Competition makes for better chip design with AMD CTO Mark Papermaster

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Hi listeners, and welcome to another episode of No Priors. Today we're excited to be talking to the CTO of AMD, Mark Papermaster. Mark has had a storied career in chips and hardware, with previous leadership positions at IBM, Apple, and Cisco. We're excited to have Mark on to get into GPUs and the competition that's been driving this industry. Welcome Mark.

**Mark Papermaster** (0:50)
Thanks Sarah, glad to be here with you and Elad.

**Sarah** (0:52)
Can you start by telling us a bit about your background? You've worked on all sorts of interesting things from the iPhone and the iPad to the latest generation of AMD Super Computing chips.

**Mark Papermaster** (1:01)
Oh sure, I've been around a while. So what's really fun is my timing was pretty good getting into the industry. I was an electrical and computer engineering grad at University of Texas, and got really interested in chip design. And so it was back at a time when chip design was radically changing. The kind of technology everyone uses today, CMOS, was just coming into production usage. And so I got on IBM's very first CMOS projects and created some of the first designs. So I got to get my hands dirty and do just about every facet of chip design, and had a number of years at IBM and took on different roles, took on driving the microprocessor development at IBM across first their power PCs, and that meant working with Apple and Motorola as well as the big iron, the big computing chips that we had in the mainframe and the big risk servers. So really got all facets of technology there and included working on some of their server development, but then shifted over to Apple. Steve Jobs hired me to run the iPhone and iPod.
And so I was there for a couple of years, but it was a time of a great transition in the industry. And for me, it was a great opportunity because I ended up in 2011, fall 2011, taking the role here at AMD of being both CTO and really running the technology and engineering and right at a point where Moore's laws starting to slow down.
And so, you know, tremendous innovation was needed.

**Sarah** (2:46)
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 unable to do that. 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?

**Mark Papermaster** (3:04)
Sure. So AMD is a storied company. It's been around well over 50 years, and it started out really being a second source company, really bringing second source on key components and x86 microprocessors. But you fast forward to where we are today, and it's a very, very broad portfolio.
When Lisa and Sue, our CEO, and I were brought into the company just over 10 years ago, it was with a mandate to get AMD back into very, very strong competitiveness. And so we started with the CPU line, brought the CPU, very, very competitive, and then really across the portfolio.
And just in February of 2022, 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 AMD EPYC x86 CPUs. Gaming, we're huge. We're underneath all the Xbox, all the PlayStation, as well as many gaming devices that you buy when you buy your add-in boards. And then across embedded devices with all of that rich Xilinx portfolio, as well as embedded x86.
And we acquired Pinsando, so it extends that portfolio right into a networking interconnect that we need as we scale out these workloads. So very, very broad portfolio.

**Elad** (4:42)
Yeah, AMD has had a pretty amazing run over the last decade plus since you joined. One of the things that you folks have really emphasized over the last couple of years, as well as AI, and there's been a big shift both in terms of the adoption of AI over the last decade or so, in terms of the traditional CNN, RNN and other types of neural network architectures, but also in terms of this shift to transformers and diffusion models and everything else.