Anton Karazeev about optical setups that can mimic the functionality of artificial neural networks (Optical Neural Networks) - paper , Nature, 2017.
The linear (and some nonlinear) transformations can be applied at the speed of light in optical setups. It’s well known from physics that a lens performs Fourier transform without any energy consumption and consequently some matrix operations can be performed optically without consumption of energy.
These advantages in speed and energy consumption make optical neural networks (ONNs) fairly prospective field of research.
Authors  implemented nanophotonic circuit and classified spoken vowels with it (they “trained” Mach-Zender interferometer by changing the phase shifts of laser beam). According to the paper, the nonlinearity wasn’t implemented optically but was calculated on classical computer. Perhaps the training phase and nonlinearity will be implemented inside the optical circuit in next versions.
-  Deep learning with coherent nanophotonic circuits
-  Mach–Zehnder interferometer
-  Computing by Means of Physics-Based Optical Neural Networks
- *OIU - Optical Interference Unit