Neuromorphic computing based on silicon photonics

Abstract

We present our latest progress using new neuromorphic paradigms for optical information processing in silicon photonics. We show how passive reservoir computing chips can be used to perform a variety of tasks (bit level tasks, nonlinear dispersion compensation, ...) at high speeds and low power consumption. In addition, we present a spatial analog of reservoir computing based on pillar scatterers and a cavity, that can be used to speed up classification of biological cells.