The company's graphic processing capabilities are at the core of most automotive megatrends
For more than two decades, NVIDIA has pioneered visual computing, the art and science of computer graphics. With a singular focus on this field, NVIDIA offers specialised platforms for the gaming, professional visualisation, data centre and automotive markets.
NVIDIA is the world leader in visual computing. The GPU, invention, serves as the visual cortex of modern computers and is at the heart of NVIDIA's products and services. Work opens up new universes to explore, enables amazing creativity and discovery, and powers what were once science fiction inventions like artificial intelligence and autonomous cars.
Started as a standard PC graphics chip company, NVIDIA has transformed into a specialised platform company that targets four very large markets — Gaming, Professional Visualisation, Datacenter and Auto — where visual computing is essential and deeply valued. The company is singularly focused on the field of visual computing with the ability to deliver the value through PC, mobile and cloud architectures. NVIDIA is vertically integrated and bring together GPUs, system software, algorithms, systems and services to create unique value for the markets NVIDIA serves.
Vishal Dhupar, Managing Director-South Asia and Sundara Nagalingam, Head-Manufacturing and Energy Industries speak about NVIDIA's presence in India, and the multinational’s global footprint. Excerpts…
What are the target milestones by 2020 for your automotive segment?
NVIDIA’s automotive business recorded revenues of $320 million in FY16, up 80 per cent over three years. Although that still represents a relatively small slice of the company’s $5.01 billion total revenue in FY16, automotive is NVIDIA’s fastest growing group.
There are several elements to NVIDIA’s auto business. The best-established, which has been in existence for over 15 years, is the use of our professional graphics platform - Quadro - by every automaker in the world to design and virtually prototype new models. Auto engineers also make extensive use of NVIDIA’s technology to accelerate computationally intensive tasks like virtual crash testing and aerodynamics simulations.
NVIDIA’s technology has also been present inside cars themselves for several years. As mobile device makers accelerate the arms race to pack these extremely powerful computers with increasingly advanced technologies, auto manufacturers are coming under pressure to meet the same standards.
The mobile revolution has raised the expectations of car buyers. After all, for most people, their car is the single-most valuable consumer device they’ll ever own. It’s no longer acceptable for in-car technology to lag multiple years behind what we take for granted in our phones, tablets and wearable devices. NVIDIA has been working with the auto industry for over 10 years, helping to bring the cutting edge of consumer tech into the vehicle.
Dhupar explains, “Our graphics processing capabilities help drive navigation, infotainment and digital dashboard systems in vehicles including the Audi Q2, Bentley Mulsanne, Honda Civic, BMW M760Li and Porsche 718 Boxster.”
All in all, there are around 8 million cars on the road today utilising the NVIDIA GPU, same architecture you’d find powering supercomputers, computer games and blockbuster visual effects, and in the next three-four years, that number is expected to rise by 25 million.
However, the area on which NVIDIA is focusing most strongly right now is autonomous vehicles. From advanced driver assistance functions like smart cruise control that will appear in production vehicles in the next year to fully self-driving models, NVIDIA is working with over 80 OEMs, Tier-1 suppliers and research organisations to make autonomous vehicles a reality.
What is your take on the growth of connected vehicles globally? In what specific areas do you expect maximum traction? Could you explain with some examples?
Connected vehicles will be an important trend but it’s critical that self-driving cars are able to function in situations where connectivity is lost. Losing your cell signal in the middle of your call is annoying, not life critical. An autonomous vehicle that relies on connectivity to drive could have much more serious consequences if the signal fails.
That’s why NVIDIA’s solution places the power of an AI supercomputer inside the car itself. At the inaugural GPU Technology Conference Europe, NVIDIA CEO Jen-Hsun Huang unveiled Xavier, an all-new AI supercomputer designed for use in self-driving cars. Because autonomous driving is an incredibly compute-intense process, the need for an efficient AI processor is paramount. As the brain of a self-driving car, Xavier is designed to be compliant with critical automotive standards, such as the ISO 26262 functional safety specification.
Volvo has long been synonymous with safety. Now, the Swedish automaker’s latest efforts could make self-driving cars synonymous with safety, too. Henrik Lilnk, senior technical leader for the Volvo Car Group, says: “Nobody should be killed or seriously injured in a new Volvo car. Long term, new Volvos shouldn’t crash.”
Earlier in 2016, Volvo became the first OEM to announce that it will deploy NVIDIA’s self-driving technology platform on the roads. The company’s Drive Me program is an autonomous pilot scheme in which a fleet of 100 Volvo XC90 SUVs will hit public roads next year, driven by actual customers and equipped with NVIDIA DRIVE PX 2. They will use deep learning to recognise objects in their environment, anticipate potential threats and navigate safely.
Tell us about how NVIDIA is working in the areas of deep learning and supercomputing for the automotive industry.
It may come as a surprise that the world’s leading auto makers and autonomous vehicle researchers are focusing their attention on a platform originally developed to play video games. The graphics proceeding unit, invented by NVIDIA in 1999, is a tiny piece of silicon that punches well above its weight. The highly parallel nature of GPU technology means it’s perfectly suited to processing the type of artificial intelligence required for self-driving cars: deep learning.
Dhupar says, “The task of driving, with its millions of factors and variables, is too complex for conventional programming. It requires the new computing model of deep learning. Rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, in deep learning the machine is ‘trained’ using large amounts of data and algorithms that give it the ability to learn how to perform the task.”
To drive itself, a car needs to know exactly where it is, recognise the objects around it, and continuously calculate the optimal path for a safe driving experience. This situational and contextual awareness of the car and its surroundings demands a powerful visual computing system that can merge data from cameras and other sensors, plus navigation sources, while also figuring out the safest path — all in real-time.
Cars already possess many sensors, from cameras and radar to laser scanners and ultrasonic sensors. The next generation of vehicles will need to go further, not just collecting but understanding this sensory information using high-performance, energy-efficient processors that can run the search, natural language processing and object recognition algorithms needed for Advanced Driver Assistance Systems (ADAS).
“We look after the consumers for a long time and examine which part of the shelf needs to be paid attention first. Even in the stock market, you have to research which stock to buy and which to sell. We are doing deep learning in that customer domain,” Nagalingam says.
Processing these incredible amounts of information requires the power of a supercomputer, one that can handle thousands of computation points every second. But, like those powering smartphones, car-based processors must also be small and operate in an extremely energy-efficient manner. Cramming a desktop-sized computer into a car dashboard is not an option.
Nagalingam shares, “There are two major areas in which NVIDIA plays a role. First is the user experience, the driver's experience inside the car. Today, if you look at the dashboard, all the instruments are mechanical instruments. But these are now changing. The car what we buy tomorrow will be software designed. They will have a different functionality of how the driver responds to the car. The instrument panel will be replaced with a different instrument cluster.”
That’s why NVIDIA has developed the DRIVE PX 2 platform, an open AI car computing platform that enables automakers and their tier 1 suppliers to accelerate production of automated and autonomous vehicles. It scales from a palm-sized, energy-efficient module for AutoCruise capabilities, to a powerful AI supercomputer capable of autonomous driving.
At Baidu World this August, NVIDIA CEO Jen-Hsun Huang announced a partnership combining Baidu’s cloud platform and mapping technology with NVIDIA’s self-driving computing platform to develop solutions for HD maps, Level 3 autonomous vehicle control and automated parking. And last month NVIDIA announced a collaboration with TomTom that will include porting and running TomTom’s RoadDNA localisation and mapping software on DRIVE PX 2 as well as integrating TomTom’s HD map support into NVIDIA’s DriveWorks software.
For road users, the ability to handover routine aspects of driving like sitting in traffic and finding a parking space to a computer is obviously attractive. But the transformative effects of next-generation car computing will go beyond our day-to-day travel experience. For example, with solid security features in place, the power of the data generated in a single drive could be enormous. Compelling benefits include cheaper insurance, new marketing approaches, reduced accident rates and better road design.
Research already indicates that half of those who buy luxury cars would choose self-driving features. It won’t be long before they become mainstream offerings. In as little as two years, self-driving features will become as ubiquitous as airbags or ABS. And, like these new-standard features, we’ll wonder how we ever managed without them.
India alone has 133 languages and over the globe there are many languages. So how is this deep learning going to assist with that kind of scenario?
Nagalingam says, “Whatever we explained is based on English. In deep learning, translation is an extremely important application; it is not only about English translation but any other language. Another feature is video capturing, for e.g., say an English movie is shot and you want its subtitles in Tamil, Telugu or Spanish. That can be automated with the help of deep learning. Only the data should be there, through which the model can translate the motion. It can have languages like, Tamil, Telugu, Marathi and Gujarati. So the training data is available and in India, it is coming up.
Which automakers are using NVIDIA technology?
It is a long list. But NVIDIA can reveal only a few like Audi, BMW, Mercedes-Benz, Toyota, Tesla, Nissan and Volvo. These are the automakers who are already using NVIDIA technologies.
How do you see the market growth for autonomous cars in India?
Given Indian driving conditions and all, that option in India will be much later. Everything matters here, like traffic signals and drivers. The roads, driving lanes and many such other factors need to be brought in order for such autonomous cars. The availability of India-specific data is also a concern. So all these things matter for development of the cars, and autonomous driving. This has to be adopted for Indian driving skills. The adoption can be done in some parts also like automatic parking. So there is still some time for autonomous cars to be developed in India.
NVIDIA’s automotive business recorded revenues of $320 million in FY16, up 80 per cent over three years.”
- Vishal Dhupar,
Managing Director - South Asia,
NVIDIA Graphics Pvt Ltd
NVIDIA's deep learning is how we develop GPU solutions
- Sundara Nagalingam,
Head - Manufacturing and Energy Industries,
NVIDIA Graphics Pvt Ltd