Some technology companies, including Apple, Amazon, Facebook and LG are developing their own AI and ML chipsets for specific purposes. In 2018, there were at least 45 startups working on AI chips, New York Times reported at the time. The driving factor is a boom in companies seeking to develop chips specifically designed to handle AI and machine learning applications. For instance, Lightmatter has raised $33 million, including investment from Google’s venture arm (with participation from Spark Capital and Matrix Partners) to make photonic chips. Luminous is not the only startup out there trying to build a supercomputer on a chip, nor is it the first to be focused on photonics. “While many photonics research efforts focus on general-purpose data movement, Luminous appropriately targets the AI compute market, which is where the demand is,” Partovi of NEO said. The idea is that by using photonics for all of the major bottlenecks that traditional processors struggle with will be removed. Luminous’ approach in basic terms is based on using light to move a dense amount data and multiply giant arrays of numbers instantly.
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Gao also founded software startup AlphaSheets. Gomez started a software-as-a-service business in the fashion industry and more recently worked as a data scientist at Tinder. Luminous was founded by Michael Gao, the company’s chief strategist, CEO Marcus Gomez and CTO Mitchell Nahmias, whose research at Princeton is the basis of the chip. The round also attracted other new investors, including Travis Kalanick’s fund 10100, BoxGroup, Uber CEO Dara Khosrowshahi, and Emil Michael as well as pre-seed investors Class 5 Global, Joshua Browder, Ozmen Ventures, Schox Investments and Third Kind Venture Capital. The company has raised $9 million in a seed round led by Bill Gates, Neo’s Ali Partovi and Luke Nosek and Steve Oskoui of Gigafund.
It’s a moonshot and yet, the young company already has a number of high-profile investors willing to bet on the prospect. Luminous Computing, a one-year-old startup, is aiming to build a photonics chip that will handle workloads needed for AI at the speed of light.
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