University of California, Los Angeles

Optical neural networks without electricity: how to pass light through a lens to instantly multiply matrices

In 2018, a UCLA group 3D-printed a diffractive deep neural network (D²NN) from ordinary plastic. Five plastic plates perform matrix multiplication using light diffraction, achieving 91.75% accuracy on handwritten digit recognition with zero electricity after fabrication. Training is done on GPU, then weights are fixed in the plate geometry.

University of California, Los Angeles