Silicon photonics for neuromorphic information processing

We present our latest results on silicon photonics neuromorphic information processing based a.o. on techniques like reservoir computing. We will discuss aspects like scalability, novel architectures for enhanced power e ciency, as well as all-optical readout. Additionally, we will touch upon new machine learning techniques to operate these integrated readouts. Finally, we will show how these systems can be used for high-speed low-power information processing for applications like recognition of biological cells.

Keywords

Photonic neuromorphic information processing, Reservoir computing

Citation

Peter Bienstman, Joni Dambre, Andrew Katumba, Matthias Freiberger, Floris Laporte, Alessio Lugnan, "Silicon photonics for neuromorphic information processing," Proc. SPIE 10551, Optical Data Science: Trends Shaping the Future of Photonics, 105510K (14 February 2018); doi: 10.1117/12.2284391

URI

https://nru.uncst.go.ug/handle/123456789/5632

Collections

Engineering and Technology

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