{"id":8747,"date":"2025-09-09T00:17:07","date_gmt":"2025-09-09T00:17:07","guid":{"rendered":"https:\/\/www.newsbeep.com\/ie\/8747\/"},"modified":"2025-09-09T00:17:07","modified_gmt":"2025-09-09T00:17:07","slug":"florida-team-builds-chip-to-run-ai-tasks-100-fold-at-lower-power-cost","status":"publish","type":"post","link":"https:\/\/www.newsbeep.com\/ie\/8747\/","title":{"rendered":"Florida team builds chip to run AI tasks 100-fold at lower power cost"},"content":{"rendered":"

Artificial intelligence is becoming a backbone of modern technology, from facial recognition to translation apps. But powering AI models demands heavy electricity, raising questions about efficiency and sustainability. <\/p>\n

Researchers at the University of Florida believe they have found a way to tackle this problem. Their new chip uses light, not just electricity, to perform one of AI\u2019s most demanding tasks.<\/p>\n

Light-based computing breakthrough<\/p>\n

The chip is built to handle convolution operations, a core function in machine learning. These operations let AI detect patterns in images, video, and text. They also consume large amounts of computing power.<\/p>\n

The team integrated optical components directly onto a silicon<\/a> chip. Laser light and microscopic lenses then carry out convolutions faster and with lower energy needs.<\/p>\n

\u201cPerforming a key machine learning computation at near zero energy is a leap forward for future AI systems,\u201d said study leader Volker J. Sorger, the Rhines Endowed Professor in Semiconductor<\/a> Photonics at the University of Florida. \u201cThis is critical to keep scaling up AI capabilities in years to come.\u201d<\/p>\n

Tests showed the prototype classified handwritten digits with about 98 percent accuracy, matching conventional chips. <\/p>\n

\"\"<\/a>Illustration of new silicon photonic chip. Credit \u2013 SPIE<\/p>\n

The system relies on two sets of Fresnel lenses, flat ultrathin structures similar to those in lighthouses. Each lens is narrower than a human hair and etched onto the chip with standard semiconductor techniques.<\/p>\n

To run a convolution, data converts into laser light on the chip<\/a>. <\/p>\n

That light passes through the Fresnel lenses, which perform the mathematical transformation. The result converts back into a digital signal for the AI model.<\/p>\n

\u201cThis is the first time anyone has put this type of optical computation on a chip and applied it to an AI neural network,\u201d said Hangbo Yang, a research associate professor in Sorger\u2019s group and co-author of the study.<\/p>\n

Multiplexing for parallel processing<\/p>\n

The chip can also process multiple data streams at once. The team achieved this by using lasers of different colors. The approach is known as wavelength multiplexing.<\/p>\n

\u201cWe can have multiple wavelengths, or colors, of light passing through the lens at the same time,\u201d Yang said. \u201cThat\u2019s a key advantage of photonics.\u201d<\/p>\n

The project involved the Florida Semiconductor Institute, UCLA, and George Washington University. <\/p>\n

Sorger noted that major players such as NVIDIA already use optical components in their AI systems. This could make it easier to bring the new chip into commercial use.<\/p>\n

\u201cIn the near future, chip-based optics will become a key part of every AI chip we use daily,\u201d Sorger said. \u201cAnd optical AI computing is next.\u201d<\/p>\n

By cutting energy use while keeping accuracy high, the Florida team\u2019s chip could help scale AI to meet global demand. <\/p>\n

If the technology advances beyond the lab, light-based chips may soon power many of the AI tools people rely on every day.<\/p>\n

The study is published in the journal Advanced Photonics<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":"Artificial intelligence is becoming a backbone of modern technology, from facial recognition to translation apps. But powering AI…\n","protected":false},"author":2,"featured_media":8748,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[9265,9266,9267,61,60,3045,233,4307,4688,9268,80,9269],"class_list":["post-8747","post","type-post","status-publish","format-standard","has-post-thumbnail","category-technology","tag-ai-chips","tag-energy-efficiency","tag-fresnel-lenses","tag-ie","tag-ireland","tag-machine-learning","tag-nvidia","tag-optical-computing","tag-photonics","tag-semiconductor-research","tag-technology","tag-university-of-florida"],"_links":{"self":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/posts\/8747","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/comments?post=8747"}],"version-history":[{"count":0,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/posts\/8747\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/media\/8748"}],"wp:attachment":[{"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/media?parent=8747"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/categories?post=8747"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.newsbeep.com\/ie\/wp-json\/wp\/v2\/tags?post=8747"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}