Facebook Open-Sources Design Of Big Sur Artificial Intelligence Server To Take …

11 Dec 2015 | Author: | No comments yet »

Facebook Open Sources ‘Big Sur’ AI Hardware Design.

FACEBOOK HAS UNVEILED its next-generation GPU-based systems for training neural networks, Open Rack-compatible hardware code-named “Big Sur” which it plans to open source. Facebook is releasing for free the designs of a new computer server – twice as fast as those used by Facebook earlier, it designed to put more power behind artificial-intelligence software, MIT Technology review reported.Over the last few years, a technology called deep learning has proven so adept at identifying images, recognizing spoken words, and translating from one language to another, the titans of Silicon Valley are eager to push the state of the art even further—and push it quickly.Facebook Inc.’s use of artificial intelligence, which ranges from tools for image recognition to the filtering of the news feeds for its social network, demands special computing infrastructure.

NVIDIA (NASDAQ: NVDA) today announced that Facebook will power its next-generation computing system with the NVIDIA® Tesla® Accelerated Computing Platform, enabling it to drive a broad range of machine learning applications. The social media giant’s latest machine learning system has been designed for artificial intelligence (AI) computing at a large scale, and in most part has been crafted with Nvidia hardware. The company recently began building custom servers for its artificial intelligence workload and Thursday announced it would release the designs for that powerful hardware to the world — for free.

While training complex deep neural networks to conduct machine learning can take days or weeks on even the fastest computers, the Tesla platform can slash this by 10-20x. Facebook worked closely with Nvidia, a leading manufacturer of GPUs, on its new server designs, which have been stripped down to cram in more of the chips. The social network on Thursday unveiled new AI hardware its researchers have developed to train neural networks, and open-sourced the design, offering other organizations a blueprint for how to set up their own AI-specific infrastructure.

At Google, this tech not only helps the company recognize the commands you bark into your Android phone and instantly translate foreign street signs when you turn your phone their way. The company said the plan to open-source the blueprints of the servers — called “Big Sur” — would help other companies and researchers benefit from the incessant tweaking of Facebook’s developers. We spoke to Nvidia’s senior product manager for GPU Computing, Will Ramey, ahead of the launch, who has been working on the Big Sur project alongside Facebook for some time. “The project is the first time that a complete computing system that is designed for machine learning and AI will be released as an open source solution,” said Ramey. “By taking the purpose-built design spec that Facebook has designed for their own machine learning apps and open sourcing them, people will benefit from and contribute to the project so it can move the entire industry forward.” While Big Sur was built with Nvidia’s new Tesla M40 hyperscale accelerator in mind, it can actually support a wide range of PCI-e cards in what Facebook believes could make for better efficiencies in production and manufacturing to get more computational power for every penny that it invests. “Servers can also require maintenance and hefty operational resources, so, like the other hardware in our data centres, Big Sur was designed around operational efficiency and serviceability,” Facebook said. “We’ve removed the components that don’t get used very much, and components that fail relatively frequently – such as hard drives and DIMMs – can now be removed and replaced in a few seconds.” Perhaps the most interesting aspect of the Big Sur announcement is Facebook’s plans to open-source it and submit the design materials to the Open Compute Project. At Facebook, it helps identify faces in photos, choose content for your News Feed, and even deliver flowers ordered through M, the company’s experimental personal assistant.

This is a bid to make it easier for AI researchers to share techniques and technologies. “As with all hardware systems that are released into the open, it’s our hope that others will be able to work with us to improve it,” Facebook said, adding that it believes open collaboration will help foster innovation for future designs, and put us closer to building complex AI systems that will probably take over the world and kill us all. All the while, these two titans hope to refine deep learning so that it can carry on real conversations—and perhaps even exhibit something close to common sense.

Nvidia released its end-to-end hyperscale data centre platform last month claiming that it will let web services companies accelerate their machine learning workloads and power advanced artificial intelligence applications. GPUs are widely used in artificial intelligence because the chips have far more individual processing cores on them than traditional processors produced by Intel Corp., making them adept at the dumb-but-numerous calculations required by AI software.

Our goal is to provide researchers and companies with the most productive platform to advance this exciting work.” In addition to reducing neural network training time, GPUs offer a number of other advantages. And the Tesla platform’s growing global adoption facilitates open collaboration with researchers around the world, fueling new waves of discovery and innovation in the machine learning field. For Facebook, releasing its designs has potent benefits: the openness can be a major incentive for top talent to join the company; firms that use the equipment may contribute their improvements back to the community, letting Facebook outsource some of its research and development costs; and if enough people buy the equipment, then economies of scale will ultimately lower the price Facebook pays for its computer hardware, Serkan Piantino, the engineering director of Facebook’s AI group, said in an briefing with reporters. “Often the things we open-source become standards in the community and it makes it easier and cheaper for us to acquire the things later because we put them out there,” Piantino said. Keep Current on NVIDIA Subscribe to the NVIDIA blog, follow us on Facebook, Google+, Twitter, LinkedIn and Instagram, and view NVIDIA videos on YouTube and images on Flickr.

The company’s technologies are transforming a world of displays into a world of interactive discovery — for everyone from gamers to scientists, and consumers to enterprise customers. The Internet’s largest services typically run on open source software. “Open source is the currency of developers now,” says Sean Stephens, the CEO of a software company called Perfect. “It’s how they share their thoughts and ideas. In the closed source world, developers don’t have a lot of room to move.” And as these services shift to a new breed of streamlined hardware better suited to running enormous operations, many companies are sharing their hardware designs as well. Important factors that could cause actual results to differ materially include: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners’ products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems; as well as other factors detailed from time to time in the reports NVIDIA files with the Securities and Exchange Commission, or SEC, including its Form 10-Q for the fiscal period ended October 25, 2015.

These forward-looking statements are not guarantees of future performance and speak only as of the date hereof, and, except as required by law, NVIDIA disclaims any obligation to update these forward-looking statements to reflect future events or circumstances. © 2015 NVIDIA Corporation. Although GPUs were originally designed to render images for computer games and other highly graphical applications, they’ve proven remarkably adept at deep learning. Traditional processors help drive these machines, but big companies like Facebook and Google and Baidu have found that their neural networks are far more efficient if they shift much of the computation onto GPUs. In short, Facebook can achieve a greater level of AI at a quicker pace. “The bigger you make the neural nets, the better they will work,” LeCun says. “The more data you get them, the better they will work.” And since deep neural nets serve such a wide variety of applications—from face recognition to natural language understanding—this single system design can significantly advance the progress of Facebook as a whole.

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