论文标题
内存函数对脉冲宽度的逐步依赖性旋转储层计算中的脉冲宽度
Step-like dependence of memory function on pulse width in spintronics reservoir computing
论文作者
论文摘要
物理储层计算是一种复发性神经网络，它应用了从物理系统到信息处理的动态响应。但是，计算性能与物理参数/现象之间的关系仍然不清楚。这项研究报告了我们关于电流依赖性磁阻尼在储层计算的计算性能中的作用的进展。磁性涡流核心的电流依赖性弛豫动力学导致相对于二进制输入的不对称记忆函数。由大输入引起的快速放松导致输入记忆的快速褪色，而小输入的缓慢放松使储层可以将输入存储器保持相对较长的时间。结果，在输入数据的脉冲宽度上发现了短期记忆和奇偶校验检查能力的阶梯状依赖性，在一定的脉冲宽度的一定范围内，容量保持在1.5，而长脉冲宽度极限则降至1.0。分析和数值分析都阐明了类似阶梯的行为可以归因于涡流核心的当前依赖性松弛时间到极限周期状态。 }
Physical reservoir computing is a type of recurrent neural network that applies the dynamical response from physical systems to information processing. However, the relation between computation performance and physical parameters/phenomena still remains unclear. This study reports our progress regarding the role of current-dependent magnetic damping in the computational performance of reservoir computing. The current-dependent relaxation dynamics of a magnetic vortex core results in an asymmetric memory function with respect to binary inputs. A fast relaxation caused by a large input leads to a fast fading of the input memory, whereas a slow relaxation by a small input enables the reservoir to keep the input memory for a relatively long time. As a result, a step-like dependence is found for the short-term memory and parity-check capacities on the pulse width of input data, where the capacities remain at 1.5 for a certain range of the pulse width, and drop to 1.0 for a long pulse-width limit. Both analytical and numerical analyses clarify that the step-like behavior can be attributed to the current-dependent relaxation time of the vortex core to a limit-cycle state. }
