News Link • Science, Medicine and Technology • 2018-08-01

3D-printed Deep Learning neural network uses light instead of electrons

And to some, it might seem a little like replacing a computer with an abacus, but researchers at UCLA have high hopes for their quirky, shiny, speed-of-light artificial neural network.

Coined by Rina Dechter in 1986, Deep Learning is one of the fastest-growing methodologies in the machine learning community and is often used in face, speech and audio recognition, language processing, social network filtering and medical image analysis as well as addressing more specific tasks, such as solving inverse imaging problems.

Traditionally, deep learning systems are implemented on a computer to learn data representation and abstraction and perform tasks, on par with – or better than – the performance of humans. However the team led by Dr. Aydogan Ozcan, the Chancellor's Professor of electrical and computer engineering at UCLA, didn't use a traditional computer set-up, instead choosing to forgo all those energy-hungry electrons in favor of light waves. The result was its all-optical Diffractive Deep Neural Network (D2NN) architecture.