Taniguchi Tomohiro, Imai Yusuke
National Institute of Advanced Industrial Science and Technology (AIST), Research Center for Emerging Computing Technologies, Tsukuba, Ibaraki, 305-8568, Japan.
Graduate School of Information Science and Technology, The University of Tokyo, Bunkyo-ku, Tokyo, 113-8656, Japan.
Sci Rep. 2024 Apr 8;14(1):8188. doi: 10.1038/s41598-024-58556-z.
Recently, an associative memory operation by a virtual oscillator network, consisting of a single spintronic oscillator, was examined to solve issues in conventional, real oscillators-based neural networks such as inhomogeneities between the oscillators. However, the spintronic oscillator still carries issues dissipating large amount of energy because it is driven by electric current. Here, we propose to use a single ferromagnet manipulated by voltage-controlled magnetic anisotropy (VCMA) effect as a fundamental element in a virtual neural network, which will contribute to significantly reducing the Joule heating caused by electric current. Instead of the oscillation in oscillator networks, magnetization relaxation dynamics were used for the associative memory operation. The associative memory operation for alphabet patterns is successfully demonstrated by giving correspondences between the colors in a pattern recognition task and the sign of a perpendicular magnetic anisotropy coefficient, which could be either positive or negative via the VCMA effect.
最近，人们研究了由单个自旋电子振荡器组成的虚拟振荡器网络的联想记忆操作，以解决传统的基于实际振荡器的神经网络中存在的问题，比如振荡器之间的不均匀性。然而，自旋电子振荡器仍然存在耗散大量能量的问题，因为它是由电流驱动的。在此，我们提议使用受电压控制磁各向异性（VCMA）效应操纵的单个铁磁体作为虚拟神经网络的基本元件，这将有助于显著减少由电流引起的焦耳热。我们使用磁化弛豫动力学而非振荡器网络中的振荡来进行联想记忆操作。通过在模式识别任务中给出图案中的颜色与垂直磁各向异性系数的符号之间的对应关系，成功演示了对字母图案的联想记忆操作，通过VCMA效应，垂直磁各向异性系数可以为正或为负。
