Nvidia Takes Deep Learning to School

发布时间:2017-05-10 00:00
来源:EE Times

Deep learning is "transforming computing" is the message that Nvidia hopes to hammer home at its GPU Tech conference. On that theme, Nvidia has styled itself as a firebrand, catalyst and deep learning enabler and — in the long run — a deep profiteer.

Among the telltale signs that Nvidia is betting its future on this branch of artificial intelligence (AI) is the recent launch of its “Deep Learning Institute,” with plans increase the number of developers to 100,000 this year. Nvidia trained 10,000 developers in deep learning in 2016.

Over the last few years, AI has made inroads into “all parts of science,” said Greg Estes, Nvidia’s vice president responsible for developer programs. AI is becoming an integral component of “all applications ranging from cancer research, robotics, manufacturing to financial services, fraud detection and intelligent video analysis,” he noted. 

Deep learning transforms computing (Source: Nvidia)
Deep learning transforms computing (Source: Nvidia)

Nvidia wants to be known as the first resort for developers creating apps that use AI as a key component, said Estes.

“Deep learning” is in the computer science curriculum at many schools, but few universities offer a degree specifically in AI.

At its Deep Learning Institute, Nvidia plans to deliver “hands-on” training to “industry, government and academia,” according to Estes, with a  mission to “help developers, data scientists and engineers to get started in training, optimizing, and deploying neural networks to solve the real world problems in diverse disciplines.”

How can you fit AI into apps?
Kevin Krewell, principal analyst at Tirias Research, told EE Times, “The challenge of Deep Learning is that it’s just hard to get started and figuring out how to fit it into traditional app development.”

He noted, “I think Nvidia is trying to get a wider set of developers trained on how to fit machine learning (ML) into existing development programs. Unlike traditional programs where algorithms are employed to perform a task, ML is a two-stage process with a training phase and a deployment phase.”

Nvidia’s edge is that “Machine learning performs better with an accelerator like a GPU, rather than relying just on the CPU,” Krewell added.

As Nvidia readies its Deep Learning Institute, the company is also entering a host of partnership deals with AI “framework” communities and universities. Among them are Facebook, Amazon, Google (Tensor Flow), the Mayo Clinic, Stanford University and Udacity.

Such collaborations with framework vendors are critical, because every developer working on AI apps needs to have cloud and deep learning resources.

As Jim McGregor, principal analyst at Tirias Research, told us, “The most difficult thing for app developers are the cloud resources and large data sets. As an example, the mobile suppliers are promoting machine learning on their devices, but to develop apps for those devices you need cloud/deep learning resources and the data sets to train those resources, which the mobile players are not providing.”

Nvidia can provide the hardware resources and a mature software model, but “developers still need the service provider and data sets,” McGregor added.

According to Nvidia, the company is also working with Microsoft Azure, IBM Power and IBM Cloud teams to port lab content to their cloud solutions. 


  Quest Global is developing new services and solutions, based on the NVIDIA Omniverse Enterprise platform, to deliver the best 3D visualization, simulation, design collaboration, and digital twin solutions for the manufacturing and automotive industries.  Through this association, Quest Global aims to facilitate the transformation of the traditional manufacturing processes and facilities by enabling manufacturers to augment their physical production environments with large-scale, AI and IoT-enabled, digital twin counterparts. These digital twins will enable manufacturers to optimize their manufacturing, logistics, and warehouse processes, reduce waste, and unlock operational efficiencies.  “As organizations work towards enabling their manufacturing operations with predictive analysis, operational efficiencies, and innovative automation, live digital twins of factory solutions play a vital role in achieving that. We are proud to work with NVIDIA to set up an Omniverse center of excellence, with trained engineers and NVIDIA-specific labs and infrastructure. This association is a testament to our commitment towards helping our customers pursue the next frontier of innovation and solve the world’s hardest engineering problems,” said Dushyant Reddy, Global Business Head for Hi-Tech, Quest Global.  NVIDIA Omniverse Enterprise is an end-to-end 3D simulation platform that helps organizations develop and operate physically accurate, perfectly synchronized and AI-enabled digital twins. Building the factories of the future requires uniting disparate datasets from many 3D digital content creation (DCC) and simulation applications in full fidelity, a capability uniquely enabled by Omniverse Enterprise, then connecting to scalable AI platforms such as NVIDIA Isaac Sim for robotics simulation and Metropolis for vision AI applications.  “The industrial metaverse requires innovative simulation and AI capabilities to tackle today’s critical manufacturing and automotive challenges,” said Brian Harrison, Senior Director of Product Management for Omniverse Digital Twins at NVIDIA. “The collaboration between Quest Global and NVIDIA delivers workflow solutions and enhancements that take manufacturing and design collaboration to the next level.”  Quest Global — a long-standing Elite member of the NVIDIA Partner Network – is uniquely positioned to leverage its 3D simulation, engineering, and AI capabilities to help manufacturers quickly develop and harness digital twins of their production environments. The company plans to utilize the capabilities of Omniverse for its customers across industry sectors for product design, optimization and operation of factories of the future, simulation and training of robotics, synthetic data generation for AI training and much more.
2023-02-03 11:44 阅读量:1414
Nvidia stock fell as much as 19 percent Thursday after the company reported earnings for the third quarter of its 2019 fiscal year, which ended on Oct. 28.Here's how the company did:Earnings: $1.84 per share, excluding certain items, vs. $1.71 per share as expected by analysts, according to Refinitiv.Revenue: $3.18 billion, vs. $3.24 billion as expected by analysts, according to Refinitiv.With respect to guidance, Nvidia said it's expecting $2.70 billion in revenue in the fiscal fourth quarter, plus us minus 2 percent, excluding certain items. That's below the Refinitiv consensus estimate of $3.40 billion.Overall, in the fiscal third quarter, Nvidia's revenue rose 21 percent year over year, according to its earnings statement.In its fiscal second-quarter earnings, the chipmaker fell short of analyst expectations on guidance despite beating on earnings and revenue estimates. The company's cryptocurrency mining products suffered a hefty decline in that quarter, and the trend continued in the fiscal third quarter.It has become less profitable to use graphics processing units, or GPUs, for mining, according to a recent analysis by Susquehanna. To mine cryptocurrency, computers compete to solve complex math problems in exchange for a specific amount of bitcoin or ethereum. But as both currencies have sunk in value, so too has this segment of revenue for Nvidia."Our near-term results reflect excess channel inventory post the crypto-currency boom, which will be corrected," Nvidia CEO Jensen Huang is quoted as saying in a Thursday press release. In the fiscal third quarter Nvidia's revenue from original equipment manufacturers and intellectual property totaled $148 million, which was down 23 percent year over year but above the FactSet consensus estimate of $102 million. Nvidia chocked up the decline to "the absence of cryptocurrency mining" in its earnings statement.In the quarter Nvidia had a $57 million charge related to older products because of the decrease in demand for cryptocurrency mining."Our Q4 outlook for gaming reflects very little shipment in the midrange Pascal segment to allow channel inventory to normalize," Nvidia's chief financial officer, Colette Kress, told analysts on a conference call after the company announced its results.It will take one to two quarters to go through the extra inventory, Huang said on the call."This is surely a setback, and I wish we had seen it earlier," he said.Inventory issues also affect other brands, Huang said. AMD stock fell 5 percent in extended trading on Thursday.Nvidia's gaming business segment generated $1.76 billion in revenue in the quarter, below the $1.89 billion FactSet consensus estimate.Nvidia's data center segment came in at $792 million in revenue, lower than the $821 million estimate.Revenue for the company's professional visualization business segment was $305 million, surpassing the $284 million estimate.Nvidia, like most other tech stocks, was hit hard in October, which was the worst month for the Nasdaq Composite Index since 2008. The stock is now up 4 percent since the beginning of the year.
2018-11-16 00:00 阅读量:1131
Nvidia is in Munich this week to declare war that it is coming after the advanced driver assistance system (ADAS) market. The GPU company is now pushing its AI-based Nvidia Drive AGX Xavier System — originally designed for Level 4 autonomous vehicles — down to Level 2+ cars.In a competitive landscape already crowded with ADAS solutions provided by rival chip vendors such as NXP, Renesas, and Intel/Mobileye, Nvidia is boasting that its GPU-based automotive SoC isn’t just a “development platform” for OEMs to prototype their self-driving vehicles.At the company’s own GPU Technology Conference (GTC) in Europe, Nvidia announced that Volvo cars will be using the Nvidia Drive AGX Xavier for its next generation of ADAS vehicles, with production starting in the early 2020s.NVIDIA's Drive AGX Xavier will be designed into Volvo's ADAS L2+ vehicles. Henrik Green (left), head of R&D of Volvo Cars, with Nvidia CEO Jensen Huang on stage at GTC Europe in Munich. (Photo: Nvidia)Danny Shapiro, senior director of automotive at Nvidia, told us, “Volvo isn’t doing just traditional ADAS. They will be delivering wide-ranging features of ‘Level 2+’ automated driving.”By Level 2+, Shapiro means that Volvo will be integrating “360° surround perception and a driver monitoring system” in addition to a conventional adaptive cruise control (ACC) system and automated emergency braking (AEB) system.Nvidia added that its platform will enable Volvo to “implement new connectivity services, energy management technology, in-car personalization options, and autonomous drive technology.”It remains unclear if car OEMs designing ADAS vehicles are all that eager for AI-based Drive AGX Xavier, which is hardly cheap. Shapiro said that if any car OEMs or Tier Ones are serious about developing autonomous vehicles, taking an approach that “unifies ADAS and autonomous vehicle development” makes sense. The move allows carmakers to develop software algorithms on a single platform. “They will end up saving cost,” he said.Phil Magney, founder and principal at VSI Labs, agreed. “The key here is that this is the architecture that can be applied to any level of automation.” He said, “The processes involved in L2 and L4 applications are largely the same. The difference is that L4 would require more sensors, more redundancy, and more software to assure that the system is safe enough even for robo-taxis, where you don’t have a driver to pass control to when the vehicle encounters a scenario that it cannot handle.”Better than discrete ECUsAnother argument for the use of AGX for L2+ is that the alternative requires the use of multiple discrete ECUs. Magney said, “An active ADAS system (such as lane keeping, adaptive cruise, or automatic emergency braking) requires a number of cores fundamental to automation. Each of these tasks requires a pretty sophisticated hardware/software stack.” He asked, “Why not consolidate them instead of having discrete ECUs for each function?”Scalability is another factor. Magney rationalized, “A developer could choose AGX Xavier to handle all these applications. On the other hand, if you want to develop a robo-taxi, you need more sensors, more software, more redundancy, and higher processor performance … so you could choose AGX Pegasus for this.”Is AGX Xavier safer?Shapiro also brought up safety issues.He told us, “Recent safety reports show that many L2 systems aren’t doing what they say they would do.” Indeed, in August, the Insurance Institute for Highway Safety (IIHS) exposed “a large variability of Level 2 vehicle performance under a host of different scenarios.” An EE Times story entitled “Not All ADAS Vehicles Created Equal” reported that some [L2] systems can fail under any number of circumstances. In some cases, certain models equipped with ADAS are apparently blind to stopped vehicles and could even steer directly into a crash.Nvidia’s Shapiro implied that by “integrating more sensors and adding more computing power” that runs robust AI algorithms, Volvo can make their L2+ cars “safer.”On the topic of safety, Magney didn’t necessarily agree. “More computing power doesn’t necessarily mean that it is safer.” He noted, “It all depends on how it is designed.”Lane keeping, adaptive cruise, and emergency braking for L2 could rely on a few sensors and associated algorithms while a driver at the wheel manages events beyond the system’s capabilities.However, the story is different with a robo-taxi, explained Magney. “You are going to need a lot more … more sensors, more algorithms, some lock-step processing, and localization against a precision map.” He said, “For example, if you go from a 16-channel LiDAR to a 128-channel LiDAR for localization, you are working with eight times the amount of data for both your localization layer as well as your environmental model.”Competitive landscapeBut really, what does Nvidia have that competing automotive SoC chip suppliers don’t?Magney, speaking from his firm VSI Labs’ own experience, said, “The Nvidia Drive development package has the most comprehensive tools for developing AV applications.”He added, “This is not to suggest that Nvidia is complete and a developer could just plug and play. To the contrary, there is a ton of organic codework necessary to program, tune, and optimize the performance of AV applications.”However, he concluded that, in the end, “you are going to be able to develop faster with Nvidia’s hardware/software stack because you don’t have to start from scratch. Furthermore, you have DRIVE Constellation for your hardware-in-loop simulations where you can vastly accelerate your simulation testing, and this is vital for testing and validation.”
2018-10-11 00:00 阅读量:1115
Nvidia has signed a big chip deal with SK Hynix, putting the South Korean memory manufacturing giant in line for a large slice of Nvidia’s future financial successes with AI and data centre sales. And potentially with future GTX 1180-shaped graphics cards too.SK Hynix is the world’s second largest memory fab behind Samsung, and number four in the world for semiconductor sales behind TSMC, Intel, and Samsung - who is, again, in the very top spot.The memory manufacturer has already been enjoying a pretty stellar year, with huge worldwide memory demand that has seen system memory prices soar. But since the deal with Nvidia has entered the public domain, SK Hynix’s stocks have gone supersonic.Won’t settle for anything less than 4K? Don’t worry, the best Nvidia graphics cards are only a click away.Stocks are currently valued at 95,300 KRW apiece, toppling the previous high of the year of 90,700 KRW back in March. As a result of the Nvidia deal, this is the highest the semiconductor manufacturer’s stocks have been in 17 years.So what chips are SK Hynix providing Nvidia? From what we know of Nvidia’s next-generation of graphics cards (which is still very little, despite the recent Nvidia GTX 1180 rumours), we can only assume that SK Hynix will be providing GDDR6 for Nvidia’s GTX 1180 and further next-generation graphics cards. SK Hynix confirmed that its speedy GDDR6 memory modules were available for purchase earlier in the year, and this memory standard has been the most likely candidate to power Nvidia’s next gaming cards for some time.Nvidia’s current Volta generation Titan V utilises 12GB of HBM2 memory (which SK Hynix also make and assumedly supply to Nvidia in some capacity), although this on-package memory solution has been largely deemed too expensive and unnecessary for gaming cards - unfortunately, AMD’s RX Vega graphics cards had to find this out first hand.It’s all kicking off for SK Hynix and the company’s financials, and is likely the icing on the cake of what has already been an extremely successful year for the memory fab. Hopefully this newfound memory deal indicates signs of life within the Nvidia supply chain toward large-scale production of new product, and a good omen for gamers patiently waiting it out for new graphics cards from the green team.
2018-05-24 00:00 阅读量:1513
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