An international team of researchers just introduced a new photonic processor.
An international team of researchers just introduced a new photonic processor.
Source: INRS

Using light to revolutionize AI

An international team of researchers, including Professor Roberto Morandotti of the Institut national de la recherche scientifique (INRS), just introduced a new photonic processor that could revolutionize artificial intelligence.

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.

Subscribe to our newsletter

Related articles

AI challenge aims to improve mammography accuracy

AI challenge aims to improve mammography accuracy

AI techniques, used in combination with the evaluation of expert radiologists, improve the accuracy in detecting cancer using mammograms.

Mental health game changer

Mental health game changer

Using a simple computer game and AI techniques, researchers were able to identify behavioural patterns in subjects with depression and bipolar disorder.

AI used to screen for FASD

AI used to screen for FASD

Scientists have developed a new tool that can screen children for fetal alcohol spectrum disorder (FASD) quickly and affordably.

Explainable AI for decoding genome biology

Explainable AI for decoding genome biology

Researchers have developed advanced explainable AI in a technical tour de force to decipher regulatory instructions encoded in DNA.

Designing medical deep learning systems

Designing medical deep learning systems

Researchers have analysed whether better design of deep learning studies can lead to the faster transformation of medical practices.

'Liquid' machine learning system adapts to changing conditions

'Liquid' machine learning system adapts to changing conditions

A machine learning system learns on the job. By continuously adapting to new data inputs, this “liquid network” could aid decision-making in medical diagnosis.

How to train a robot - using AI and supercomputers

How to train a robot - using AI and supercomputers

Computer scientists use TACC systems to generate synthetic objects for robot training.

Smart apps help people with hearing loss

Smart apps help people with hearing loss

Researchers have developed smartphone-based apps that solve the biggest problems for people with hearing loss: filtering out background noise and improving speech perception.

Biomedical research: deep learning outperforms machine learning

Biomedical research: deep learning outperforms machine learning

Deep-learning methods have the potential to offer substantially better results, generating superior representations for characterizing the human brain.

Popular articles