Diffractive processor designed for deep learning computes hundreds of transformations in parallel
Massively parallel universal linear transformations using a wavelength multiplexed diffractive deep neural network. Credit: Ozcan Research Group, UCLA. In today’s digital age, computational tasks have become increasingly complex. This, in turn, has led to an exponential growth in the energy consumed by digital computers. Thus, it is necessary to develop hardware resources capable of performing …