Physical reservoir computing and deep neural networks using artificial and natural noncollinear spin textures
文献类型：期刊论文
| 作者 | Li, Haotian2,6; Li, Liyuan2,6; Xiang, Rongxin2,6; Liu, Wei4; Yan, Chunjie2,6; Tao, Zui2,6; Zhang, Lei1,5 ; Liu, Ronghua2,3,6
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| 刊名 | PHYSICAL REVIEW APPLIED
 |
| 出版日期 | 2024-07-11 |
| 卷号 | 22 |
| ISSN号 | 2331-7019 |
| DOI | 10.1103/PhysRevApplied.22.014027 |
| 通讯作者 | Liu, Ronghua(rhliu@nju.edu.cn) |
| 英文摘要 | 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. |
| 资助项目 | National Key Research and Development Program of China[2023YFA1406603] ; National Natural Science Founda-tion of China[12074178] ; National Natural Science Founda-tion of China[12074386] ; National Natural Science Founda-tion of China[12374128] ; National Natural Science Founda-tion of China[12204006] ; Open Research Fund of Jiangsu Provincial Key Laboratory for Nanotechnology |
| WOS研究方向 | Physics |
| 语种 | 英语 |
| WOS记录号 | WOS:001267497500001 |
| 出版者 | AMER PHYSICAL SOC |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Founda-tion of China ; Open Research Fund of Jiangsu Provincial Key Laboratory for Nanotechnology |
| 源URL | [http://ir.hfcas.ac.cn:8080/handle/334002/136875]  |
| 专题 | 中国科学院合肥物质科学研究院 |
| 通讯作者 | Liu, Ronghua |
| 作者单位 | 1.High Magnet Field Lab Anhui Prov, Hefei 230031, Peoples R China 2.Nanjing Univ, Sch Phys, Nanjing 210093, Peoples R China 3.Nanjing Univ, Natl Key Lab Spintron, Suzhou 215163, Peoples R China 4.Anhui Univ, Inst Phys Sci & Informat Technol, Hefei 230601, Peoples R China 5.Chinese Acad Sci, Hefei Inst Phys Sci, Anhui Key Lab Low Energy Quantum Mat & Devices, High Magnet Field Lab, Hefei 230031, Peoples R China 6.Nanjing Univ, Natl Lab Solid State Microstruct, Jiangsu Prov Key Lab Nanotechnol, Nanjing 210093, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Haotian,Li, Liyuan,Xiang, Rongxin,et al. Physical reservoir computing and deep neural networks using artificial and natural noncollinear spin textures[J]. PHYSICAL REVIEW APPLIED,2024,22. |
| APA | Li, Haotian.,Li, Liyuan.,Xiang, Rongxin.,Liu, Wei.,Yan, Chunjie.,...&Liu, Ronghua.(2024).Physical reservoir computing and deep neural networks using artificial and natural noncollinear spin textures.PHYSICAL REVIEW APPLIED,22. |
| MLA | Li, Haotian,et al."Physical reservoir computing and deep neural networks using artificial and natural noncollinear spin textures".PHYSICAL REVIEW APPLIED 22(2024). |
入库方式： OAI收割
来源：合肥物质科学研究院
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