A Coupled Spintronics Neuromorphic Approach for High-Performance Reservoir Computing

Abstract

The rapid development in the field of artificial intelligence has increased the demand for neuromorphic computing hardware and its information-processing capability. A spintronics device is a promising candidate for neuromorphic computing hardware and can be used in extreme environments due to its high resistance to radiation. Improving the information-processing capability of neuromorphic computing is an important challenge for implementation. Herein, a novel neuromorphic computing framework using spintronics devices is proposed. This framework is called coupled spintronics reservoir (CSR) computing and exploits the high-dimensional dynamics of coupled spin-torque oscillators as a computational resource. The relationships among various bifurcations of the CSR and its information-processing capabilities through numerical experiments are analyzed and it is found that certain configurations of the CSR boost the information-processing capability of the spintronics reservoir toward or even beyond the standard level of machine learning networks. The effectiveness of our approach is demonstrated through conventional machine learning benchmarks and edge computing in real physical experiments using pneumatic artificial muscle-based wearables, which assist human operations in various environments. This study significantly advances the availability of neuromorphic computing for practical uses.