NVIDIA Testimony at Senate Commerce, Science, and Transportation Committee Hearing

1. NVIDIA Testimony June 14, 2017 Senate Commerce, Science, and Transportation Committee Hearing Thank you, Chairman Thune, Senator Nelson and distinguished members of the Committee. My name is Rob Csongor. I am vice president and general manager of NVIDIA’s Automotive business. NVIDIA is one of the world’s leading computer technology companies. We’re headquartered in Silicon Valley, with more than 10,000 employees worldwide. I appreciate your invitation to give testimony today on the important subject of self- driving. In particular, I am grateful for the opportunity to introduce you to the breakthrough work NVIDIA is doing in artificial intelligence. Along with hundreds of our partners, we believe AI is the new computing model, the game-changer that makes autonomous vehicles possible. By understanding how AI works, we can achieve better regulatory decisions and accelerate our progress to what we all want – deployment of safer, self-driving vehicles that will save lives. NVIDIA’s computer innovation is focused at the intersection of visual processing, high performance computing, and artificial intelligence — a unique combination at the heart of the world’s next-generation computer systems. This new form of computing is based on our invention of the GPU or graphics processing unit, nearly two decades ago. The GPU was originally designed to power computer graphics, but it has evolved into a powerful computer brain that processes massive amounts of data at extraordinary speed. Ten years ago, researchers began to use GPUs to accelerate mathematically intense applications, such as mapping the human genome and predicting weather. More recently, scientists working in a new field of AI called deep learning, discovered that GPUs are critical to creating algorithms that enable computers to learn from experience and data, similar to how the human brain works. In a short period of time, AI algorithms rapidly outperformed code written manually by programmers. As a result, deep learning has become a strategic imperative across many industries. Consumer services from

2. companies like Google, Amazon, Microsoft and Facebook powered by our technology are now available to millions. In the healthcare industry AI is accelerating the search for cancer cures. For scientists and researchers, NVIDIA delivers supercomputing solutions used at the Department of Energy, the Department of Defense, the National Institutes of Health, among other organizations. The automotive world is next. A self-driving car is an immense computational challenge. The car must be able to detect and perceive objects everywhere around it, in motion, and in diverse weather and lighting conditions. The car must determine its precise position, plan safe paths from one point to another, and then drive while navigating complex situations. We simply cannot get there with conventional programming science. AI technology can solve these problems. To this end, NVIDIA has created an open computing platform comprised of powerful processors optimized for AI, in both the car and the data center. In addition, NVIDIA is developing a full, open software stack that the automotive ecosystem is building on. Today, we are working with virtually every automaker on research and development of advanced self-driving vehicles using AI. Our technology is being used by more than 225 automotive companies worldwide, including Audi, Tesla, Toyota, Volvo, Mercedes and others. We are now at the point where we can create AI systems that have levels of perception and performance far beyond humans, and importantly, do not get distracted, fatigued or impaired. Much like humans gain knowledge through experience, AI systems improve over time with additional training data and testing. An AI system works by training a deep neural network with large amounts of data in a data center, monitoring and testing accuracy. Once validated, the car is updated with new algorithms over the air, like any modern computer or mobile device. The car runs the algorithms on real road conditions. The results are then sent back to the data center, where the new data can be used to retrain and improve the algorithms. And then the cycle continues, making the entire fleet better with each iteration. Our methodology, along with our partners, will combine multiple layers of testing – in a data center, on proving grounds, and on public roads. In addition, leveraging NVIDIA’s

3. expertise in visual computing, we can use computer simulation to accelerate the training and testing process. We believe new regulations are necessary. But clearly, there are opportunities to streamline development and testing. Ideally, we would be able to test cars and collect diverse data from any state. The patchwork of different regulations across regions hampers that. It would be enormously beneficial to have a unified set of regulations across all states. It would also be constructive to ensure the standards for compliance are set correctly. The bar we are comparing against is a human driver. A system that is significantly safer than a human driver can save lives, once deployed. Conversely, unrealistic compliance targets runs the risk of costing lives. And finally, the deployment of a fleet on real roads, collecting lots of data, is the path to achieving safety for the entire fleet. Self-driving holds the promise to change our lives. Through our inventions, our research, and the incredible work of development partners innovating on our technology, NVIDIA believes this promise is achievable. We look forward to working with this Committee, the Department of Transportation, NHTSA, and other groups to ensure the safe deployment of autonomous vehicles through game-changing technology paired with effective policy and regulation. Thank you for the opportunity to tell you of our work.