Browsing by Author "Bienstman, P."

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    Photonic Delay-based Reservoir Computing Integrated on InP Chip
    (Conference on Lasers and Electro-Optics Europe & European Quantum Electronics Conference, 2019) Harkhoe, K.; Verschaffelt, G.; Katumba, A.; Bienstman, P.; Van der Sande, G.
    Delay-based reservoir computing (RC) offers a simple technological route to implement photonic neuromorphic computation. Its operation boils down to a time-multiplexing with the delay limiting the processing speed. As most optical setups end up to be bulky employing long fiber loops or free-space optics, the processing speeds are limited in the range of kSa/s to tens of MSa/s [1]. In this work, we focus on external cavities which are far shorter than what has been realized before in experiment. We present the results of an experimental validation of reservoir computing based on a semiconductor laser with a 10.8 cm delay line, both integrated on an active/passive InP photonic chip built on the Jeppix platform [3]. The single mode laser operates around 1550nm with a side mode suppression of larger than 20dB.
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    Photonic neuromorphic information processing and reservoir computing
    (APL Photonics, 2020) Lugnan, A.; Katumba, A.; Laporte, F.; Freiberger, M.; Sackesyn, S.; Ma, C.; Gooskens, E.; Dambre, J.; Bienstman, P.
    Photonic neuromorphic computing is attracting tremendous research interest now, catalyzed in no small part by the rise of deep learning in many applications. In this paper, we will review some of the exciting work that has been going in this area and then focus on one particular technology, namely, photonic reservoir computing.