论文标题
使用非线性自旋波干扰的纳米级神经网络
Nanoscale neural network using non-linear spin-wave interference
论文作者
论文摘要
我们演示了神经网络的设计，其中所有神经形态计算功能（包括信号路由和非线性激活）均通过自旋波传播和干扰进行。网络的重量和互连是通过施加在旋转波传播基板上的磁场图案实现的，并散布了自旋波。散射波的干扰会在波源和检测器之间产生映射。训练神经网络等同于找到实现所需输入输出映射的场模式。基于Pytorch机器学习框架的定制微磁性求解器用于反设计散射器。我们表明，在高强度下，自旋波的行为从线性干扰到非线性干扰的行为，其计算能力在非线性方向上大大增加。我们设想在自旋波域中执行其整个功能的小规模，紧凑和低功率神经网络。
We demonstrate the design of a neural network, where all neuromorphic computing functions, including signal routing and nonlinear activation are performed by spin-wave propagation and interference. Weights and interconnections of the network are realized by a magnetic field pattern that is applied on the spin-wave propagating substrate and scatters the spin waves. The interference of the scattered waves creates a mapping between the wave sources and detectors. Training the neural network is equivalent to finding the field pattern that realizes the desired input-output mapping. A custom-built micromagnetic solver, based on the Pytorch machine learning framework, is used to inverse-design the scatterer. We show that the behavior of spin waves transitions from linear to nonlinear interference at high intensities and that its computational power greatly increases in the nonlinear regime. We envision small-scale, compact and low-power neural networks that perform their entire function in the spin-wave domain.
