![]() While it may be a while before Netcast is commercialized, a bevy of the optical scientists who authored the NTT Research/MIT paper seems to have shifted from both companies to Luminous Computing, whose website states its mission to “build the world’s most powerful AI supercomputer” by “ optics to the heart of computer architecture.” Luminous will do this in part, it states, by applying optical technology (a la Netcast) to devices that can’t run sophisticated AI today (e.g., edge equipment for IoT). It has been successful in experiments, the paper’s authors said, and will support edge device data rates at today’s electronic speeds while consuming “orders of magnitude” less power. The technology described in Science was dubbed Netcast thanks to its use of wave division multiplexing (WDM). ![]() Even if these servers are located close to the device, latency and security are affected, the paper’s authors said. Instead, the edge devices must rely on separate servers to crunch the data. The so-called matrix algebra involved in creating and deploying a DNN hikes the power required so high that IoT gear such as sensors and smartphones can’t process it. What is a DNN, you ask? It’s a kind of AI tool that mimics human reasoning to make decisions. Anyway, the article outlined how newly designed smart optical transceivers can be deployed in sensors, smartphones, content delivery networks, and even aircraft to tackle the hefty processing and associated power consumption of deep neural networks (DNNs). ![]() Why the technology it describes for edge applications wasn’t widely publicized until last week is a question that, put to NTT Research, went unanswered as of this writing. A startup named Luminous Computing is focused on a technique for powering artificial intelligence (AI)-equipped edge devices that could prove to be a game changer.įirst, the science: Back in October 2022, a team of 16 experts from NTT Research Inc., the Massachusetts Institute of Technology (MIT), and Nokia (NYSE: NOK) authored a paper titled “Delocalized Photonic Deep Learning on the Internet’s Edge” that appeared in Science.
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