The interest in neuromorphic computing hardware increased significantly in recent years, for two main reasons. We envision small-scale, compact and low-power neural networks that perform their entire function in the spin-wave domain. 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. A custom-built micromagnetic solver, based on the Pytorch machine learning framework, is used to inverse-design the scatterer. Training the neural network is equivalent to finding the field pattern that realizes the desired input-output mapping. The interference of the scattered waves creates a mapping between the wave sources and detectors. 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. We demonstrate the design of a neural network hardware, where all neuromorphic computing functions, including signal routing and nonlinear activation are performed by spin-wave propagation and interference.
0 Comments
Leave a Reply. |