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
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.
Topics & Concepts
Reservoir computingArtificial neural networkNatural (archaeology)Spin (aerodynamics)Computer scienceArtificial intelligenceCondensed matter physicsPhysicsGeologyRecurrent neural networkPaleontologyThermodynamicsNeural Networks and Reservoir ComputingNeural Networks and ApplicationsAdvanced Memory and Neural Computing