Experimental Investigation of Time Series Classification using a Self-Pulsing Microring Resonator Network

Abstract

Photonic neuromorphic computing offers compelling advantages in power efficiency and parallel processing, but often falls short in realizing scalable nonlinearity and long-term memory. These limitations can be overcome by silicon microring resonator (MRR) networks. These integrated photonic circuits enable compact, high-throughput neuromorphic computing by simultaneously exploiting spatial, temporal, and wavelength dimensions. This work provides an in-depth study of of MRR networks for photonics-based machine learning (ML). We investigate the system's effectiveness on two widely used image classification benchmarks, MNIST and Fashion-MNIST, by encoding images directly into time sequences. In particular, we enhance the computational performance of a linear readout classifier within the reservoir computing paradigm through the strategic use of multiple physical output ports, diverse laser wavelengths, and varied input power levels. Moreover, we explore a single-pixel classification setting, where inference does not require digital memory, thanks to the inherent memory and parallelism of our MRR network.