Based on increasing data sets and faster processors, artificial intelligence finds its way into industry and science. With this, the demand for computational resources increases massively. Nevertheless, hardware based on the von-Neumann architecture exhibits several shortcomings while used for AI, such as high energy consumption and long run times. Therefore, unconventional computing methods based on photonic hardware gain more and more interest. One promising concept called reservoir computing utilizes the natural information processing of dynamical systems and enables machine learning with photonic hardware. In this talk, we investigate the information encoding into a dynamical system based on optoelectronic hardware. An information-carrying
input signal drives a delayed dynamical system and triggers its transient information processing. We analyze the abilities of the reservoir to recall the past and predict the future of a chaotic time series. In a second step, we introduce a concept where several optoelectronic delay systems are coupled in a row to utilize their interplay and overcome trade-offs known in reservoir computing. We present a configuration based on the modular design principle where only the internal order in the deep reservoir is varied, providing an effective way to utilize an ensemble of layers for several different tasks without even changing their hyperparameters.
Zoom: https://uibuniversitat.zoom.us/j/83043347066
Meeting ID: 830 4334 7066
