Taniguchi Tomohiro, Ogihara Amon, Utsumi Yasuhiro, Tsunegi Sumito
Research Center for Emerging Computing Technologies, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki, 305-8568, Japan.
Department of Physics Engineering, Faculty of Engineering, Mie University, Tsu, Mie, 514-8507, Japan.
Sci Rep. 2022 Jun 23;12(1):10627. doi: 10.1038/s41598-022-14738-1.
Recent studies have shown that nonlinear magnetization dynamics excited in nanostructured ferromagnets are applicable to brain-inspired computing such as physical reservoir computing. The previous works have utilized the magnetization dynamics driven by electric current and/or magnetic field. This work proposes a method to apply the magnetization dynamics driven by voltage control of magnetic anisotropy to physical reservoir computing, which will be preferable from the viewpoint of low-power consumption. The computational capabilities of benchmark tasks in single MTJ are evaluated by numerical simulation of the magnetization dynamics and found to be comparable to those of echo-state networks with more than 10 nodes.
最近的研究表明，在纳米结构铁磁体中激发的非线性磁化动力学可应用于诸如物理 Reservoir 计算等受大脑启发的计算。先前的工作利用了由电流和/或磁场驱动的磁化动力学。这项工作提出了一种将由磁各向异性电压控制驱动的磁化动力学应用于物理 Reservoir 计算的方法，从低功耗的角度来看，这将是更可取的。通过对磁化动力学的数值模拟评估了单个 MTJ 中基准任务的计算能力，发现其与具有 10 个以上节点的回声状态网络相当。
