There is a long history linking optics and machine learning, going back to the 1980’s when optics was first used for the implementation of neural networks. The interest in the optical implementation of neural networks has been revived recently due to the explosion in the size of the networks that are realized and the associated high energy consumption required to train and operate digitally these networks. In this presentation, I will focus primarily on multimode fibers and their use as nonlinear optical computing elements. I will show that in a variety of classification tasks, the combination of nonlinear optical elements and digital co-processors [1] can reach classification accuracy competitive with very large digital multi-layer networks but with lower energy consumption. A possible application area of this technology is autonomous robots, vehicles and drones where low energy consumption is a critical need.

[1] Programming Nonlinear Propagation for Efficient Optical Learning Machines
Scalable optical learning operator, Uğur Teğin, Mustafa Yıldırım, Iİker Oğuz, Christophe Moser, Demetri Psaltis, Nature Computational Science, volume 1, pages542–549 (2021)