Physical reservoir computing and deep neural networks using artificial and natural noncollinear spin textures
Haotian Li, Liyuan Li, Rongxin Xiang, Wei Liu, Chunjie Yan, Zui Tao, Lei Zhang, Ronghua Liu · Physical Review Applied · 2024
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.