Science Daily August 13, 2019
Optical computing provides unique opportunities in terms of parallelization, scalability, power efficiency, and computational speed and has attracted major interest for machine learning. Researchers at UCLA have demonstrated systematic improvements in diffractive optical neural networks, based on a differential measurement technique that mitigates the strict nonnegativity constraint of light intensity. Using this differential detection scheme, involving 10 photodetector pairs behind 5 diffractive layers with a total of 0.2 million neurons, they numerically achieved blind testing accuracies of 98.54%, 90.54%, and 48.51% for MNIST, Fashion-MNIST, and grayscale CIFAR-10 datasets, respectively. By utilizing the inherent parallelization capability of optical systems, they reduced the crosstalk. The presented framework might be useful to bring optical neural network-based low power solutions for various machine learning applications and help us design new computational cameras that are task-specific…read more. Open Access TECHNICAL ARTICLE
Blind testing classification accuracies of nondifferential and differential diffractive optical networks, without any class specificity or division. Credit: Advanced Photonics, 1(4), 046001 (2019). https://doi.org/10.1117/1.AP.1.4.046001