Pattern recognition with neuromorphic computing using magnetic-field induced dynamics of skyrmions (2204.13260v1)

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.