The Exchanges

Q Yeah, I want to get into that and sort of what we can expect in terms of computing innovation if we're not just jamming more transistors on chips or we're unable to do that. Um, every one of our listeners I think has heard of AMD, but can you give like a very brief overview of the major markets you serve there?

A Sure. So AMD is a, a story company. It's been around well over 50 years. And it, uh, it started out really being, you know, a second source company, really bringing, uh, you know, second source on key components and x-eighty-six microprocessors. But you fast forward to where we are, uh, today, uh, and it's a very, very broad portfolio. Uh, when, uh, Lisa and Sue, our CEO and I were brought, uh, into the company just over 10 years ago, uh, it was with, uh, a mandate to, uh, get Uh, AMD back into very, very strong competitiveness, and so, uh, we started with the CPU line, brought the CPU, uh, very, very competitive, and then really across the portfolio, and just in February of twenty-twenty-two, acquired Xilinx, so that expanded the portfolio further. So AMD creates the world's largest supercomputers. It's got a massive install base now in the cloud, so many of your cloud operations That you're running, are running on, uh, AMD EPYC, uh, x-a-t-six CPUs. Gaming, we're, we're, we're huge. We're underneath all the, uh, Xbox, all the PlayStation, as well as, uh, many, uh, gaming devices that, uh, that, that you buy when you buy your, your, uh, add-in boards. And then across, uh, embedded devices with all of that rich Xilinx portfolio, as well as embedded x-a-t-six. And we, we acquired Pensando, so it extends that, uh, portfolio Ah, right into a networking interconnect that we need as …

AI assessment note: “supercomputers... cloud... Gaming, we're, we're, we're huge. We're underneath all the, uh, Xbox”

Q traditional, uh, CNN, RNN, and other types of, um, neural network architectures, but also in terms of this shift to transformers and diffusion models and everything else. Um, can you tell us a little bit more about what initially caught your attention In the AI landscape and then how AMD started to focus more and more on that over time and what, what sort of solutions you've come up with?

A You bet. Well, uh, we all know the AI journey, you know, has been going since, uh, really the, uh, the race began when, uh, the application space for AI opened up, uh, and GPUs were obviously, uh, pivotal there. When you look at the, uh, the, the key work that, uh, you know, Uh, Henson had done in terms of showing how GPUs could drastically improve the, uh, accuracy of image recognition, natural language processing. Uh, and so that, that, that's been known, uh, for some time. And so what we did at AMD as, uh, we, uh, right away, uh, saw the opportunity. Uh, the question was plotting our course, uh, to be that strong player in AI. So it was a very, Uh, thoughtful and deliberate strategy because AMD, we had to turn around the company. So if you look at where AMD was, uh, in, uh, you know, 2012, uh, you know, through, uh, you know, really 2017, uh, it was largely all, all of the revenue was based on PCs and then gaming. And so it was about making sure that the portfolio, the building blocks We're competitive. Those building blocks had to be leadership. They had to attract people to get on that AMD platform for high performance applications. And so first we actually had to rebuild the CPU roadmap. And that was a Zen microprocessors that, uh, that we released in, uh, in both, uh, PCs with a Ryzen line, as well as Epic, our X-AVI server line. So that started the revenue ramp for the …

AI assessment note: “what we did at AMD as, uh, we, uh, right away, uh, saw the opportunity.”

Q different tooling. API endpoints, et cetera, that the hyperscalers don't currently have, um, but also that in part they have access to GPU and there's a GPU shortage, and so that's also driving part of their utilization. How do you think about that market as it evolves over the next three, four years, and perhaps, you know, GPU becomes a bit more accessible and maybe shortages or constraints fall away?

A Well, that's definitely happening. I mean, the supply constraint will go away. We'll be a part of that. We're ramping up and shipping as we speak on our instant line, and it's going quite well. It's going according to plan. But moreover, to answer your question, I think the way to think about it is that it's just breathtaking how the market is expanding so rapidly. I said earlier that most of the applications today that started on the, you know, degenerative AI with these LLMs, that's been largely cloud-based, and not just cloud-based, but hyperscaler-based, because it's such a massive cluster that's required, not just for the training, but frankly, for quite a bit of the, the, That type of generative AI LLM inferencing also is on these massive clusters. But what's happening now is we're getting application after application that is just taking off non-linearly. It's, uh, and what we're seeing is a proliferation as people are understanding, uh, how they can tailor their models, how they can fine tune it, uh, how they can have smaller models that don't have to answer, uh, any question you have or any application you need to support, but it might be just for your business and your area of Of extra exploration. And so that allows a tremendous variety of the size of compute and, and how you need to configure that cluster. So a rapidly expanding market. Application specific configur…

AI assessment note: “the supply constraint will go away. We'll be a part of that.”

Q being a reason that you actually wanted to go to AMD. Like, what directions of innovation should we expect investment in? I don't, I don't know if it's like too deep to ask you to give us a layman's understanding of like, three D stacking, but I, I think it is really interesting to, to think about it at a, at a time when it's not obvious where to go.

A Well, no, sir, it's a, it's a great question, and the reason that I was so attracted to, uh, to AMD is, one, it's, it had a storied history of being a disrupter in the industry, uh, and, and I certainly felt very strongly that, uh, AMD could disrupt, uh, with very strong CPU and GPU, but more importantly, uh, putting the pieces together, uh, the, the idea of chiplets was just coming together. There was, there was early exploration of that, of that around that, Uh, around that time. And, uh, the engineering, uh, team here at AMD, we were able to, um, you know, really, uh, get the team rallied and the, the, the, the key leadership rallied around it and drove that, uh, that, that innovation. So That, that the reason it's so important is when Moore's law slows down, you know, the easy way to think about it is, it used to be that the chip technology itself, the foundry, going from one generation to the next, did most of the heavy lifting. So you could just bank on that new semiconductor technology node, shrinking your devices, giving you more performance, it'd have less power and it'd be at the same cost. So that was what Moore's law was about. And with Moore's law slowing, it means you still get those device improvements, but it Costs more. Your power's not coming down as much as it used to, and you are still getting that integration. You're still certainly being able to pack more …

AI assessment note: “how you use heterogeneous computing, meaning Bringing the right compute engine for the right application”

Q A big front of competition is, as you just pointed out, there's performance, like overall performance, there's efficiency, and then there's, um, like the software platform, like CUDA, RockM, et cetera. How do you think about the investment in the optimized math libraries and like how you want developers to understand your approach versus competitors?

A Yeah, you, you're so right, Sarah. It's multifaceted to be able to compete in this arena. Uh, you see many, uh, startups going after the space, but the, the fact is the, the bulk of inferencing done today is done on general purpose CPUs, not the huge LLM inferencing, but, you know, just general, uh, inferencing for AI applications. And then for large language model applications, it's almost all on GPUs because That is the software and developer ecosystems out there. And so we've been competitive on, on, uh, CPUs. We've been gaining, uh, shared a rapid clip because we've got, you know, a, a very strong CPU, uh, generation after generation that we've been releasing on, on schedules we've laid out for the industry, but for GPU, it did take us, uh, until now to develop really world-class hardware and world-class software. And what we've done, uh, is ensured that because we're a GPU, it, it should be easy to deploy. Uh, and so really making sure, uh, that we leverage the fact that we have all the GPU semantics. So if you're, you're a coder, uh, it's, it's just easy to code if, if you're using the, the lower level semantics. Uh, but also, uh, we support all of the key software libraries that are out there. When you think about the kind of frameworks, whether it be PyTorch. We're a founding member of a PyTorch foundation, whether it be Onyx, um, whether it be TensorFlow, we are out th…

AI assessment note: “we support all of the key software libraries that are out there”

Q on the health side. There's Figure. It seems like there's suddenly an explosion of new sort of hardware devices. And I was just curious to get your perspective on what do you think tends to predict success for those types of products? Um, what tends to predict failure? Like how to think about this whole sort of suite of suite of new things and devices that are coming our way.

A Well, that's a great question. I'll give you, um, you know, one point. I'll start just with sort of a technological point of view. I mean, Uh, I'm proud of the fact, uh, that, uh, chip design, uh, is part of the reason you're seeing all these different type of applications because you're getting more and more compute capability that is shrunk down and, and draws, uh, such a low power that you can, you can see, uh, more and more of these devices that have simply incredible, uh, computing and audiovisual capabilities, uh, that, that they can bring to you. I mean, you look at, uh, MetaQuest and Vision, uh, Pro and things like that. This isn't happening overnight. It's, it, you look at the earlier versions, they were simply too heavy, too big, not enough computing, ah, umph, because if the Uh, the lag between, you know, seeing a photon on the, that screen and on your head mounted device, uh, and actually being a processor that lags too high, you actually get physically ill wearing, uh, you know, wearing that and trying to watch a movie or play, play a game. So one, I'm very proud of the, the technology advances, uh, that, uh, we've been able to make as an industry. And we were certainly very proud of our, uh, aspects that, uh, that, uh, we drive from AMD. Uh, but the broader question that you've asked is, well, how do you know what's going to be successful? The technology is enable…

AI assessment note: “devices that are successful really serve a need. I mean, they really give you a capability”