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To Power A.I., Start-Up Creates a Giant Computer Chip -
Aug. 19, 2019 The New York Times By Cade Metz.

New A.I. systems rely on neural networks. Loosely based on the network of neurons in the human brain, these complex mathematical systems can learn tasks by analyzing vast amounts of data. By pinpointing patterns in thousands of cat photos, for instance, a neural network can learn to recognize a cat.

That requires a particular kind of computing power. Today, most companies analyze data with help from graphics processing units, or G.P.U.s. These chips were originally designed to render images for games and other software, but they are also good at running the math that drives a neural network.

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About six years ago, as tech giants like Google, Facebook and Microsoft doubled down on artificial intelligence, they started buying enormous numbers of G.P.U.s from the Silicon Valley chip maker Nvidia. In the year leading up to the summer of 2016, Nvidia sold $143 million in G.P.U.s. That was more than double the year before.

But the companies wanted even more processing power. Google built a chip specifically for neural networks — the tensor processing unit, or T.P.U. — and several other chip makers chased the same goal.
artificial_intelligence  Cerebras  conventional_wisdom  Intel  Qualcomm  semiconductors  start_ups 
6 weeks ago by jerryking

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