Analog machine learning hardware offers a promising alternative to digital counterparts as a more energy efficient and faster platform. Wave physics based on acoustics and optics is a natural candidate to build analog processors for time-varying signals. In a new report on Science AdvancesTyler W. Hughes and a research team in the departments of Applied Physics and Electrical Engineering at Stanford University, California, identified mapping between the dynamics of wave physics and computation in recurrent neural networks.
