Google Isn't Playing Games With New Chip

By Robert McMillan 

When Google's AlphaGo computer program bested South Korean Go champion Lee Se-dol in March, it took advantage of a secret weapon: a microprocessor chip specially designed by Google. The chip sped up the Go-playing software, allowing it to plot moves in the time-limited match and look further ahead in the game.

But the processor, built in secret over the last three years and announced on Wednesday, plays a more strategic role in the company. Google, a division of Alphabet Inc., has been using it for more than a year to accelerate artificial intelligence applications as the software techniques known as machine learning become increasingly important to its core businesses. Overall the chip, known as the Tensor Processing Unit, is 10 times faster than alternatives Google considered for this work, the company said.

The company has been rumored to have been working on its own chip designs, but Wednesday's announcement marked the first time it confirmed such an effort.

Google isn't the only firm speeding up artificial intelligence with new chip designs. Microsoft Corp. is using programmable chips called Field Programmable Gate Arrays to accelerate AI computations, including those used by its Bing search engine. International Business Machines Corp. designed its own brain-inspired chip called TrueNorth that is currently being tested by Lawrence Livermore National Laboratory.

Nvidia Corp. has been pushing its chips, known as graphical processing units, into artificial intelligence as well. GPUs are designed to render videogame images on personal computers but have turned out to be well suited to performing calculations used by machine learning applications.

Google, which relies mainly on standard Intel Corp. processors for most computing jobs, also has used Nvidia GPUs for artificial intelligence calculations including its early tests of the AlphaGo software.

Large companies have begun using new processor designs to augment general-purpose processors as the pace of improvement in that field has slowed, said Mark Horowitz, a professor of electrical engineering at Stanford University.

"They're not doing this to replace the Intel processors," he said. "These are addendums to these processors."

Google and Apple Inc. lately have been aggressively hiring chip designers and engineers, Mr. Horowitz said. Apple launched its own chip-making effort around 2009 to improve the power and efficiency of its devices and develop new features.

Google believes its new chip will give it a seven-year advantage -- roughly three processor generations -- over currently available processors when it comes to machine learning. That is important because Google is betting its future on such software. It uses machine learning in more than 100 programs for applications including search, voice recognition, and self-driving cars. Such programs require intensive calculation, and supplying the processing power and electricity to do this math quickly is expensive.

"In order to make them feasible to roll out, economically, with the required latency for users and all that stuff, we looked around at the existing alternative and we decided that we needed to do our own custom accelerators," said Norman Jouppi, a distinguished hardware engineer at Google.

It is unclear how much of Google's overall computation runs on its new processors. Mr. Jouppi said Google uses more than 1,000 of the chips, but he wouldn't say whether that meant the company was buying fewer processors from vendors such as Intel or Nvidia.

"We're still buying literally tons of CPUs and GPUs," he said. "Whether it's a ton less than we would have otherwise, I can't say."

Google began using the Tensor Processing Unit in April 2015 to speed up its StreetView service's reading of street signs. It allows the company to process all the text stored in its massive collection of StreetView images -- things such as street signs and address numbers attached to the sides of houses -- in just five days, much faster than previous methods, Mr. Jouppi said.

The chip also is used in Google search ranking, photo processing, speech recognition, and language translation. The company plans to make the chips available as part of its Google Cloud Platform computing-on-demand service, he said.

TPU chips are soldered onto cards that slide into the disk-drive slots in Google's standard servers, where they handle the specialized calculations required by Google's machine learning software.

--Don Clark contributed to this article

Write to Robert McMillan at [e-mail]

(END) Dow Jones Newswires

May 18, 2016 15:11 ET (19:11 GMT)

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