Tag Archives: Jen-Hsun Huang

Everything Jensen Huang Revealed @ NVIDIA Computex 2018!

Everything Jensen Huang Revealed @ NVIDIA Computex 2018!

At an exclusive NVIDIA Computex 2018 press conference, NVIDIA CEO Jensen Huang revealed a slew of new computing products. But first, let us break your hearts and tell you upfront that he did not announce the long-awaited replacements for NVIDIA Pascal graphics cards…

But he did show off a number of interesting products, and we have the opportunity to take some pictures and videos of the new NVIDIA HGX-2, NVIDIA Drive Xavier, NVIDIA Drive Pegasus and NVIDIA Jetson Xavier!

We have more than 1.5 hours of videos in this article, and lots of pictures from the event, so grab a drink, sit back and enjoy!

 

The NVIDIA Computex 2018 Press Conference

As there are limited room, this is an invitation-only press conference at the Grand Hyatt Taipei.

What a crowd… 30 minutes before the NVIDIA Computex 2018 press conference kicks off! 

Guess who’s here too? 

Unbelievable – there was time to get a selfie with Jensen himself before the press conference 

He seemed to have a good time tossing cookies to members of the media.

Alright… now, let’s get to the actual press conference, which lasted over 1.5 hours and was punctuated by several emergency sirens!

Next Page > What Jensen Huang Revealed @ NVIDIA Computex 2018

 

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What Jensen Huang Revealed @ NVIDIA Computex 2018

Jensen’s presentation was about an hour long, followed by a 30 minute Q&A session. Here are the full videos of his presentation and Q&A session.

If you are looking for news on NVIDIA Turing – the next GeForce – skip to minute 13:02 of the Q&A session video. This was what Jensen said :

When is the next GeForce? I’m going to invite you guys. Don’t worry!

No only will you be invited, there will probably be lunch.

A little hint? It’s a long time from now. It’s a long time from now.

In the next page, we have the blow-by-blow account of Jensen’s announcements, as well as pictures. In the meantime, here are our video tour of some of the new NVIDIA products the Jensen just revealed.

 

The NVIDIA Jetson Xavier Up Close!

The NVIDIA Jetson Xavier is a new deep learning computer designed for intelligent machines, e.g. robots. It delivers 30 teraflops of performance in a small form factor. The developer’s kit will see an early release in August 2018, with a price of US$ 1,299.

 

The NVIDIA Drive Xavier + Drive Pegasus Up Close!

Both NVIDIA Drive Xavier and NVIDIA Drive Pegasus use the new NVIDIA Xavier SoC – the world’s largest System-On-Chip, with 9 billion transistors!

The Drive Xavier uses a single Xavier SoC, with 30 teraflops of computing performance. The Drive Pegasus, on the other hand, has two Xavier SoCs and two Tesla V100 GPUs, with a total computing performance of 280 teraflops!

 

The NVIDIA HGX-2 Up Close!

The NVIDIA HGX-2 is a new architecture for hyperscale servers, delivering 1 petaflops on a single board, or 2 petaflops with two boards! Each HGX-2 board has eight Tesla V100 GPUs with 32 GB of HBM2 memory per module,

Next Page > The NVIDIA Computex 2018 In Pictures

 

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The NVIDIA Computex 2018 Press Conference In Pictures

Jensen starts by showing off the new Tesla V100 GPU with 32 GB of HBM2 memory. 16 of these go into the new NVIDIA DGX-2 – the world’s largest GPU node with 2 teraflops of performance!

No… Jensen is not serving pizza 

That is the NVIDIA HGX-2 – world’s most powerful motherboard with eight Tesla V100 GPUs delivering 1 teraflops of computing power!

Jensen announces NVIDIA Xavier – the world’s first AI processor for intelligent machines.

Jensen shows off the NVIDIA Drive Pegasus – a complete intelligent platform powered by the new NVIDIA Xavier SoC.

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Introducing NVIDIA Jetson Xavier board for the NVIDIA ISAAC robotics platform!

This is the complete NVIDIA Jetson Xavier developer’s kit computer.

Here is a closer look at the NVIDIA Xavier board (middle), with its module (right), and the complete computer (right).

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Tech ARP Computex 2018 Live Coverage – Day One

Team ARP is on the ground at Taipei to cover Computex 2018 LIVE on the Tech ARP Facebook page and this article. Computex 2018 begins today, June 4, 2018 and we will be here for three full days. So join us for our live coverage every day, until June 6!

 

This article will be updated live with pictures and details as and when they come in. Videos will be inserted at the end of every night, as they take time to process and upload.

 

Here We Go!

We are flying off to Taipei on 3 June 2018, packing five outstanding smartphones to help us cover Computex 2018!

 

Day 1 Schedule

The first day of Computex 2018 is the busiest day of the event. We are scheduled for back-to-back major events from morning until evening!

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Early Morning : Registration + Taipei 101 Convention Centre Walkabout

1:00 PM – 2:00 PM : NVIDIA Computex 2018 Press Conference

3:00 PM – 4:00 PM : NVIDIA Consumer Tech Briefing

7:00 PM – 10:00 PM : Corsair Computex Party!

 

Computex 2018 Kicks Off!

Finally have the license to enter #Computex2018!

Check out the gaming systems that Western Digital is preparing to show off the WD Black NVMe SSD tomorrow!

At the Grand Hyatt Taipei, we kick off Computex 2018 with the NVIDIA Press Conference!

What a crowd… 30 mins before the NVIDIA Press Conference kicks off! 

Guess who’s here too? 

Not NVIDIA cookies but got it from Jensen himself. LOL 

Got a selfie with Jensen before the press conference 

Jensen tossing cookies… 

Next Page > NVIDIA Computex 2018 Press Conference

 

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NVIDIA Computex 2018 Press Conference

NVIDIA CEO Jensen Huang kicks off the press conference

Jensen starts by showing off the new Tesla V100 GPU with 32 GB of HBM2 memory. 16 of these go into the new NVIDIA DGX-2 – the world’s largest GPU node with 2 teraflops of performance!

No… Jensen is not serving pizza 

That is the NVIDIA HGX-2 – world’s most powerful motherboard with eight Tesla V100 GPUs delivering 1 teraflops of computing power!

Jensen announces NVIDIA Xavier – the world’s first AI processor for intelligent machines.

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Jensen shows off the NVIDIA Drive Pegasus – a complete intelligent platform powered by the new NVIDIA Xavier SoC.

Introducing NVIDIA Jetson Xavier board for the NVIDIA ISAAC robotics platform!

This is the complete NVIDIA Jetson Xavier developer’s kit computer.

Edward kindly took this photo of me getting a selfie with Jensen. Thanks!

Next Page > 2018 NVIDIA Consumer Tech Briefing, Corsair Computex Party!

 

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2018 NVIDIA Consumer Tech Briefing

The BFGD is… coming soon!

This is how you do live ray tracing on a single system… with 4 Tesla GPUs in an NVIDIA DGX-2 system!

 

Andrew Saunders compares the new NVIDIA G-SYNC HDR against the current G-SYNC.

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Corsair Computex Party!

Pirates are known to throw a great party! 

Cocktail menu at the Corsair Computex Kick-off Party!

Honey-glazed ham! 

Oh guess who showed up at the Corsair party? 

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Jensen Huang Is Fortune 2017 Businessperson Of The Year!

Fortune magazine just named NVIDIA President & CEO Jensen Huang as its 2017 Businessperson of the Year! Congratulations, Jensen and NVIDIA!

 

Jensen Huang Is 2017 Businessperson Of The Year

Huang joins a very exclusive group of business leaders so honoured, which include Facebook CEO Mark Zuckerberg, Alphabet CEO Larry Page, SpaceX CEO Elon Musk, Amazon CEO Jeff Bezos and Starbucks CEO Howard Schultz.

In the feature story of the latest issue, Fortune‘s Andrew Nusca writes, “The co-founder and CEO of semiconductor and software maker NVIDIA saw the future of computing more than a decade ago and began developing products that could power the Artificial Intelligence era.”

“[NVIDIA] doesn’t make a chat app or a search service or another kind of technology meant to appeal to the average smartphone-toting consumer,” Nusca wrote. “No, NVIDIA makes the muscular mystery stuff that powers all of it.”

 

NVIDIA’s Secret Sauce – Culture

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The story Fortune tells about NVIDIA may be the closest look yet at the culture of the company that’s put itself at the center of the AI revolution sweeping the globe.

“The company’s secret sauce? Its culture,” Nusca writes. “For a publicly traded technology company with more than 11,000 employees, Nvidia is surprisingly tight-knit.”

“It’s a credit to the many long-serving staffers who remain at the company (badge numbers are issued in serial; the lower the number, the longer the tenure) and the business battles they’ve endured together,” Nusca writes.

“It’s also the product of a founder CEO who embraces community, strategic alignment, and a core value system that promotes the pursuit of excellence through intellectual honesty,” Nusca adds.

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Jensen Huang Reveals NVIDIA Isaac, Max-Q, Volta, HGX & More!

The first major event at Computex Taipei 2017 was the Powering The AI Revolution keynote by NVIDIA CEO Jensen Huang. Although the keynote was heavy on artificial intelligence technologies like NVIDIA Isaac and NVIDIA Volta, Jensen also announced other technologies like GeForce GTX with Max-Q Design.

 

NVIDIA Isaac, Max-Q, Volta, HGX & More!

In this 90 minute long keynote, Jensen reveals the future of artificial intelligence and robotics. In that vision, the GPU is taking over from the CPU in delivering the petaflops of computing power required to deliver artificial intelligence.

 

He also reveals the new NVIDIA technologies that will power the next-generation AI applications – the new NVIDIA Tesla V100, which is the largest GPU ever made, and the NVIDIA HGX server that hosts eight of these GPUs to deliver almost 1 petaflops of compute performance in a single chassis!

Jensen also shows how the NVIDIA Isaac Initiative allows robots to self-learn in a virtual environment, using nothing more than the NVIDIA Jetson 2 module. Watch how AI on the edge delivers smarter intelligence with minimal work.

Whether you are a scientist in these fields, or just geeks like us, you will enjoy listening to his views and NVIDIA’s endeavours in those technologies.

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Our Blow-by-Blow Account Of The Keynote

First stop of the day – the NVIDIA AI Forum keynote. Jensen Huang is upstairs polishing his keynote while the crowd grows downstairs.

With the Japanese contingent waiting to storm up the escalator to the 3rd floor hall.

Everyone’s green as an ogre…

Next Page > The NVIDIA AI Forum Keynote by Jensen Huang

 

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The NVIDIA AI Forum Keynote Begins!

NVIDIA Jensen Huang arrives for his eagerly awaited keynote on AI and related NVIDIA technologies.

Jensen Huang announces Project Holodeck! It’s basically virtual reality for engineers.

 

GeForce GTX With Max-Q Design

Jensen Huang shows off a notebook using the new GeForce GTX with Max Q design.

 

The NVIDIA Tesla V100 (NVIDIA Volta)

Jensen Huang introduces Volta – the new Tesla V100 accelerator. That’s 120 TFLOPS of compute performance courtesy of 5,120 CUDA cores.

And this is the NVIDIA HGX server with EIGHT NVIDIA Tesla V100 accelerators producing 960 TFLOPS!

Jensen Huang showing off the new Tesla V100 accelerator module. It is the largest GPU ever built!

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The NVIDIA GPU Cloud

The NVIDIA GPU Cloud is revealed!

 

The NVIDIA Isaac Initiative

NVIDIA also introduced their robotics platform – the Isaac Initiative, named after Isaac Asimov.

The NVIDIA Isaac Initiative will be built around NVIDIA Jetson 2 and the Isaac Robot Simulator, which allows the robots to train themselves in a virtual world.

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The Computex Taipei 2017 Live Coverage (Day 1)

Team ARP is on the ground at Taipei to cover Computex Taipei 2017 LIVE on the Tech ARP Facebook page and this article. Computex Taipei 2017 begins today, May 30, 2017 and will run for five full days. So join us for our live coverage every day, until June 2!

This article will be updated live with pictures and details as and when they come in. Videos will be inserted at the end of every night, as they take time to process and upload.

 

Day 1 Schedule

The first day of Computex 2017 is the busiest day of the event. We are scheduled for back-to-back major events from morning until evening!

11:30 AM – 1 PM : NVIDIA AI Keynote by NVIDIA CEO,  Jensen Huang

2:00 PM – 3:00 PM : Intel Keynote by Gregory Bryant, VP & GM of the Intel Client Computing Group

3:30 PM – 4:30 PM : Dell Computex 2017 Press Conference

5:00 PM – 6:00 PM : NVIDIA Computex 2017 Press Conference

 

Computex Taipei 2017 Kicks Off!

Finally… after a damn long delay on take off, and a missed approach on landing, I’m finally on Taiwanese soil! ?

The ticket into Computex 2017…

The entire first day will be spent in the Taipei 101 half of Computex Taipei 2017.

This year, Taipei is not as hot as last year’s inferno of a weather. Phew!

Next Page > NVIDIA AI Keynote by NVIDIA CEO Jensen Huang

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NVIDIA AI Keynote by NVIDIA CEO Jensen Huang

Catch our full coverage and the complete video of this keynote here!

First stop of the day – the NVIDIA AI Forum keynote. Jensen Huang is upstairs polishing his keynote while the crowd grows downstairs.

With the Japanese contingent waiting to storm up the escalator to the 3rd floor hall.

Everyone’s green as an ogre…

NVIDIA Jensen Huang arrives for his eagerly awaited keynote on AI and related NVIDIA technologies.

Jensen Huang announces Project Holodeck!

Jensen Huang shows off a notebook using the new GeForce GTX with Max Q design.

Jensen Huang introduces Volta – the new Tesla V100 accelerator. That’s 120 TFLOPS of compute performance courtesy of 5,120 CUDA cores.

And this is the NVIDIA HGX server with EIGHT NVIDIA Tesla V100 accelerators producing 960 TFLOPS!

Jensen Huang showing off the new Tesla V100 accelerator module. It is the largest GPU ever built!

The NVIDIA GPU Cloud is revealed!

NVIDIA also introduced their robotics platform – the Isaac Initiative, named after Isaac Asimov.

The Isaac Initiative will be built around NVIDIA Jetson 2 and the Isaac Robot Simulator, which allows the robots to train themselves in a virtual world.

Catch our full coverage and the complete video of this keynote here!

Next Page > Intel Keynote by Gregory Bryant

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Intel Keynote by Gregory Bryant

Catch our full coverage and the complete video of this keynote here!

Gregory Bryant from Intel announces the Intel Xeon Platinum family of scalable server processors.

Intel also announced the Low Power Series of their 2nd Gen 3D NAND SSD drives.

Introducing the Intel Compute Card!

The Intel Compute Card is a modular computer that will fit into a variety of devices. You can pull it out of one device and slot it into another.

ASUS Chairman Jonney Shih shows off their secret project Kukuna.

Intel also showed off their new Optane technology.

Intel officially announces the Intel Core-X Series Processor Family with up to 18 cores per CPU!

Intel also announced the Intel Core i9 processors, with X-Series and Extreme editions.

Catch our full coverage and the complete video of this keynote here!

Next Page > Dell Computex 2017 Press Conference

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Dell Computex 2017 Press Conference

Catch our full coverage and the complete video of this keynote here!

At the Dell Computex 2017 press conference, Ray Mak showed off the first and only AOI powered by the AMD Ryzen 7 processor – the Dell Inspiron 27 7000.

Ray also showed off a smaller AMD A10-powered AIO model – the Dell Inspiron 24 5000.

AMD President & CEO Dr. Lisa Su even came up to talk about their partnership with Dell.

Catch our full coverage and the complete video of this keynote here!

Next Page > NVIDIA Computex 2017 Press Conference

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NVIDIA Computex 2017 Press Conference

At the NVIDIA pre-briefing showcase, they showed off the NVIDIA DGX Station.

NVIDIA also demoed the new NVIDIA GPU Cloud.

They also showed off a closer demo of the Isaac training simulation for robots.

After the briefing on NVIDIA GeForce GTX with Max-Q, NVIDIA showed off their upcoming G-Sync HDR monitor.

From the NVIDIA Max-Q tech briefing – it’s about peak EFFICIENCY, not maximum performance.

NVIDIA WhisperMode is also about efficiency, but will be available by end of June for all GeForce 10 series graphics cards.

Here are the first 3 notebooks to debut with the NVIDIA Max-Q technology.

Next > The Computex 2017 Live Coverage (Day 2)

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NVIDIA : Accelerating Artificial Intelligence With GPUs

by Jen-Hsun Huang

 

The Big Bang

For as long as we have been designing computers, AI has been the final frontier. Building intelligent machines that can perceive the world as we do, understand our language, and learn from examples has been the life’s work of computer scientists for over five decades. Yet, it took the combination of Yann LeCun’s Convolutional Neural Net, Geoff Hinton’s back-propagation and Stochastic Gradient Descent approach to training, and Andrew Ng’s large-scale use of GPUs to accelerate Deep Neural Networks (DNNs) to ignite the big bang of modern AI — deep learning.

At the time, NVIDIA was busy advancing GPU-accelerated computing, a new computing model that uses massively parallel graphics processors to accelerate applications also parallel in nature.  Scientists and researchers jumped on to GPUs to do molecular-scale simulations to determine the effectiveness of a life-saving drug, to visualize our organs in 3D (reconstructed from light doses of a CT scan), or to do galactic-scale simulations to discover the laws that govern our universe. One researcher, using our GPUs for quantum chromodynamics simulations, said to me: “Because of NVIDIA’s work, I can now do my life’s work, in my lifetime.” This is wonderfully rewarding. It has always been our mission to give people the power to make a better future. NVIDIA GPUs have democratized supercomputing and researchers have now discovered that power.

Photo credit : Virtual Desktop

In 2011, AI researchers discovered NVIDIA GPUs. The Google Brain project had just achieved amazing results — it learned to recognize cats and people by watching movies on YouTube. But it required 2,000 CPUs in servers powered and cooled in one of Google’s giant data centers. Few have computers of this scale. Enter NVIDIA and the GPU. Bryan Catanzaro in NVIDIA Research teamed with Andrew Ng’s team at Stanford to use GPUs for deep learning. As it turned out, 12 NVIDIA GPUs could deliver the deep-learning performance of 2,000 CPUs. Researchers at NYU, the University of Toronto, and the Swiss AI Lab accelerated their DNNs on GPUs. Then, the fireworks started.

 

Deep Learning Performs Miracles

Alex Krizhevsky of the University of Toronto won the 2012 ImageNet computer image recognition competition. Krizhevsky beat — by a huge margin — handcrafted software written by computer vision experts. Krizhevsky and his team wrote no computer vision code. Rather, using deep learning, their computer learned to recognize images by itself. They designed a neural network called AlexNet and trained it with a million example images that required trillions of math operations on NVIDIA GPUs. Krizhevksy’s AlexNet had beaten the best human-coded software.

The AI race was on. By 2015, another major milestone was reached.

Using deep learning, Google and Microsoft both beat the best human score in the ImageNet challenge. Not a human-written program, but a human. Shortly thereafter, Microsoft and the China University of Science and Technology announced a DNN that achieved IQ test scores at the college post-graduate level.

Then Baidu announced that a deep learning system called Deep Speech 2 had learned both English and Mandarin with a single algorithm. And all top results of the 2015 ImageNet competition were based on deep learning, running on GPU-accelerated deep neural networks, and many beating human-level accuracy.

In 2012, deep learning had beaten human-coded software. By 2015, deep learning had achieved “superhuman” levels of perception.

 

A New Computing Platform for a New Software Model

Computer programs contain commands that are largely executed sequentially. Deep learning is a fundamentally new software model where billions of software-neurons and trillions of connections are trained, in parallel.

Running DNN algorithms and learning from examples, the computer is essentially writing its own software. This radically different software model needs a new computer platform to run efficiently. Accelerated computing is an ideal approach and the GPU is the ideal processor.

As Nature recently noted, early progress in deep learning was “made possible by the advent of fast graphics processing units (GPUs) that were convenient to program and allowed researchers to train networks 10 or 20 times faster.”

A combination of factors is essential to create a new computing platform — performance, programming productivity, and open accessibility.

Performance. NVIDIA GPUs are naturally great at parallel workloads and speed up DNNs by 10-20x, reducing each of the many training iterations from weeks to days. We didn’t stop there. By collaborating with AI developers, we continued to improve our GPU designs, system architecture, compilers, and algorithms, and sped up training deep neural networks by 50x in just three years — a much faster pace than Moore’s Law. We expect another 10x boost in the next few years.

Programmability. AI innovation is on a breakneck pace. Ease of programming and developer productivity are paramount. The programmability and richness of NVIDIA’s CUDA platform allow researchers to innovate quickly — building new configurations of CNNs, DNNs, deep inception networks, RNNs, LSTMs, and reinforcement learning networks.

Accessibility. Developers want to create anywhere and deploy everywhere. NVIDIA GPUs are available all over the world, from every PC OEM; in desktops, notebooks, servers, or supercomputers; and in the cloud from Amazon, IBM, and Microsoft. All major AI development frameworks are NVIDIA GPU accelerated — from internet companies, to research, to startups. No matter the AI development system preferred, it will be faster with GPU acceleration.

We have also created GPUs for just about every computing form-factor so that DNNs can power intelligent machines of all kinds. GeForce is for PC.  Tesla is for cloud and supercomputers. Jetson is for robots and drones. And DRIVE PX is for cars. All share the same architecture and accelerate deep learning.

 

Every Industry Wants Intelligence

Baidu, Google, Facebook, Microsoft were the first adopters of NVIDIA GPUs for deep learning. This AI technology is how they respond to your spoken word, translate speech or text to another language, recognize and automatically tag images, and recommend newsfeeds, entertainment, and products that are tailored to what each of us likes and cares about.

Startups and established companies are now racing to use AI to create new products and services, or improve their operations. In just two years, the number of companies NVIDIA collaborates with on deep learning has jumped nearly 35x to over 3,400 companies.

Industries such as healthcare, life sciences, energy, financial services, automotive, manufacturing, and entertainment will benefit by inferring insight from mountains of data. And, with Facebook, Google, and Microsoft opening their deep-learning platforms for all to use, AI-powered applications will spread fast. In light of this trend, Wired recently heralded the “rise of the GPU.”

Self-driving cars. Whether to augment humans with a superhuman co-pilot, or revolutionize personal mobility services, or reduce the need for sprawling parking lots within cities, self-driving cars have the potential to do amazing social good. Driving is complicated. Unexpected things happen. Freezing rain turns the road into a skating rink. The road to your destination is closed. A child runs out in front of the car.

You can’t write software that anticipates every possible scenario a self-driving car might encounter. That’s the value of deep learning; it can learn, adapt, and improve. We are building an end-to-end deep learning platform called NVIDIA DRIVE PX for self-driving cars — from the training system to the in-car AI computer. The results are very exciting.  A future with superhuman computer co-pilots and driverless shuttles is no longer science fiction.

Robots. FANUCa leading manufacturing robot maker, recently demonstrated an assembly-line robot that learned to “pick” randomly oriented objects out of a bin. The GPU-powered robot learned by trial and error. This deep-learning technology was developed by Preferred Networks, which was recently featured in a The Wall Street Journal article headlined, “Japan Seeks Tech Revival with Artificial Intelligence.”

Healthcare and Life Sciences. Deep Genomics is applying GPU-based deep learning to understand how genetic variations can lead to disease. Arterys uses GPU-powered deep learning to speed analysis of medical images. Its technology will be deployed in GE Healthcare MRI machines to help diagnose heart disease. Enlitic is using deep learning to analyze medical images to identify tumors, nearly invisible fractures, and other medical conditions.

These are just a handful of examples. There are literally thousands.

 

Accelerating AI with GPUs: A New Computing Model

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Deep-learning breakthroughs have sparked the AI revolution. Machines powered by AI deep neural networks solve problems too complex for human coders. They learn from data and improve with use. The same DNN can be trained by even non-programmers to solve new problems. Progress is exponential. Adoption is exponential.

And we believe the impact to society will also be exponential. A recent study by KPMG predicts that computerized driver assistance technologies will help reduce car accidents 80% in 20 years — that’s nearly 1 million lives a year saved. Deep-learning AI will be its cornerstone technology.

The impact to the computer industry will also be exponential. Deep learning is a fundamentally new software model. So we need a new computer platform to run it — an architecture that can efficiently execute programmer-coded commands as well as the massively parallel training of deep neural networks. We are betting that GPU-accelerated computing is the horse to ride. Popular Science recently called the GPU “the workhorse of modern A.I.” We agree.

 

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