Integrated photonic delay-lasers for reservoir computing - Sande, Guy Van der; Harkhoe, Krishan; Katumba, Andrew; Bienstman, Peter; Verschaffelt, Guy (SPIE, 2020)
Currently, multiple photonic reservoir computing systems show great promise for providing a practical yet pow- erful hardware substrate for neuromorphic computing. Among those, delay-based systems o er a simple techno- logical route to implement photonic neuromorphic computation. Its operation boils down to a time-multiplexing with the delay length limiting the processing speed. As most optical setups end up to be bulky employing long ber loops or free-space optics, the processing speeds are ranging from kSa/s to tens of MSa/s. Therefore, we focus on external cavities which are far shorter than what has been realized before in such experiments. We present experimental results of reservoir computing based on a semiconductor laser, operating in a single mode regime around 1550nm, with a 10.8cm delay line. Both are integrated on an active/passive InP photonic chip built on the Jeppix platform. Using 23 virtual nodes spaced 50 ps apart in the integrated delay section, we increase the processing speed to 0.87GSa/s. The computational performance is benchmarked on a forecasting task applied to chaotic time samples. Competitive performance is observed for injection currents above thresh- old, with higher pumps having lower prediction errors. The feedback strength can be controlled by electrically pumping integrated ampli ers within the delay section. Nevertheless, we nd good performance even when these ampli ers are unpumped. To proof the relevance and necessity of the external cavity on the computational ca- pacity, we have analysed linear and nonlinear memory tasks. We also propose several post-processing methods, which increase the performance without a penalty to speed.
Keywords: Neuromorphic computing; Reservoir computing; Delayed feedback; Semiconductor laser; Photonic integration