Dual time- and wavelength-multiplexed photonic reservoir computing

Photonics-based AI processors have the potential to outperform digital AI accelerators in terms of throughput, latency, and efficiency, and meet the growing demands of AI edge computing. One such class of processors, known as photonic reservoir computers, are a promising candidate for performing real-time edge computing, but their performance has been limited by the small number of effective nodes in the reservoir. We discuss our advances in developing a next-generation photonic reservoir with dual time and wavelength multiplexing. This allows for a reservoir with increased size and complexity, enabling it to successfully perform complex classification and prediction tasks.