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Learning with light: New system allows optical 'deep learning'

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"Deep Learning" computer systems, based on artificial neural networks that mimic the way the brain learns from an accumulation of examples, have become a hot topic in computer science. In addition to enabling technologies such as face- and voice-recognition software, these systems could scour vast amounts of medical data to find patterns that could be useful diagnostically, or scan chemical formulas for possible new pharmaceuticals.


Learning with light: New system allows optical 'deep learning': Neural networks could be implemented more quickly using new photonic technology

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But the computations these systems must carry out are highly complex and demanding, even for the most powerful computers. Now, a team of researchers at MIT and elsewhere has developed a new approach to such computations, using light instead of electricity, which they say could vastly improve the speed and efficiency of certain deep learning computations. Their results appear today in the journal Nature Photonics in a paper by MIT postdoc Yichen Shen, graduate student Nicholas Harris, professors Marin Soljacic and Dirk Englund, and eight others. Soljacic says that many researchers over the years have made claims about optics-based computers, but that "people dramatically over-promised, and it backfired." While many proposed uses of such photonic computers turned out not to be practical, a light-based neural-network system developed by this team "may be applicable for deep-learning for some applications," he says.


Deep Learning at the Speed of Light on Nanophotonic Chips

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Deep learning has transformed the field of artificial intelligence, but the limitations of conventional computer hardware are already hindering progress. Researchers at MIT think their new "nanophotonic" processor could be the answer by carrying out deep learning at the speed of light. In the 1980s, scientists and engineers hailed optical computing as the next great revolution in information technology, but it turned out that bulky components like fiber optic cables and lenses didn't make for particularly robust or compact computers. In particular, they found it extremely challenging to make scalable optical logic gates, and therefore impractical to make general optical computers, according to MIT physics post-doc Yichen Shen. One thing light is good at, though, is multiplying matrices--arrays of numbers arranged in columns and rows.


Photonic Computing Company Takes Aim at Artificial Intelligence

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Chip startup Lightmatter has received an infusion of $11 million from investors to help bring the world's first silicon photonics processor for AI to market. Using technology originally developed at MIT, the company is promising "orders of magnitude performance improvements over what's feasible using existing technologies."


Optical computing for deep learning with a programmable nanophotonic processor NextBigFuture.com

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Soljačić says that many researchers over the years have made claims about optics-based computers, but that "people dramatically over-promised, and it backfired." While many proposed uses of such photonic computers turned out not to be practical, a light-based neural-network system developed by this team "may be applicable for deep-learning for some applications," he says. Traditional computer architectures are not very efficient when it comes to the kinds of calculations needed for certain important neural-network tasks. Such tasks typically involve repeated multiplications of matrices, which can be very computationally intensive in conventional CPU or GPU chips. After years of research, the MIT team has come up with a way of performing these operations optically instead.