Experimental Realization of Reservoir Computing with Wave Chaotic Systems

The execution of machine learning (ML) software largely depends on the computing `substrate', which is often not optimized for running ML tasks. The invention of ML-tailored hardware may greatly improve the computing speed and power efficiency. Photonic devices are well-suited for ML due to the parallelism of light. Reservoir computing (RC) is essentially a one-layer neural network (NN) with nonlinear connections, but radically simpler than NN since only the coupling between the reservoir nodes and outputs is trained. Thus RC is well-suited for physical realizations. Here we utilize the complicated wave dynamics inside a chaotic-shaped overmoded electromagnetic cavity containing nonlinear elements to emulate the complex dynamics of an RC. We propose unique techniques to create virtual RC nodes by both frequency stirring and spatial perturbation. The computational power of the wave chaotic RC is experimentally demonstrated with the so-called observer task, where we predict the future evolution of chaotic Rossler y(t) and z(t) time series using the x(t) series as the input. Different tasks are executed with a single RC physical device by simply switching output couplers.