While gaming remains Nvidia’s primary source of growth, the company’s artificial intelligence business is growing rapidly. In the second fiscal-quarter, Nvidia reported increased demand for both its graphics and AI chips.

For some idea of Nvidia’s growth in AI, Huang says there are now over 4,000 AI startups working with his company. That number is up from 2,000 in April 2017 and goes to show the growing interest around the technology.

Nvidia continues to innovate in AI and is quickly establishing itself as a major player; particularly in driverless cars.

The company’s self-driving platform DRIVE is powered by Nvidia Xavier chips and consists of three parts:

1. DRIVE AV — This part of the DRIVE platform was also available in the previous generation and uses neural networks to perform the calculations required for self-driving cars.
2. DRIVE IX — The first of the two new inclusions is a software development kit which enables AI assistants that can harness data from sensors inside and outside the vehicle.
3. DRIVE AR — While there have been many advancements around self-driving technologies and AI assistants for cars, augmented reality is still relatively unexplored. With DRIVE AR, Nvidia intends to enable new graphical experiences which can deliver things such as information about points of interest along the route.

Nvidia says its Xavier chips have been in development for over four years and represents the work of over 2,000 engineers. Xavier features more than nine billion transistors and Nvidia claims it’s the most complex system-on-a-chip (SoC) ever created.

In a conference call last August, Huang teased Tesla’s reported difficulties in building its own AI chips: “It’s super hard to build Xavier and all the software stack on top of it. If it doesn’t turn out for whatever reason for them [Tesla] you can give me a call and I’d be more than happy to help.”

Autonomous car manufacturers need to train petabytes of data per day, reiterate their models, and deploy them again in order to get those vehicles to market. Nvidia has a server, equivalent to 800 traditional servers, called the DGX-2 which delivers two petaflops of performance.

During AI Expo London earlier this year, Nvidia senior director of enterprise David Hogan said: “The compute demands of AI are huge and beyond what anybody has seen within a standard enterprise environment before. You cannot train a neural network on a standard CPU cluster.”

While Nvidia is well-positioned in AI, and is already reaping the benefits, it foresees gaming continuing to be its primary source of revenue for some time.

Nvidia reported earnings per share of $1.24 on revenues of $2.58 billion in the second fiscal quarter ended July 31. Gaming was about 50.3% of total revenues, and GPUs were 81% of revenues.

Huang believes Nvidia’s GPU sales will increase thanks to the growing popularity of ‘ray tracing’ in games, deliver stunning realism by enabling things such as full-detail reflections which mirror the action happening in and around the player’s field of view:

Few games offered support for ray tracing last year and supporting RTX chips were expensive, so sales were subdued. That’s changed this year, with more supporting games and cheaper RTX chips available; a combination which produced $1.3 billion in gaming revenue for Nvidia this quarter.

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Xavier is designed to power the NVIDIA DRIVE software stack which has been expanded to a trio of AI platforms covering every aspect of the experience inside next-generation automobiles.

DRIVE AV — This part of the DRIVE platform was also available in the previous generation and uses neural networks to perform the calculations required for self-driving cars.

DRIVE IX — The first of the two new inclusions is a software development kit which enables AI assistants that can harness data from sensors inside and outside the vehicle.

DRIVE AR — While there have been many advancements around self-driving technologies and AI assistants for cars, augmented reality is still relatively unexplored. With DRIVE AR, Nvidia intends to enable new graphical experiences which can deliver things such as information about points of interest along the route.

The processor has been in development for over four years and represents the work of over 2,000 engineers. Xavier features more than nine billion transistors and Nvidia claims it’s the most complex system-on-a-chip (SoC) ever created.

Autonomous vehicles require data to be captured and processed at very fast rates. To help achieve this, Xavier is built around a custom 8-core CPU. The processor also includes a new 512-core Volta GPU, deep learning accelerator, new computer vision accelerators, and 8K HDR video processors.

These features come together to calculate 30 trillion operations per second while consuming just 30 watts. Nvidia claims this generation of its DRIVE platform is 15 times more energy efficient than its previous.

Nvidia Pegasus is the company’s supercomputer for self-driving cars. It uses two Xavier SoCs in tandem with two next-generation GPUs to deliver 320 trillion operations per second of performance.

Customers are due to receive their first samples of Pegasus in mid-2018.

What are your thoughts on Nvidia’s new Xavier SoC?

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Pegasus is powered by four high-performance AI processors. It couples two of NVIDIA’s newest Xavier system-on-a-chip processors — featuring an embedded GPU based on the NVIDIA Volta architecture — with two next-generation discrete GPUs with hardware created for accelerating deep learning and computer vision algorithms.

Level 5 represents fully autonomous vehicles which do not even have a steering wheel or gas pedal for manual control. This will require significant power to process all the required operations quickly.

“Creating a fully self-driving car is one of society’s most important endeavors — and one of the most challenging to deliver,” said Jensen Huang, NVIDIA founder and CEO. “The breakthrough AI computing performance and efficiency of Pegasus is crucial for the industry to realize this vision.”

“New types of cars will be invented”

NVIDIA claims its Drive PX Pegasus solution is able to deliver more than 320 trillion operations per second. The company’s previous self-driving car supercomputer, the Nvidia Drive PX 2, was around 13 times slower.

Building a fully autonomous Level 5 self-driving vehicle requires thousands of sensors including high-resolution, 360-degree cameras. The position of the vehicle must be calculated within centimeter accuracy to ensure safety. NVIDIA estimates the computing demands of driverless vehicles to be up to 100 times more intensive than the most advanced cars today.

Autonomous vehicles are set to be a huge industry in the coming years and NVIDIA is rapidly positioning itself as a leader in the space. Of the 225 partners developing on the NVIDIA DRIVE PX platform, more than 25 are developing fully autonomous robotaxis using NVIDIA CUDA GPUs.

“Driverless cars will enable new ride- and car-sharing services. New types of cars will be invented, resembling offices, living rooms or hotel rooms on wheels,” continues Huang. “Travelers will simply order up the type of vehicle they want based on their destination and activities planned along the way. The future of society will be reshaped.”

NVIDIA believes fully autonomous will have a drastic impact on people’s lives. Millions of hours, it claims, of ‘lost time’ will be recaptured by would-be drivers being able to work, play, eat, or even sleep on their daily commutes. Safety, meanwhile, will improve — with fewer accidents caused by fatigued, distracted, or impaired drivers.

Pegasus will be available to NVIDIA automotive partners in the second half of 2018.

What are your thoughts on NVIDIA Pegasus?

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