论文标题
使用磁场诱导的天空动力学通过神经形态计算进行模式识别
Pattern recognition with neuromorphic computing using magnetic-field induced dynamics of skyrmions
论文作者
论文摘要
物理系统中的非线性现象可用于低能消耗的脑启发计算。称为Skyrmion的拓扑自旋结构的动力学的响应是这种神经形态计算的候选者之一。但是，它的能力尚未在实验中得到很好的探索。在这里，我们在实验中使用非线性响应源自磁场诱导的天际动力学，从而实验证明了神经形态计算。我们设计了一个简单的基于天际的神经形态设备，并以高达94.7％和波形识别的精度成功地获得了手写数字识别。值得注意的是，识别精度与设备中的天空数量之间存在正相关。诸如位置和大小之类的天空系统的大量自由源于更复杂的非线性映射和较大的输出尺寸，因此准确性很高。我们的结果为开发节能和高性能的天际神经形态计算设备提供了指南。
Nonlinear phenomena in physical systems can be used for brain-inspired computing with low energy consumption. Response from the dynamics of a topological spin structure called skyrmion is one of the candidates for such a neuromorphic computing. However, its ability has not been well explored experimentally. Here, we experimentally demonstrate neuromorphic computing using nonlinear response originating from magnetic-field induced dynamics of skyrmions. We designed a simple-structured skyrmion-based neuromorphic device and succeeded in handwritten digit recognition with the accuracy as large as 94.7 % and waveform recognition. Notably, there exists a positive correlation between the recognition accuracy and the number of skyrmions in the devices. The large degree of freedoms of skyrmion systems, such as the position and the size, originate the more complex nonlinear mapping and the larger output dimension, and thus high accuracy. Our results provide a guideline for developing energy-saving and high-performance skyrmion neuromorphic computing devices.
