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Institute Colloquium



Dr. Daniel Brunner

Physics, integration and training of scalable and autonomous Photonic Neural Networks

May 22, 2024

Start: 10:30 am

Venue: Lecture hall in the Abbe Center Beutenberg (Hans-Knöll-Straße 1)


After lunch, there is the opportunity to talk to the speaker over a cup of coffee.

Start: 2 pm

Venue: Science Lounge at Leibniz IPHT


Contact person: Mario Chemnitz

Daniel Brunner is a CNRS researcher with the FEMTO-ST, France. His interests include novel computing using quantum or nonlinear substrates with a focuses on photonic neural networks. He was received several University and the IOP’s 2010 Roys prize, the IOP Journal Of Physics: Photonics emerging leader 2021 prize as well as the CNRS Bronze medal in 2022. He edited one Book and two special issues, has presented his results 60+ times upon invitation, has published 50+ scientific articles, has been awarded a prestigious ERC Consolidator grant and is a pilot of the French PEPR Electronique project of the France 2030 initiative.

Neural network (NN) concepts revolutionize computing by solving challenges previously thought to be reserved to the abstract intelligence of humans. However, the astonishing and substantial conceptual breakthroughs are so far not mirrored by advances in integrated hardware specialized in physically implementing NNs. As always with computing, scalability is the key metric. Integrated photonic architectures have the potential to revolutionize energy consumption and speed. However, conventional 2D lithography strongly limits the size of integrated NNs due to fundamental scaling laws. We want to overcome this problem by using 3D printed photonic integration, where photonic waveguides realizing a NN’s connections. Finally, for maximal efficiency, the largest fraction of a NN’s hardware should be dedicated to the core computational task, while auxiliary infrastructure should be pushed into the background. I will demonstrate a fully autonomous photonic NN based on a high-dimensional semiconductor laser that implements a scalable photonic NN fully in parallel and with minimal support by a classical digital computer.

The lecture will be given in English.