Artificial intelligence (AI) systems are increasingly central to technology, powering everything from facial recognition to language translation. But as AI models grow more complex, they consume vast amounts of electricity—posing challenges for energy efficiency and sustainability. A new chip developed by researchers at the University of Florida could help address this issue by using light, rather than just electricity, to perform one of AI’s most power-hungry tasks. The advance dramatically reduces energy consumption and speeds up processing.

The cover of Advanced Photonics Volume 7 Issue 5 features a schematic of a system that performs convolution operations using optical components—laser light and microscopic lenses—integrated directly onto a silicon chip. The cover image is based on the research presented in the article by H. Yang et al., “Near-energy-free photonic Fourier transformation for convolution operation acceleration,” Advanced Photonics 7(5), 056007 (2025), doi: 10.1117/1.AP.7.5.056007

“Performing a key machine learning computation at near zero energy is a leap forward for future AI systems,” said study leader Volker J. Sorger, PhD, the Rhines Endowed Professor in Semiconductor Photonics at the University of Florida. “This is critical to keep scaling up AI capabilities in years to come.”

“This is the first time anyone has put this type of optical computation on a chip and applied it to an AI neural network,” said Hangbo Yang, PhD, a research associate professor in Sorger’s group and first-author of the study.

In addition to demonstrating accurate on-chip machine-learning computation at near-zero energy, the team also demonstrated that the prototype chip can process multiple data streams simultaneously by using lasers of different colors—a technique known as wavelength multiplexing. “We can have multiple wavelengths, or colors, of light passing through the lens at the same time,” Yang said. “That’s a key advantage of photonics.”