Nvidia R&D Chief on How AI is Improving Chip Design

By John Russell

April 18, 2022

Getting a glimpse into Nvidia’s R&D has become a regular feature of the spring GTC conference with Bill Dally, chief scientist and senior vice president of research, providing an overview of Nvidia’s R&D organization and a few details on current priorities. This year, Dally focused mostly on AI tools that Nvidia is both developing and using in-house to improve its own products – a neat reverse sales pitch if you will. Nvidia has, for example begun using AI to effectively improve and speed GPU design.

Bill Dally of Nvidia in his home ‘workshop’

“We’re a group of about 300 people that tries to look ahead of where we are with products at Nvidia,” described Dally in his talk this year. “We’re sort of the high beams trying to illuminate things in the far distance. We’re loosely organized into two halves. The supply half delivers technology that supplies GPUs. It makes GPUs themselves better, ranging from circuits, to VLSI design methodologies, architecture networks, programming systems, and storage systems that go into GPUs and GPUs systems.”

“The demand side of Nvidia research tries to drive demand for Nvidia products by developing software systems and techniques that need GPUs to run well. We have three different graphics research groups, because we’re constantly pushing the state of the art in computer graphics. We have five different AI groups, because using GPUs to run AI is currently a huge thing and getting bigger. We also have groups doing robotics and autonomous vehicles. And we have a number of geographically ordered oriented labs like our Toronto and Tel Aviv AI labs,” he said.

Occasionally, Nvidia launches a Moonshot effort pulling from several groups – one of these, for example, produced Nvidia’s real-time ray tracing technology.

As always, there was overlap with Dally’s prior-year talk – but there was also new information. The size of the group has certainly grown from around 175 in 2019. Not surprisingly, efforts supporting autonomous driving systems and robotics have intensified. Roughly a year ago, Nvidia recruited Marco Pavone from Stanford University to lead its new autonomous vehicle research group, said Dally. He didn’t say much about CPU design efforts, which are no doubt also intensifying.

Presented here are small portions of Dally’s comments (lightly edited) on Nvidia’s growing use of AI in designing chips along a with a few supporting slides.

Mapping Voltage Drop

“It’s natural as an expert in AI that we would want to take that AI and use it to design better chips. We do this in a couple of different ways. The first and most obvious way is we can take existing computer-aided design tools that we have [and incorporate AI]. For example, we have one that takes a map of where power is used in our GPUs, and predicts how far the voltage grid drops – what’s called IR drop for current times resistance drop. Running this on a conventional CAD tool takes three hours,” noted Dally.

“Because it’s an iterative process, that becomes very problematic for us. What we’d like to do instead is train an AI model to take the same data; we do this over a bunch of designs, and then we can basically feed in the power map. The [resulting] inference time is just three seconds. Of course, it’s 18 minutes if you include the time for feature extraction. And we can get very quick results. A similar thing in this case, rather than using a convolutional neural network, we use a graph neural network, and we do this to estimate how often different nodes in the circuit switch, and this actually drives the power input to the previous example. And again, we’re able to get very accurate power estimations much more quickly than with conventional tools and in a tiny fraction of the time,” said Dally.

2 Predicting Parasitics

“One that I particularly like – having spent a fair amount of time a number of years ago as a circuit designer – is predicting parasitics with graph neural networks. It used to be that circuit design was a very iterative process where you would draw a schematic, much like this picture on the left here with the two transistors. But you wouldn’t know how it would perform until after a layout designer took that schematic and did the layout, extracted the parasitics, and only then could you run the circuit simulations and find out you’re not meeting some specifications,” noted Dally.

“You’d go back and modify your schematic [and go through] the layout designer again, a very long and iterative and inhuman labor-intensive process. Now what we can do is train neural networks to predict what the parasitics are going to be without having to do layout. So, the circuit designer can iterate very quickly without having that manual step of the layout in the loop. And the plot here shows we get very accurate predictions of these parasitics compared to the ground truth.”

3 Place and Routing Challenges

“We can also predict routing congestion; this is critical in the layout of our chips. The normal process is we would have to take a net list, run through the place and route process, which can be quite time consuming often taking days. And only then we would get the actual congestion, finding out that our initial placement is not adequate. We need to refactor it and place the macros differently to avoid these red areas (slide below), which is where there’s too many wires trying to go through a given area, sort of a traffic jam for bits. What we can do instead now is without having to run the place and route, we can take these net lists and using a graph neural network basically predict where the congestion is going to be and get fairly accurate. It’s not perfect, but it shows the areas where there are concerns, we can then act on that and do these iterations very quickly without the need to do a full place and route,” he said.

4 Automating Standard Cell Migration

“Now those [approaches] are all sort of using AI to critique a design that’s been done by humans. What’s even more exciting is using AI to actually do the design. I’ll give you two examples of that. The first is a system we have called NVCell, which uses a combination of simulated annealing and reinforcement learning to basically design our standard cell library. So each time we get a new technology, say we’re moving from a seven nanometer technology to a five nanometer technology, we have a library of cells. A cell is something like an AND gate and OR gate, a full adder. We’ve got actually many thoundands of these cells that have to be redesigned in the new technology with a very complex set of design rules,” said Dally.

“We basically do this using reinforcement learning to place the transistors. But then more importantly, after they’re placed, there are usually a bunch of design rule errors, and it goes through almost like a video game. In fact, this is what reinforcement learning is good at. One of the great examples is using reinforcement learning for Atari video games. So this is like an Atari video game, but it’s a video game for fixing design rule errors in a standard cell. By going through and fixing these design rule errors with reinforcement learning, we’re able to basically complete the design of our standard cells. What you see (slide) is that the 92 percent of the cell library was able to be done by this tool with no design rule or electrical rule errors. And 12 percent of them are smaller than the human design cells, and in general, over the cell complexity, [this tool] does as well or better than the human design cells,” he said.

“This does two things for us. One is it’s a huge labor savings. It’s a group on the order of 10 people will take the better part of a year to port a new technology library. Now we can do it with a couple of GPUs running for a few days. Then the humans can work on those 8 percent of the cells that didn’t get done automatically. And in many cases, we wind up with a better design as well. So it’s labor savings and better than human design.”

There was a good deal more to Dally’s talk, all of it a kind of high-speed dash through a variety of Nvidia’s R&D efforts. If you’re interested, here is HPCwire’s coverage of two previous Dally R&D talks – 2019, 2021 – for a rear-view mirror into work that may begin appearing in products. As a rule, Nvidia’s R&D is very product-focused rather than basic science. You’ll note his description of the R&D mission and organization hasn’t changed much but the topics are different.

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industry updates delivered to you every week!

ARM, Fujitsu Targeting Open-source Software for Power Efficiency in 2-nm Chip

July 19, 2024

Fujitsu and ARM are relying on open-source software to bring power efficiency to an air-cooled supercomputing chip that will ship in 2027. Monaka chip, which will be made using the 2-nanometer process, is based on the Read more…

SCALEing the CUDA Castle

July 18, 2024

In a previous article, HPCwire has reported on a way in which AMD can get across the CUDA moat that protects the Nvidia CUDA castle (at least for PyTorch AI projects.). Other tools have joined the CUDA castle siege. AMD Read more…

Quantum Watchers – Terrific Interview with Caltech’s John Preskill by CERN

July 17, 2024

In case you missed it, there's a fascinating interview with John Preskill, the prominent Caltech physicist and pioneering quantum computing researcher that was recently posted by CERN’s department of experimental physi Read more…

Aurora AI-Driven Atmosphere Model is 5,000x Faster Than Traditional Systems

July 16, 2024

While the onset of human-driven climate change brings with it many horrors, the increase in the frequency and strength of storms poses an enormous threat to communities across the globe. As climate change is warming ocea Read more…

Researchers Say Memory Bandwidth and NVLink Speeds in Hopper Not So Simple

July 15, 2024

Researchers measured the real-world bandwidth of Nvidia's Grace Hopper superchip, with the chip-to-chip interconnect results falling well short of theoretical claims. A paper published on July 10 by researchers in the U. Read more…

Belt-Tightening in Store for Most Federal FY25 Science Budets

July 15, 2024

If it’s summer, it’s federal budgeting time, not to mention an election year as well. There’s an excellent summary of the curent state of FY25 efforts reported in AIP’s policy FYI: Science Policy News. Belt-tight Read more…

SCALEing the CUDA Castle

July 18, 2024

In a previous article, HPCwire has reported on a way in which AMD can get across the CUDA moat that protects the Nvidia CUDA castle (at least for PyTorch AI pro Read more…

Aurora AI-Driven Atmosphere Model is 5,000x Faster Than Traditional Systems

July 16, 2024

While the onset of human-driven climate change brings with it many horrors, the increase in the frequency and strength of storms poses an enormous threat to com Read more…

Shutterstock 1886124835

Researchers Say Memory Bandwidth and NVLink Speeds in Hopper Not So Simple

July 15, 2024

Researchers measured the real-world bandwidth of Nvidia's Grace Hopper superchip, with the chip-to-chip interconnect results falling well short of theoretical c Read more…

Shutterstock 2203611339

NSF Issues Next Solicitation and More Detail on National Quantum Virtual Laboratory

July 10, 2024

After percolating for roughly a year, NSF has issued the next solicitation for the National Quantum Virtual Lab program — this one focused on design and imple Read more…

NCSA’s SEAS Team Keeps APACE of AlphaFold2

July 9, 2024

High-performance computing (HPC) can often be challenging for researchers to use because it requires expertise in working with large datasets, scaling the softw Read more…

Anders Jensen on Europe’s Plan for AI-optimized Supercomputers, Welcoming the UK, and More

July 8, 2024

The recent ISC24 conference in Hamburg showcased LUMI and other leadership-class supercomputers co-funded by the EuroHPC Joint Undertaking (JU), including three Read more…

Generative AI to Account for 1.5% of World’s Power Consumption by 2029

July 8, 2024

Generative AI will take on a larger chunk of the world's power consumption to keep up with the hefty hardware requirements to run applications. "AI chips repres Read more…

US Senators Propose $32 Billion in Annual AI Spending, but Critics Remain Unconvinced

July 5, 2024

Senate leader, Chuck Schumer, and three colleagues want the US government to spend at least $32 billion annually by 2026 for non-defense related AI systems.  T Read more…

Atos Outlines Plans to Get Acquired, and a Path Forward

May 21, 2024

Atos – via its subsidiary Eviden – is the second major supercomputer maker outside of HPE, while others have largely dropped out. The lack of integrators and Atos' financial turmoil have the HPC market worried. If Atos goes under, HPE will be the only major option for building large-scale systems. Read more…

Everyone Except Nvidia Forms Ultra Accelerator Link (UALink) Consortium

May 30, 2024

Consider the GPU. An island of SIMD greatness that makes light work of matrix math. Originally designed to rapidly paint dots on a computer monitor, it was then Read more…

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Shutterstock_1687123447

Nvidia Economics: Make $5-$7 for Every $1 Spent on GPUs

June 30, 2024

Nvidia is saying that companies could make $5 to $7 for every $1 invested in GPUs over a four-year period. Customers are investing billions in new Nvidia hardwa Read more…

Nvidia Shipped 3.76 Million Data-center GPUs in 2023, According to Study

June 10, 2024

Nvidia had an explosive 2023 in data-center GPU shipments, which totaled roughly 3.76 million units, according to a study conducted by semiconductor analyst fir Read more…

AMD Clears Up Messy GPU Roadmap, Upgrades Chips Annually

June 3, 2024

In the world of AI, there's a desperate search for an alternative to Nvidia's GPUs, and AMD is stepping up to the plate. AMD detailed its updated GPU roadmap, w Read more…

Some Reasons Why Aurora Didn’t Take First Place in the Top500 List

May 15, 2024

The makers of the Aurora supercomputer, which is housed at the Argonne National Laboratory, gave some reasons why the system didn't make the top spot on the Top Read more…

Intel’s Next-gen Falcon Shores Coming Out in Late 2025 

April 30, 2024

It's a long wait for customers hanging on for Intel's next-generation GPU, Falcon Shores, which will be released in late 2025.  "Then we have a rich, a very Read more…

Leading Solution Providers

Contributors

Google Announces Sixth-generation AI Chip, a TPU Called Trillium

May 17, 2024

On Tuesday May 14th, Google announced its sixth-generation TPU (tensor processing unit) called Trillium.  The chip, essentially a TPU v6, is the company's l Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

IonQ Plots Path to Commercial (Quantum) Advantage

July 2, 2024

IonQ, the trapped ion quantum computing specialist, delivered a progress report last week firming up 2024/25 product goals and reviewing its technology roadmap. Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

The NASA Black Hole Plunge

May 7, 2024

We have all thought about it. No one has done it, but now, thanks to HPC, we see what it looks like. Hold on to your feet because NASA has released videos of wh Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire