Artificial neural networks, layers of interconnected artificial neurons, are of great interest for machine learning tasks such as speech recognition and medical diagnosis. Actually, electronic computing hardware are nearing the limit of their capabilities, yet the demand for greater computing power is constantly growing.
Researchers turned themselves to photons instead of electrons to carry information at the speed of light. In fact, not only photons can process information much faster than electrons, but they are the basis of the current Internet, where it is important to avoid the so-called electronic bottleneck (conversion of an optical signal into an electronic signal, and vice versa).
Increased computing speed
The proposed optical neural network is capable of recognizing and processing large-scale data and images at ultra-high computing speeds, beyond ten trillion operations per second. Professor Roberto Morandotti, an expert in integrated photonics, explains how an optical frequency comb, a light source comprised of many equally spaced frequency modes, was integrated into a computer chip and used as a power-efficient source for optical computing.
This device performs a type of matrix-vector multiplication known as a convolution for image-processing applications. It shows promising results for real-time massive-data machine learning tasks, such as identifying faces in cameras or pathology identification in clinical scanning applications. Their approach is scalable and trainable to much more complex networks for demanding applications such as unmanned vehicles and real-time video recognition, allowing, in a not-so-far future, a full integration with the up-and-coming Internet of Things.
The research was reported in Nature.