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Harnessing Noise in Optical Computing for AI - ELE Times

#artificialintelligence

Artificial intelligence and machine learning are currently affecting our lives in many small but impactful ways. For example, AI and machine learning applications recommend entertainment we might enjoy through streaming services such as Netflix and Spotify. In the near future, it's predicted that these technologies will have an even larger impact on society through activities such as driving fully autonomous vehicles, enabling complex scientific research and facilitating medical discoveries. But the computers used for AI and machine learning demand a lot of energy. Currently, the need for computing power related to these technologies is doubling roughly every three to four months.


Harnessing noise in optical computing for AI

#artificialintelligence

Artificial intelligence and machine learning are currently affecting our lives in many small but impactful ways. For example, AI and machine learning applications recommend entertainment we might enjoy through streaming services such as Netflix and Spotify. In the near future, it's predicted that these technologies will have an even larger impact on society through activities such as driving fully autonomous vehicles, enabling complex scientific research and facilitating medical discoveries. But the computers used for AI and machine learning demand a lot of energy. Currently, the need for computing power related to these technologies is doubling roughly every three to four months.


Photonic Processors Light the Way

Communications of the ACM

Ongoing advances in electronics and computing have introduced opportunities to achieve things that once seemed inconceivable: build autonomous machines, solve complex deep learning problems, and communicate instantaneously across the planet. Yet, for all the advances, today's systems--which rely on electronic processors--are grounded in a frustrating reality: the sheer physics of electrons limits their bandwidth and forces them to produce enormous heat, which means they draw vast amounts of energy. As demand for fast and low-energy artificial intelligence (AI) grows, researchers are exploring ways to push beyond electrons and into the world of photons. They are replacing electronic processors with photonic designs that incorporate lasers and other light components. While there is skepticism among some observers that the technology can transform analog computing, researchers in the optical space are now building systems demonstrating significant benefits in AI and deep learning.


Light-Powered Computers Brighten AI's Future

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The idea of building a computer that uses light rather than electricity goes back more than half a century. "Optical computing" has long promised faster performance while consuming much less energy than conventional electronic computers. The prospect of a practical optical computer has languished, however, as scientists have struggled to make the light-based components needed to outshine existing computers. Despite these setbacks, optical computers might now get a fresh start--researchers are testing a new type of photonic computer chip, which could pave the way for artificially intelligent devices as smart as self-driving cars, but small enough to fit in one's pocket. A conventional computer relies on electronic circuits that switch one another on and off in a dance carefully choreographed to correspond to, say, the multiplication of two numbers.


Viewpoint: Reservoir Computing Speeds Up

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Light-based (photonic) technologies offer many benefits when it comes to building a computer: they are efficient, have high bandwidths, and deliver fast processing speeds. But progress in photonics-based computing has almost always been outpaced by advances in semiconductor electronics, which make up the logical elements in today's computers. Where photonics does seem to be making headway is with alternative computation schemes, which solve problems differently than conventional transistor-based (binary) computers. One example of an alternative scheme is reservoir computing, which uses interconnected devices to mimic the neuronal architecture of the brain. Laurent Larger, of the French National Center for Scientific Research (CNRS) and the University of Burgundy Franche-Compté, and co-workers [1] have taken a photonics-based reservoir computer design and refitted part of it with optoelectronic components to achieve a threefold increase in processing speed.