Light-based chip boosts power efficiency of AI tasks 100-fold

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. Their research is reported in Advanced Photonics.

The chip is designed to carry out convolution operations, a core function in machine learning that enables AI systems to detect patterns in images, video, and text. These operations typically require significant computing power.

Speeding up processing

By integrating optical components directly onto a silicon chip, the researchers have created a system that performs convolutions using laser light and microscopic lenses-dramatically reducing energy consumption and speeding up processing.

"Performing a key machine learning computation at near zero energy is a leap forward for future AI systems," said study leader Volker J. Sorger, 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."

In tests, the prototype chip classified handwritten digits with about 98 percent accuracy, comparable to traditional electronic chips. The system uses two sets of miniature Fresnel lenses-flat, ultrathin versions of the lenses found in lighthouses-fabricated using standard semiconductor manufacturing techniques. These lenses are narrower than a human hair and are etched directly onto the chip.

To perform a convolution, machine learning data is first converted into laser light on the chip. The light passes through the Fresnel lenses, which carry out the mathematical transformation. The result is then converted back into a digital signal to complete the AI task.

• This article was first published on spie.org.