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Powering the Large Hadron Collider

When the LHC starts up tomorrow, it will draw twice the power of nearby Geneva

3 min read

9 September 2008—The Large Hadron Collider (LHC) at the European Organization for Nuclear Research (CERN), set to start up tomorrow, is the largest physics experiment in history, and it’s probably the most power hungry. Spanning the border between Switzerland and France, the 27-kilometer accelerator ring with its accompaniment of radiation-hardened integrated circuits, feeder accelerators, computers, and supercooled superconducting magnets will, according to varying estimates, draw between 220 and 300 megawatts of electricity—enough to power the city of Geneva twice over. Keeping the power flowing reliably takes a good bit of ingenuity, as a sudden loss of power could mean serious damage to the machine and months of lost work.

Once all of these accelerators are fully operational in 2009, CERN’s estimated annual electricity consumption could approach 1000 gigawatt-hours, IEEE Spectrum learned on a visit to the lab in July. The massive LHC will account for about 60 percent; less than 15 percent of the total will go to mundane functions like keeping the lights on; and the other accelerators in the complex will account for the rest. A big part of the consumption is the hundreds of enormous superconducting magnets, though they draw much less power than equivalent conventional magnets would. The superconductors must be cryogenically cooled to temperatures between 1.8 and 4.5 kelvins (colder than outer space). If the temperature creeps even a fraction of a kelvin above that, the magnets stop working and lose control of the beam. An uncontrolled beam can melt 500 kilograms of copper in an instant, causing serious damage and halting the experiment for months. So it is crucial to keep power flowing into CERN at all times.

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The Future of Deep Learning Is Photonic

Computing with light could slash the energy needs of neural networks

10 min read
Image of a computer rendering.

This computer rendering depicts the pattern on a photonic chip that the author and his colleagues have devised for performing neural-network calculations using light.

Alexander Sludds
DarkBlue1

Think of the many tasks to which computers are being applied that in the not-so-distant past required human intuition. Computers routinely identify objects in images, transcribe speech, translate between languages, diagnose medical conditions, play complex games, and drive cars.

The technique that has empowered these stunning developments is called deep learning, a term that refers to mathematical models known as artificial neural networks. Deep learning is a subfield of machine learning, a branch of computer science based on fitting complex models to data.

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