A neuromorphic radial-basis-function net using magnetic bits for time series prediction

• Proposed an innovative MTJ-based radial basis function (RBF) network for time series prediction. • Achieved superior prediction accuracy and significantly faster training times compared to traditional software-based artificial neural networks. • Demonstrated that temperature increases can further reduce prediction errors of MTJ-based RBF network. Magnetic tunnel junctions (MTJs) are considered strong candidates for constructing neuromorphic systems owing to their low power consumption and high integrability. However, research on MTJ-based local approximation network is still lacking. In this work, we propose an MTJ-based radial basis function (RBF) network and numerically investigate its time-series prediction capability. The results demonstrate that the MTJ-based RBF network can enhance its prediction performance by utilizing increased environmental temperatures, achieving performance better than traditional software artificial neural networks.