Innovating as One Team
The roots of many of NVIDIA’s landmark innovations — the foundational technology that powers AI, accelerated computing, real-time ray tracing, and seamlessly connected data centers — can be found in the company’s research organization, a global team of around 400 experts in fields including computer architecture, generative AI, graphics, and robotics.
Making a Deliberate Effort to Do Great Research
Established in 2006 and led since 2009 by Bill Dally, former chair of Stanford University’s computer science department, NVIDIA Research is unique among corporate research organizations. The team makes a deliberate effort to pursue complex technological challenges while having a profound impact on the company and the world.
"We make a deliberate effort to do great research while being relevant to the company," said Dally, chief scientist and senior vice president of NVIDIA Research. "It’s easy to do one or the other. It’s hard to do both."
One Team, One Vision
One of NVIDIA’s core values is "one team," a deep commitment to collaboration that helps researchers work closely with product teams and industry stakeholders to transform their ideas into real-world impact.
"Everybody at NVIDIA is incentivized to figure out how to work together because the accelerated computing work that NVIDIA does requires full-stack optimization," said Bryan Catanzaro, vice president of applied deep learning research at NVIDIA. "You can’t do that if each piece of technology exists in isolation and everybody’s staying in silos. You have to work together as one team to achieve acceleration."
Transforming NVIDIA and the Industry
NVIDIA Research didn’t just lay the groundwork for some of the company’s most well-known products — its innovations have propelled and enabled today’s era of AI and accelerated computing.
It began with CUDA, a parallel computing software platform and programming model that enables researchers to tap GPU acceleration for myriad applications. Launched in 2006, CUDA made it easy for developers to harness the parallel processing power of GPUs to speed up scientific simulations, gaming applications, and the creation of AI models.
Making Ray Tracing a Reality
Once NVIDIA Research was founded, its members began working on GPU-accelerated ray tracing, spending years developing the algorithms and the hardware to make it possible. In 2009, the project — led by the late Steven Parker, a real-time ray tracing pioneer who was vice president of professional graphics at NVIDIA — reached the product stage with the NVIDIA OptiX application framework, detailed in a 2010 SIGGRAPH paper.
Accelerating AI for Virtually Any Application
NVIDIA’s research contributions in AI software kicked off with the NVIDIA cuDNN library for GPU-accelerated neural networks, which was developed as a research project when the deep learning field was still in its initial stages — then released as a product in 2014.
Achieving Breakthroughs in Chip Design, Networking, Quantum and More
AI and graphics are only some of the fields NVIDIA Research tackles — several teams are achieving breakthroughs in chip architecture, electronic design automation, programming systems, quantum computing, and more.
Conclusion
NVIDIA Research is a small group of people who are privileged to work on ideas that could fail. And so, it is incumbent upon us to not waste that opportunity and to do our best on projects that, if they succeed, will make a big difference.
FAQs
Q: What is NVIDIA Research?
A: NVIDIA Research is a global team of around 400 experts in fields including computer architecture, generative AI, graphics, and robotics.
Q: What is the mission of NVIDIA Research?
A: The mission of NVIDIA Research is to pursue complex technological challenges while having a profound impact on the company and the world.
Q: How does NVIDIA Research collaborate with product teams and industry stakeholders?
A: NVIDIA Research collaborates with product teams and industry stakeholders to transform their ideas into real-world impact through a deep commitment to collaboration and full-stack optimization.
Q: What is the significance of CUDA in NVIDIA’s research?
A: CUDA, a parallel computing software platform and programming model, enabled researchers to tap GPU acceleration for myriad applications, propelling the era of AI and accelerated computing.
