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

Abstract

Despite being formidable tools in artificial intelligence, artificial neural networks consume substantial energy during their training phase. This study introduces hardware-based artificial neural networks that utilize artificial and natural noncollinear spin textures, significantly reducing energy consumption and enhancing operational efficiency. The authors demonstrate two such spin-texture-based physical reservoirs, which exhibit robust information-processing capabilities in two nonlinear benchmark tests. Additionally, they implement a direct-feedback-alignment algorithm within hardware, further advancing the efficiency of deep neural networks.