with a state-of-the-art energy-intensive software-based neural network. The key advantage of our hardware approach lies in its capacity to seamlessly integrate data from sensors without the need for temporal conversion, thanks to the time-dependent input and tunable intrinsic timescales of the skyrmion dynamics in the reservoir. This feature enables real-time feeding of the reservoir, opening a multitude of applications.