Physical reservoir computing and deep neural networks using artificial and natural noncollinear spin textures

The growing demand for artificial intelligence has motivated research into nontraditional physical devices that enable efficient learning in various tasks. This requires the devices to exhibit natural nonlinear dynamics with minimal power consumption. Here we present the application of artificial spin ice (ASI) and an as-grown chiral helimagnet (CHM) as the nonlinear component in physical reservoir computing (RC) and deep neural networks (DNNs). Their complex nonlinear magnetodynamics can be easily characterized by the broadband coplanar waveguide-based ferromagnetic resonance technique, originating from the specifically geometrical frustration effect and intrinsic multiple magnetic interactions competition, respectively. On the basis of the experimentally obtained nonlinear magnetodynamic response curves of these two noncollinear spin textures, we build ASI- and CHM-based physical reservoirs for RC and use the absorption and differential ferromagnetic resonance spectra as the activation function and its derivatives to perform nonlinear transformation of inputs for DNNs. The results demonstrate that physical RC and DNNs can accomplish time-series prediction and image-recognition tasks, respectively, with high accuracy and low power consumption. Our findings provide valuable insights and a promising pathway toward neuromorphic hardware using abundant artificial or natural nontrivial magnetic systems.