Integrated Photonic Reservoir Computing with All-Optical Readout

• Published in: arXiv.org
• Main Authors: Ma, Chonghuai, Joris Van Kerrebrouck, Deng, Hong, Sackesyn, Stijn, Gooskens, Emmanuel, Bai, Bing, Dambre, Joni, Bienstman, Peter
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 28.06.2023
• Subjects: Hardware; Neural networks; Photonics; Read out systems; Real time; Training

Integrated photonic reservoir computing has been demonstrated to be able to tackle different problems because of its neural network nature. A key advantage of photonic reservoir computing over other neuromorphic paradigms is its straightforward readout system, which facilitates both rapid training and robust, fabrication variation-insensitive photonic integrated hardware implementation for real-time processing. We present our recent development of a fully-optical, coherent photonic reservoir chip integrated with an optical readout system, capitalizing on these benefits. Alongside the integrated system, we also demonstrate a weight update strategy that is suitable for the integrated optical readout hardware. Using this online training scheme, we successfully solved 3-bit header recognition and delayed XOR tasks at 20 Gbps in real-time, all within the optical domain without excess delays.