Gesture recognition with Brownian reservoir computing using geometrically confined skyrmion dynamics.

ABSTRACT: Physical reservoir computing leverages the dynamical properties of complex physical systems to process information efficiently, significantly reducing training efforts and energy consumption. Magnetic skyrmions, topological spin textures, are promising candidates for reservoir computing systems due to their enhanced stability, non-linear interactions and low-power manipulation. Traditional spin-based reservoir computing has been limited to quasi-static detection or real-world data must be rescaled to the intrinsic timescale of the reservoir. We address this challenge by time-multiplexed skyrmion reservoir computing, that allows for aligning the reservoir's intrinsic timescales to real-world temporal patterns. Using millisecond-scale hand gestures recorded with Range-Doppler radar, we feed

SUBMITTER: Beneke G 

PROVIDER: S-EPMC11405713 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature