Experimental demonstration of reservoir computing on a silicon photonics chip

By K. Vandoorne, P. Mechet, T. Van Vaerenbergh, M. Fiers, G. Morthier, David Verstraeten, Benjamin Schrauwen, Joni Dambre, Peter Bienstman. Nature Communications, 2014

802 citations recorded by the source corpus.

In today’s age, companies employ machine learning to extract information from large quantities of data. One of those techniques, reservoir computing (RC), is a decade old and has achieved state-of-the-art performance for processing sequential data. Dedicated hardware realizations of RC could enable speed gains and power savings. Here we propose the first integrated passive silicon photonics reservoir. We demonstrate experimentally and through simulations that, thanks to the RC paradigm, this generic chip can be used to perform arbitrary Boolean logic operations with memory as well as 5-bit header recognition up to 12.5 Gbit s−1, without power consumption in the reservoir. It can also perform isolated spoken digit recognition. Our realization exploits optical phase for computing. It is scalable to larger networks and much higher bitrates, up to speeds >100 Gbit s−1. These results pave the way for the application of integrated photonic RC for a wide range of applications. Reservoir computing uses computational techniques related to neural networks to perform certain computing tasks. Here, the authors implement a passive optical reservoir computing scheme integrated on a silicon chip, operating at speeds up to 12.5 Gbit s−1.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/c99d52d51cc863591b9505b0554604d245b04fd4

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Citations in the corpus

Continuous-variable quantum neural networks (2018, background): “[30] Kristof Vandoorne, Pauline Mechet, Thomas Van Vaerenbergh, Martin Fiers, Geert Morthier, David Verstraeten, Benjamin Schrauwen, Joni Dambre, and Peter Bienstman.”
Emerging opportunities and challenges for the future of reservoir computing (2024, methodology): “Reservoir networks based on optical systems have also made signi ﬁ cant progress 81 , such as multi-scattering nodes in free space 32 , single nonlinear nodes with ﬁ ber loop 58 , and integrated on-chip reservoirs 66 .”
Experimental photonic quantum memristor (2021, background): “[47] Kristof Vandoorne, Pauline Mechet, Thomas Van Vaerenbergh, Martin Fiers, Geert Morthier, David Verstraeten, Benjamin Schrauwen, Joni Dambre, and Peter Bienstman, “Experimental demonstration of reservoir computing on a silicon photonics chip,” Nature communications 5, 1–6 (2014).”