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From: Glenn Petersen10/2/2017 9:01:02 PM
2 Recommendations   of 1574
 
To Compete With New Rivals, Chipmaker Nvidia Shares Its Secrets

Author: Tom Simonite
Wired
September 29, 2017



Getty Images
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Five years ago, Nvidia was best known as a maker of chips to power videogame graphics in PCs. Then researchers found its graphics chips were also good at powering deep learning, the software technique behind recent enthusiasm for artificial intelligence.

The discovery made Nvidia into the preferred seller of shovels for the AI gold rush that’s propelling dreams of self-driving cars, delivery drones and software that plays doctor. The company’s stock-market value has risen 10-fold in three years, to more than $100 billion.

That’s made Nvidia and the market it more-or-less stumbled into an attractive target. Longtime chip kingpin Intel and a stampede of startups are building and offering chips to power smart machines. Further competition comes from large tech companies designing their own AI chips. Google’s voice recognition and image search now run on in-house chips dubbed “tensor processing units,” while the face-unlock feature in Apple’s new iPhone is powered by a home-grown chip with a “ neural engine”.

Nvidia’s latest countermove is counterintuitive. This week the company released as open source the designs to a chip module it made to power deep learning in cars, robots, and smaller connected devices such as cameras. That module, the DLA for deep learning accelerator, is somewhat analogous to Apple’s neural engine. Nvidia plans to start shipping it next year in a chip built into a new version of its Drive PX computer for self-driving cars, which Toyota plans to use in its autonomous-vehicle program.

Why give away this valuable intellectual property for free? Deepu Talla, Nvidia’s vice president for autonomous machines, says he wants to help AI chips reach more markets than Nvidia can accommodate itself. While his unit works to put the DLA in cars, robots, and drones, he expects others to build chips that put it into diverse markets ranging from security cameras to kitchen gadgets to medical devices. “There are going to be hundreds of billions of internet of things devices in the future,” says Talla. “We cannot address all the markets out there.”



Source: S&P CapitalIQ
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One risk of helping other companies build new businesses is that they’ll start encroaching on your own. Talla says that doesn’t concern him because greater use of AI will mean more demand for Nvidia’s other hardware, such as the powerful graphic chips used to train deep learning software before it is deployed. “There’s no good deed that goes unpunished but net-net it’s a great thing because this will increase the adoption of AI,” says Talla. “We think we can rise higher.”

Mi Zhang, a professor at Michigan State University, calls open sourcing the DLA design a “very smart move.” He guesses that while researchers, startups, and even large companies will be tempted by Nvidia’s designs, they mostly won’t change them radically. That means they are likely to maintain compatibility with Nvidia’s software tools and other hardware, boosting the company’s influence.

Zhang says it makes sense that devices beyond cars and robots have much to gain from new forms of AI chip. He points to a recent project in his research group developing hearing aids that used learning algorithms to filter out noise. Deep-learning software was the best at smartly recognizing what to tune out, but the limitations of existing hearing aid-scale computer hardware made it too slow to be practical.

Creating a web of companies building on its chip designs would also help Nvidia undermine efforts by rivals to market AI chips and create their ecosystems around them. In a tweet this week, one Intel engineer called Nvidia’s open source tactic a “devastating blow” to startups working on deep learning chips.

It might also lead to new challenges for Intel. The company bought two such startups in the past year: Movidius, focused on image processing, and Mobileye, which makes chips and cameras for automated driving.

wired.com

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From: Glenn Petersen10/4/2017 9:58:23 AM
1 Recommendation   of 1574
 
Despite the hype, nobody is beating Nvidia in AI

Written by Dave Gershgorn
Quartz
October 02, 2017



NVIDIA CEO Jen-Hsun Huang shows NEW HGX with TESLA V100 VERSA GPU CLOUD COMPUTING as he talks about AI and gaming during the Computex Taipei exhibition at the world trade center in Taipei, Taiwan, Tuesday, May 30, 2017. (AP Photo/Chiang Ying-ying)
AI market in hand. (AP Photo/Chiang Ying-ying)
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You have to wonder whether Nvidia is going to get sick of winning all the time.

The company’s stock price is up to $178—69% more than this time last year. Nvidia is riding high on its core technology, the graphics processing unit used in the machine-learning that powers the algorithms of Facebook and Google; partnerships with nearly every company keen on building self-driving cars; and freshly announced hardware deals with three of China’s biggest internet companies. Investors say this isn’t even the top for Nvidia: William Stein at SunTrust Robinson Humphrey predicts Nvidia’s revenue from selling server-grade GPUs to internet companies, which doubled last year, will continue to increase 61% annually until 2020.

Nvidia will likely see competition in the near future. At least 15 public companies and startups are looking to capture the market for a “second wave” of AI chips, which promise faster performance with decreased energy consumption, according to James Wang of investment firm ARK. Nvidia’s GPUs were originally developed to speed up graphics for gaming; the company then pivoted to machine learning. Competitors’ chips, however, are being custom-built for the purpose.

The most well-known of these next-generation chips is Google’s Tensor Processing Unit (TPU), which the company claims is 15-30 times faster than others’ central processing units (CPUs) and GPUs. Google explicitly mentioned performance improvements over Nvidia’s tech; Nvidia says the underlying tests were conducted on Nvidia’s old hardware. Either way, Google is now offering customers the option to rent use of TPUs through its cloud.

Intel, the CPU maker recently on a shopping spree for AI hardware startups—it bought Nervana Systems in 2016 and Mobileye in March 2017—also poses a threat. The company says it will release a new set of chips called Lake Crest later in 2017 specifically focused on AI, incorporating the technology it acquired through Nervana Systems. Intel is also hedging its bets by investing in neuromorphic computing, which uses chips that don’t rely on traditional microprocessor architecture but instead try to mimic neurons in the brain.

ARK predicts Nvidia will keep its technology ahead of the competition. Even disregarding the market advantage of capturing a strong initial customer base, Wang notes that the company is also continuing to increase the efficiency of GPU architecture at a rate fast enough to be competitive with new challengers. Nvidia has improved the efficiency of its GPU chips about 10x over the past four years.

Nvidia has also been investing since the mid-aughts in research to optimize how machine-learning frameworks, the software used to build AI programs, interact with the hardware, critical to ensuring efficiency. It currently supports every major machine-learning framework; Intel supports four, AMD supports two, Qualcomm supports two, and Google supports only Google’s.

Since GPUs aren’t specifically built for machine learning, they can also pull double-duty in a datacenter as video- or image-processing hardware. TPUs are custom-built for AI only, which means they’re inefficient at tasks like transcoding video into different qualities or formats. Nvidia CEO Jen-Hsun Huang told investors in August that “a GPU is basically a TPU that does a lot more,” since many social networks are promoting video on their platforms.

“Until TPUs demonstrate an unambiguous lead over GPUs in independent tests, Nvidia should continue to dominate the deep-learning data center.” Wang writes, noting that AI chips for smaller devices outside of the datacenter are still ripe for startups to disrupt.

qz.com

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From: Glenn Petersen10/10/2017 4:08:36 PM
   of 1574
 
Nvidia says its new supercomputer will enable the highest level of automated driving

No steering wheels, no pedals, no mirrors

by Andrew J. Hawkins
The Verge
Oct 10, 2017, 6:00am EDT



Nvidia Founder, President and CEO Jen-Hsun Huang delivers a keynote address at CES 2017. Photo by Ethan Miller/Getty Images
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Nvidia, one of the world’s best known manufacturers of computer graphics cards, announced a new, more powerful computing platform for use in autonomous vehicles. The company claims its new system, codenamed Pegasus, can be used to power Level 5, fully driverless cars without steering wheels, pedals, or mirrors.

The new iteration of the GPU maker’s Drive PX platform will deliver over 320 trillion operations per second, which amounts to more than 10 times its predecessor’s processing power. Pegasus will be marketed to the hundreds of automakers and tech companies that are currently developing self-driving cars starting the second half of 2018, the company says.


Nvidia’s promise of Level 5 autonomy shouldn’t be taken lightly. Most automakers and tech companies speak carefully about the levels of autonomy, avoiding claims on which they may not ultimately be able to deliver. Nothing on the road today that’s commercially available is higher than a Level 2. Audi says its new A8 sedan is Level 3 autonomous — but we have to take the company’s word for it because present regulations won’t allow the German automaker to turn it on. Most car companies have said they will probably skip Level 3 and 4 because it’s too dangerous, and go right to Level 5. So for Nvidia to state definitively it can deliver the highest level of autonomous driving starting next year is pretty staggering — and maybe a little bit reckless.

Presently, self-driving cars that don’t require any human intervention are only theoretical. This vision of the future, where the vehicle can handle every task in all possible conditions, is the one that is most appealing to futurists and tech evangelists. But it will take years, if not decades, before our roads and rules catch up to robotic cars that can roam freely without limitations.

Nvidia’s Drive PX Pegasus computing platform Nvidia In a conference call with reporters Monday, Nvidia’s executives acknowledged that these driverless cars with their Level 5-empowering GPUs will most likely first be deployed in a ride-hailing capacity in limited settings, like college campuses or airports. But as soon as their life-saving potential is realized, they expect them to be rolled out onto more public roads. “These vehicles are going to save a lot of lives,” said Danny Shapiro, senior director of automated driving at Nvidia.

The type of computers produced by Nvidia and its competitors like Intel are arguably the most important part of the driverless car. Everything the vehicle “sees” with its sensors, all of the images, mapping data, and audio material picked up by its cameras, needs to be processed by giant PCs in order for the vehicle to make split-second decisions. All this processing must be done with multiple levels of redundancy to ensure the highest level of safety. This is why so many self-driving operators prefer SUVs, minivans, and other large wheelbase vehicles: autonomous cars need enormous space in the trunk for their big “brains.”

The trunk of a self-driving Ford Fusion Sam Abuelsamid But Nvidia claims to have shrunk down its GPU, making it an easier fit for production vehicles. Pegasus contains an amount of power equivalent to “a 100-server data center in the form-factor size of a license plate,” Shapiro said.

Nvidia began working on autonomous vehicles several years ago and has racked up partnerships with dozens of automakers and suppliers racing to develop self-driving cars, including Chinese search engine giant Baidu, Toyota, Audi, Tesla, and Volvo.

Nvidia’s original architecture for self-driving cars, introduced in 2015, is a supercomputer platform called Drive PX that can process all of the data coming from the vehicle’s cameras and sensors. The platform then uses an AI algorithm-based operating system and a cloud-based, high-definition 3D map to help the car understand its environment, know its location, and anticipate potential hazards while driving. The system’s software can be updated over the air — similar to how a smartphone’s operating system is updated — making the car become smarter over time.

A more powerful next-generation computer called Drive PX 2 — along with a suite of software tools and libraries aimed at speeding up the deployment of self-driving vehicles — followed in 2016. Nvidia has continued to push its tech further with the introduction last year of Xavier, a complete system-on-a-chip processor that is essentially an AI brain for self-driving cars. And Pegasus is the equivalent of two Xavier units, plus two next-generation discrete GPUs, Nvidia says. The new system was introduced at a GPU conference in Munich, Germany on Tuesday.

Nvidia also made two additional announcements at the conference: that it was partnering with Deutsche Post DHL Group and auto supplier ZF to deploy fully autonomous delivery trucks by 2019; and that it was offering early access to its virtual “Holodeck” technology to select designers and developers. (The Verge’s Adi Robertson wrote recently about the unlimited number of VR projects using “holodeck” terminology.

theverge.com

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From: Glenn Petersen10/11/2017 6:40:43 AM
   of 1574
 
NVIDIA opens up its Holodeck VR design suite

Designers can model and interact with people, robots and objects in real time. \

Steve Dent, @stevetdent
Engadget
October 10, 2017

NVIDIA
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Hardware makers have figured out that enterprises are the best way to make money off of VR and AR, not consumers. NVIDIA, a company that does both things well but has been particularly strong on the business side lately, has just opened up its Holodeck "intelligent" VR platform to select designers and developers. First unveiled in May, it allows for photorealistic graphics, haptics, real-world physics and multi-user collaboration.

That helps engineers and designers build and interact with photorealistic people, objects and robots in a fully simulated environment. The idea is to get new hardware prototyped in as much detail as possible before building real-world models. It also allows manufacturers to start training personnel well before hardware is market-ready. For instance, NVIDIA showed how the engineers that built the Koenigsegg supercar could explore the car "at scale and in full visual fidelity" and consult in real time on design changes.

Holodeck is built on a bespoke version of Epic Games' Unreal Engine 4 and uses NVIDIA's VRWorks, DesignWorks and GameWorks. It requires some significant hardware, either an NVIDIA 1080, Quadro P600, NVIDIA 1080 Ti or Titan XP GPU, but the firm says it will eventually lower the bar. It's not clear what kind of headsets are supported, but both of the major PC models (the HTC Vive and Oculus Rift) will likely work.

NVIDIA is already using its Holodeck as a way to train AI agents in its Isaac Simulator, a photorealistic machine-learning environment. With Holodeck, NVIDIA is taking on Microsoft and its Hololens in the enterprise and design arena -- though the latter AR system is more about letting engineers interact with real and virtual objects at the same time. Another player in the simulation scene is Google with Glass Enterprise, a product aimed more at training and manufacturing than design.

All of this doesn't seem like it's going to help you game or be entertained, but there is a silver lining. Much of this very advanced tech is bound to trickle down to consumers, hopefully making VR and AR good enough to actually become popular.

engadget.com

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From: Glenn Petersen10/13/2017 11:02:00 PM
   of 1574
 
Nvidia Can Go to $250 on All the Data Center Opportunities, Says Needham

Nvidia's business in data centers has several avenues to tens of billions in revenue, including "inferencing," an emerging area of machine learning, but also selling chips to Uber and other "transportation as a service" companies, according to Rajvindra Gill of Neeedham & Co.

By Tiernan Ray
Barron's
Oct. 13, 2017 11:19 a.m. ET \

Another day, another Nvidia ( NVDA) price target increase, this one from Needham & Co.’s Rajvindra Gill, who reiterates a Buy rating, and raises his price target to $250 from $200, after attending the company’s “GTC” conference in Munich, Germany, and coming away upbeat about the prospects for the company’s data center market.

Gill’s new target beats the $220 that RBC Capital’s Mitch Steves offered yesterday on his own enthusiasm for Nvidia’s markets.

Gill talked with Nvidia CEO Jen-Hsun Huang at the event, along with other attendees, and the discussion mostly “centered around the growth drivers in data center,” he writes.

The market could be worth $21 billion to $35 billion over five years, writes Gill, in three buckets.

One big area is the current “training” market in machine learning:

Nearly all the hyperscalers, cloud and server vendors (Google, Alibaba, Cisco, Huawei, AWS, Microsoft Azure, IBM, Lenovo, Tencent) along with several A.I. startups will train on GPUs in the cloud — both internally and for their customers.

Inference, acting on the results of training, is another one, though “we are waiting to see evidence” of the GPU take up there, he writes:

The second major growth driver is inference. We estimate there are 20 million CPU nodes that will be accelerated over the next five years to support AI applications (live video on Internet, video surveillance cameras). At $500-$1,000 ASPs, we forecast the inference TAM at $10 billion to $20 billion.

And yet another part is spreading GPUs to new areas, including the “transportation as a service" companies such as Uber:

For example, Lyft or Uber could possibly deploy supercomputing GPUs to process the innumerable driving decisions needed to support AVs along with SQL databases being accelerated with AI-GPUs. Moreover, 15 of the top 500 supercomputers have GPUs. We believe over the next five years, 100% of those supercomputers will be accelerated. In a typical supercomputer node, we estimate NVDA receives $64k (8 GPUs X $8k each). This would translate to an HPC GPU TAM of ~$10BN.

barrons.com

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