Abstract
Large-scale optical machine learning
exploiting disorder
Photonics is an ideal technology for low-energy and ultrafast information
processing, and photonic computing is currently seeing a surge of interest, with
exciting perspectives. In Machine Learning, optics is naturally well suited to
implement a layer of neural networks, via either integrated or free-space
approaches. However, most proof-of-concepts of optical machine learning to date
are limited to modest dimensions, single or relatively shallow artificial neural layer
networks, and to relatively simple ML tasks. I will show how multiple scattering of
light allows performing optically an interesting operation for machine learning: large
scale random matrix multiplication, I will present their application to various tasks,
and discuss how we can extend this concept beyond single layers operations, and
to modern machine learning tasks.
→Contact: Cyrille Solaro (solaro@unistra.fr), Jérôme Dubail (j.dubail@unistra.fr)
