GAINESVILLE, USA (Kashmir English): A new generation of computer chip powered by light rather than electricity could revolutionise artificial intelligence, according to researchers.
The new chip that uses lasers and micro-lenses, hit 98% accuracy in tests while consuming far less energy than electronics.
A team of engineers in the US has developed a prototype chip quite capable of making AI calculations between 10 and 100 times more energy-efficient than the modern days best chips.
The feat centres on one of the most power-consuming operations in machine learning: convolution. This process allows AI systems to recognise patterns in photos, videos and even written text, but it consumes vast amounts of electricity on conventional processors.
The new design instead uses lasers and microscopic lenses etched directly onto circuit boards.
In laboratory tests, the chip matched the accuracy of electronic chips—achieving 98% success when identifying handwritten digits—while using only a fraction of the energy.
“This is the first time anyone has put this type of optical computation on a chip and applied it to an AI neural network,” according to Hangbo Yang, a research associate professor at the University of Florida and co-author of the study.
Lead researcher Volker J. Sorger, from the University of Florida’s Florida Semiconductor Institute, described the advance as “a leap forward for future AI systems”. He added: “Performing a key machine learning computation at near-zero energy is critical to keep scaling up AI capabilities in years to come.”
The study, published in Advanced Photonics on 8 September, involved collaboration between the University of Florida, UCLA and George Washington University.
How the chip works?
The prototype integrates two sets of ultra-thin Fresnel lenses—miniature versions of those found in lighthouses but only a fraction of a human hair wide.
Machine learning data is first converted into laser light on-chip, passed through the lenses, and then transformed back into digital signals to complete the task.
