GTC 2019: Chief Scientist Bill Dally Gives Glimpse into Nvidia Research Engine 

Amid the frenzy of GTC – Nvidia’s annual conference showcasing all things GPU (and now AI) – William Dally, chief scientist and SVP of research, provided a brief but insightful portrait of Nvidia’s research organization. It’s perhaps not gigantic by large company standards, roughly 175 full-time researchers worldwide, but still sizable and quite impactful. At GTC, the exhibit hall was packed as usual with sparkly new products in various stages of readiness. It’s good to remember that many of these products were ushered into existence or somehow enabled by earlier work from Dally’s organization.

“We have had many successes, only a small number of them are listed here (in his presentation), and in my view what we really do is invent the future of Nvidia,” said Dally during his press briefing.

Nvidia must agree and politely declined to share Dally’s slides afterward. Perhaps a little corporate wariness is warranted. No matter – a few phone pics will do. In his 20-minute presentation, Dally hardly gushed secrets but did a nice job of laying out Nvidia’s research philosophy, broad organization, and even discussed a few of its current priorities. It’s probably not a surprise that optical interconnect is one pressing challenge being tackled and that work is in progress on “something that can go to 2 terabits per second per millimeter off the chip edge at 2 picojoules per bit.” More on that project later.

Bill_Dally, Nvidia chief scientist and head of research

Presented here are most of Dally’s comments (lightly edited). They comprise an overview of Nvidia’s approach to thinking about and setting up the research function in a technology-driven company. Some of the material will be familiar; some may surprise you. Before jumping in it’s worth noting that Dally is well-qualified for the job. He was recruited from Stanford University in 2009 where he was chairman of the computer science department. He is a member of the National Academy of Engineering, a Fellow of the American Academy of Arts & Sciences, a Fellow of the IEEE and the ACM, and received the 2010 Eckert-Mauchly Award. There’s short bio at the end of the article.

The Role of Research at Nvidia

To give you an idea of what we do I’ll give you our philosophy. Our goal is to stay ahead of most of Nvidia and try to do things that can’t be done in the product groups but will make a difference for the product groups. When I was talked into leaving the academic world in 2008 by Jensen and starting Nvidia research in its current incarnation, I spent time surveying a lot of other industrial research labs and found the most of them do great research or have huge impact on the company, but almost none of them did both. The ones that did great research tended to publish lots of papers but were completely disconnected from their company product groups. Others wound up sort of being consultants for the product groups and doing development work but not really doing much research. So I set as a goal for Nvidia research to walk this very narrow line between these two chasms, either of which will swallow you, to try to do great research and make a difference for the company.

Our philosophy on how to do research is to maximize what we learn for the minimum amount of effort put in. So in jumping into a project we basically look at the risk reward ratio, what would be the payout if we succeed and how much effort is it going to take to do this? We try to do experiments that are cheap, [require] very little effort but if successful will have a huge impact.

Another thing we do is we involve the product groups at the beginning of research projects rather than at the end and this has two really valuable consequences. One is that by involving them at the beginning they get sense of ownership in it. When we get done we’re not just popping up with something that they have never seen before but it is something that they have been sort of a god parent to from day one incubating the technology along. Probably more important though is we wind up, by getting them involved in the beginning, solving those real problems not some artificial academic imagined problem that we pose for ourselves. We wind up having the technology much easier for them to adopt.

For the rest of this article go to sister publication HPCwire.