GTC 2015 Highlights

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2. 4,000 guests • 550 talks • 175 posters “At the NVIDIA GPU Developer’s conference this week I’ll be thinking about the future and wondering if I’m not already in it.” —TechZone 2

3. TITAN X DIGITS DevBox GPU Deep Learning Platform Pascal — 10x Maxwell For Deep Learning NVIDIA DRIVE PX Deep Learning Platform for Self-Driving Cars The World’s Fastest GPU GTC 2015 focused on the promising field of deep learning. And we made four major announcements that will fuel its advance. 3

4. Our first announcement, TITAN X. The world’s fastest GPU, TITAN X boasts 8 billion transistors, 3,072 CUDA cores, and 12GB of memory. It can reach 7 teraflops of single-precision performance. “NVIDIA has now introduced four unanswered graphics cards into the market since AMD’s Radeon 285 in August 2014.” — Forbes “Let’s skip the foreplay. NVIDIA’s TITAN X is the best single-GPU graphics card on the market, and a remarkable feat of engineering. This is an inarguable conclusion.” — Forbes 4

5. To illustrate the performance of TITAN X, as well as the state of the art in real- time graphics, we showed Epic’s latest Unreal Engine 4 demo, Kite. But TITAN X is also a breakthrough for deep learning research, enabling data scientists to train their networks in a fraction of the time it used to take. 5

6. NVIDIA GPUs have been broadly adopted in deep learning, a branch of artificial intelligence. Deep learning has been ignited by the convergence of three trends: the flood of data brought by web services companies, recent algorithm breakthroughs, and the ability to compute massive amounts of data with GPUs. Today, machines are being trained to recognize images, text and speech. But this is just the tip of the iceberg. The world’s largest and most innovative companies are deploying deep learning across a variety of applications. In 2012, GPUs enabled a breakthrough in the ImageNet Challenge, the World Cup of deep learning and computer vision. GPUs have recently enabled machines to outperform humans at this task. 6

7. We showcased leading-edge research in deep learning from Andrej Karpathy of Stanford. His work combines two neural networks — one trained for image recognition, one for language processing. Connected “like LEGOs,” the neural networks can not only classify the objects in a photo, i.e., “bird” or “branch,” but also describe them in the context of the scene. 7

8. Our second announcement, DIGITS DevBox. To fuel the advance of deep learning research, we created a very powerful box. “The DIGITS DevBox is comprised of both DIGITS software and a quartet of TITAN X GPUs — not to mention several popular deep learning frameworks — altogether of which promises up to four times faster development.” — ZDNet 8

9. Our third announcement, our latest GPU roadmap. “NVIDIA also gave details of a future GPU technology, dubbed Pascal...the technology will be particularly suited for humanlike computer chores known by the phrase ‘deep learning,’ offering a tenfold speed up in such calculations.” — The Wall Street Journal 9

10. Every major automaker in the world is working toward self-driving cars. Perhaps the biggest challenge facing them today is the ability for cars to navigate complex, urban situations where human drivers make decisions based on nuances and clues. What may appear to be “free space” for a car to drive through can change in a heartbeat. For example, if a school bus stops on the other side of the road, or if the door of a parked car opens suddenly. For humans, the right response becomes second nature with life experience. But there are too many possibilities to hard code into machines. Deep learning offers a way to augment traditional techniques to pave the way toward self-driving cars. 10

11. Our fourth announcement, DRIVE PX. A self-driving car computer, DRIVE PX can augment traditional computer vision techniques by powering a deep neural network onboard the car. The work builds on Project DAVE: research by Urs Muller, chief technologist of autonomous driving at NVIDIA, and Yann LeCun, director of AI Research at Facebook, when they collaborated at DARPA. “The notion is that with powerful enough hardware, self-driving vehicles will be better able to recognize what they’re seeing, learn from the environment and make the right decisions.” — re/code 11

12. “NVIDIA Steps on the Gas” — The Wall Street Journal “Tesla and NVIDIA are among the small set of Silicon Valley companies leading the transformation of 21st century car technology.” — Fortune “The days of humans driving their own cars are numbered, according to Elon Musk… NVIDIA's work will be a ‘big enabler’ for Tesla's efforts.” — Mashable 12

13. The theme of deep learning carried through our guest keynotes. Jeff Dean, senior fellow at Google, described how the company is using GPU-powered deep neural networks to bring greater levels of intelligence to image, text, and speech recognition. He also highlighted work done by the recently acquired Deep Mind. Using Atari video games, the researchers trained a network to not just classify, but take actions in an environment. Ultimately, the network beat a series of games and the work earned the cover of Nature magazine. “We love GPU cards. We just use a lot of them.” — Jeff Dean, Google 13

14. Andrew Ng, widely recognized as a leading thinker in deep learning and currently chief scientist at Baidu, China’s largest search engine, rounded out the conference with his keynote. Ng highlighted recent work on Baidu’s Deep Speech engine, which uses deep learning to recognize and process voice commands even in noisy environments. The GPU-powered neural network trained on more than 100,000 hours of speech samples to deliver the lowest error rates ever seen in this field of research. 14

15. More than 550 talks were presented on the wide variety of fields and industries that GPUs are disrupting, from cancer research to the exploration of Mars. Our exhibit hall showcased the latest innovations from our partners. And our Emerging Companies Summit once again highlighted the work of startups. Artomatix, this year’s winner of the $100,000 Early Stage Challenge, is using machine learning and big data analytics to automate the creation of artwork for video games. “One of the more fascinating talks here at GTC 2015 is centered around deep machine learning and its applications in the medical field.” — WCCFTech “Yes, that’s right: VDI is as big at GTC as it was at both Citrix Synergy and VMworld last year.” — Virtualization Practice 15

16. Developers increasingly view GTC as the place to come and learn about the latest in GPU computing. This year, more than 2,000 individual programming labs — twice as many as last year — were completed in areas ranging from CUDA basics to computer vision to deep learning. 16

17. We generated more than 1,300 articles from top business, financial, tech, consumer tech, IT, HPC, and vertical media. 17

18. SOCIAL MEDIA HIGHLIGHTS 234,000 Total engagement on social media (likes, clicks, shares) “The #GTC15 keynote on deep learning applications is blowing me away. Leaving me w/ a totally different impression of @nvidia” 95,000 Day 1 keynote live stream + replay views “I’ve struggled to explain DL to people before. The #GTC15 explanation is awesome!” 90,000 Total views of blog posts “#GTC machine learning track room seats ~200 & standing room only in first session, feels like academic conference #respect #nvidia” 18

19. “The ‘G’ (graphics) label for NVIDIA’s main product is becoming an anachronism. Instead, NVIDIA’s hardware, software and engineering output are manifested in algorithms and APIs, not circuits and interconnects. GPUs are a disruptive technology for databases, business analytics and robotics that will allow unknown startups like those in the GTC Emerging Companies Summit and giant corporations like IBM and Baidu to reshape markets.” —Forbes 19

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