Fundamental research leading towards faster wireless networks, secure low-power technologies for the Internet of Things, and even 3D displays will be the focus of Intel’s collaboration with the French Alternative Energies and Atomic Energy Commission (CEA).

Intel and the CEA already work together in the field of high-performance computing, and a new agreement signed Thursday will see Intel fund work at the CEA’s Laboratory for Electronics and Information Technology (LETI) over the next five years, according to Rajeeb Hazra, vice president of Intel’s data center group.

The CEA was founded in 1945 to develop civil and military uses of nuclear power. Its work with Intel began soon after it ceased its atmospheric and underground nuclear weapons test programs, as it turned to computer modeling to continue its weapons research, CEA managing director Daniel Verwaerde said Thursday.

That effort continues, but the organization’s research interests today are more wide-ranging, encompassing materials science, climate, health, renewable energy, security and electronics.

These last two areas will be at the heart of the new research collaboration, which will see scientists at LETI exchanging information with those at Intel.

Both parties dodged questions about who will have the commercial rights to the fruits of their research, but each said it had protected its rights. The deal took a year to negotiate.

“It’s a balanced agreement,” said Stéphane Siebert, director of CEA Technology, the division of which LETI is a part.

Who owns what from the five-year research collaboration may become a thorny issue, for French taxpayers and Intel shareholders alike, as it will be many years before it becomes clear which technologies or patents are important.

Hazra emphasized the extent to which Intel is dependent on researchers outside the U.S. The company has over 50 laboratories in Europe, four of them specifically pursuing so-called exa-scale computing, systems capable of billions of billions of calculations per second.

Source-http://www.thegurureview.net/mobile-category/intel-look-to-atomic-energy-for-mobile-technologys-future.html

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.

Big Sur comprises eight high-performance GPUs of up to 300 watts each, with the flexibility to configure between multiple PCI-e topologies. It makes use of Nvidia’s Tesla Accelerated Computing Platform, and as a result is twice as fast as Facebook’s previous generation rack.

“This means we can train twice as fast and explore networks twice as large,” said the firm in its engineering blog. “And distributing training across eight GPUs allows us to scale the size and speed of our networks by another factor of two.”

Facebook claims that as well as better performance, Big Sur is also far more versatile and efficient than the off-the-shelf solutions in its previous generation.

“While many high-performance computing systems require special cooling and other unique infrastructure to operate, we have optimised these new servers for thermal and power efficiency, allowing us to operate them even in our own free-air cooled, Open Compute standard data centres,” explained the company.

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. 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.

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.

Consisting of two accelerators, Nvidia’s latest hyperscale line aims to let researchers design new deep neural networks more quickly for the increasing number of applications they want to power with AI. It also is designed to deploy these networks across the data centre. The line also includes a suite of GPU-accelerated libraries.

Courtesy-TheInq