Pattern recognition with neuromorphic computing using magnetic-field induced dynamics of skyrmions

TL;DR

This paper demonstrates experimentally that skyrmion dynamics can be used for neuromorphic computing, achieving high accuracy in digit and waveform recognition with potential for energy-efficient devices.

Contribution

It introduces a simple skyrmion-based neuromorphic device and shows its effectiveness in pattern recognition tasks, highlighting the role of skyrmion properties in nonlinear mapping.

Findings

Achieved 94.7% accuracy in handwritten digit recognition.

Established a positive correlation between skyrmion count and recognition accuracy.

Demonstrated the potential for energy-efficient neuromorphic computing using skyrmions.

Abstract

Nonlinear phenomena in physical systems can be used for brain-inspired computing with low energy consumption. Response from the dynamics of a topological spin structure called skyrmion is one of the candidates for such a neuromorphic computing. However, its ability has not been well explored experimentally. Here, we experimentally demonstrate neuromorphic computing using nonlinear response originating from magnetic-field induced dynamics of skyrmions. We designed a simple-structured skyrmion-based neuromorphic device and succeeded in handwritten digit recognition with the accuracy as large as 94.7 % and waveform recognition. Notably, there exists a positive correlation between the recognition accuracy and the number of skyrmions in the devices. The large degree of freedoms of skyrmion systems, such as the position and the size, originate the more complex nonlinear mapping and the larger output dimension, and thus high accuracy. Our results provide a guideline for developing energy-saving and high-performance skyrmion neuromorphic computing devices.