Microsoft Research Podcast

Microsoft Research Podcast

An ongoing series of conversations bringing you right up to the cutting edge of Microsoft Research.

Clouds, catapults and life after the end of Moore’s Law with Dr. Doug Burger

May 9, 2018 | By Microsoft blog editor

Doug Burger – Distinguished Engineer, MSR NExT. Photo courtesy of Maryatt Photography.

Episode 23, May 9, 2018

Some of the world’s leading architects are people that you’ve probably never heard of, and they’ve designed and built some of the world’s most amazing structures that you’ve probably never seen. Or at least you don’t think you have. One of these architects is Dr. Doug Burger, Distinguished Engineer at Microsoft Research NExT. And, if you use a computer, or store anything in the Cloud, you’re a beneficiary of the beautiful architecture that he, and people like him, work on every day.

Today, in a fast-paced interview, Dr. Burger talks about how advances in AI and deep machine learning have placed new acceleration demands on current hardware and computer architecture, offers some observations about the demise of Moore’s Law, and shares his vision of what life might look like in a post-CPU, post-von-Neumann computing world.

Find the latest details on the program in the Project Brainwave announcement and on the Project Catapult research page.



Doug Burger: These are people who just have the scientific and engineering discipline, but also that deep artistic understanding. And so, they create new things by arranging things differently in this bag. And when you see a design that’s done well it’s beautiful. It’s a beautiful thing.

Host: You’re listening to the Microsoft Research Podcast, a show that brings you closer to the cutting-edge of technology research and the scientists behind it. I’m your host, Gretchen Huizinga.

Some of the world’s leading architects are people that you’ve probably never heard of, and they’ve designed and built some of the world’s most amazing structures that you’ve probably never seen. Or at least you don’t think you have. One of these architects is Dr. Doug Burger, Distinguished Engineer at Microsoft Research NExT. And, if you use a computer, or store anything in the Cloud, you’re a beneficiary of the beautiful architecture that he, and people like him, work on every day.

Today, in a fast-paced interview, Dr. Burger talks about how advances in AI and deep machine learning have placed new acceleration demands on current hardware and computer architecture, offers some observations about the demise of Moore’s Law, and shares his vision of what life might look like in a post-CPU, post-von-Neumann computing world. That and much more on this episode of the Microsoft Research Podcast.

Host: Doug Burger, welcome to the podcast today.

Doug Burger: Thank you, great to be here.

Host: We’re in for an acronym rich recording session, I believe. I assume that our audience knows more about your world than I do, but since my mom listens, I’m going to clarify a couple of times. I hope that’s okay with you.

Doug Burger: Whether it is acronym-heavy is TBD, sorry.

Host: And you are an SME. You are actually, a Distinguished Engineer for MSR NExT. Tell us what that is.

Doug Burger: The title is a standard Microsoft title for somebody that is in a technology leadership position. MSR NExT is a branch of MSR or an organization within MSR. So, when you then think about MSR we have a lot of diversity. We have geographic diversity. We have disciplinary diversity. And we have some organizational diversity. And so, NExT is just a different organizational structure that tends to produce different outcomes. That’s how I like to think of it.

Host: So, you do research in computer architecture. Give our listeners an overview of the problems you’re trying solve. What gets you up in the morning in computer architecture research?

Doug Burger: Great question. So, formally defined, computer architecture is defined as the interface between hardware and software. An architecture is what the software is able to see of the hardware. So, if I build a chip, and a system, and a disk drive, and some memory, all of those things, they’re just dead hardware, until you do something with them. And to do something with them, you have to have a way to talk to them. And so, all of these things expose a way to talk to them. And that really is the architecture. You can think of it as the language of the computer at its very lowest level.

Host: Machine language?

Doug Burger: Machine language, right.

Host: And how that translates from people into the transistor.

Doug Burger: That’s exactly right. There’s actually a lot of layers in between the person and the transistor. And the architecture that I just described is one of those layers, more towards the bottom, but not at the bottom.

Host: Right. Speaking of the stuff that’s at the bottom, transistors and devices and things like that, we’ve experienced a very long run of what we call Moore’s Law.

Doug Burger: Yes, it’s been wonderful.

Host: Which is transistors get smaller, but their power density stays constant. And some people, including you, have suggested that Moore’s Law is probably coming to an end. Why do you think that is?

Doug Burger: Let me start with a little bit of context. At its heart, all of this digital computing we’re doing is built on switches and wires. It’s a light switch. You flick it, it turns on. You flick it, it turns off. That’s really what a transistor is. What we do, is we’d start with a wire and a transistor, which remember, is just a switch. It’s a fancy name for a switch. And then we’d start putting them together. We put a few more transistors together, and then you have a logic gate. Something that can say, “and/or/not” right. Take a zero and turn it into a one, that’s a not; or two ones together added together become a one; and a one and a zero added together becomes a zero. Just the very basic universal logic functions. Long ago, in the time of Babbage, we were building computers that really didn’t work out of mechanical relays then we had vacuum tubes which at least were electronic. And then of course, the transistor was invented, and the integrated circuit. And these were a really big deal because now it allowed you to miniaturize these things on a chip.

Host: Sure.

Doug Burger: And then what Gordon Moore did, in his seminal paper in the mid-sixties, he pointed out that he expected these things to double in density, be able to fit twice as many of these things on a chip, on an integrated circuit, in a year and a half, or two years, as you would, two years ago. And then he revised the law in 1975, to be a little bit faster and then we’ve been doubling the density every couple of years since then. For 50 years. It’s been a 50-year exponential, where the cost of computing has been dropping exponentially for 50 years.

Host: Sure.

Doug Burger: And that’s what’s given us this crazy society we have with, you know, the internet and computational science, and sequencing genomes for incredibly cheap compared to what they were. It’s nuts.

Host: And so, are we nearing the end of this era?

Doug Burger: So, I get asked this a lot, and there’s two versions of Moore’s Law. There’s the actual one that he published, which talks about the rate at which chips get denser. And then there’s what I call the New York Times version, which is something loosely associated with an exponential in computing.

Host: Right.

Doug Burger: Like, performance is growing exponentially or…

Host: Well, and I… that’s what we hear, right?

Doug Burger: …that’s right. That’s right, that’s what people think. So, now if you’re on the precise version, which that’s where I am, because that’s my field. The rate of that density-increase has slowed down for the first time in 50 years. So, it hasn’t stopped, but it’s noticeably slowed. Now, maybe the chip manufacturers will pick the cadence back up again and then I’ll issue a mea culpa, and say I was wrong. But I think we’re in the end game. I think we’re in a period right now where it’s slowed. It may be that we’re on a new cadence that is just slower, or it may be that the length of time between each new generation of semi-conductor technology, lengthens out. And the problem really is, is that we’re running up against atomic limits.

Host: I was just going to say, you’ve defined these, you know, size limits in terms of atoms.

Doug Burger: Right.

Host: And how small they are now, if you “half” it again and “half” it again we’re pretty soon down to a couple atoms.

Doug Burger: That’s right.

Host: I can’t even comprehend how small that is.

Doug Burger: Yes. And now, people have built single atom or single electron transistors, right? So, it’s not a question of can we, it’s more a question of can you build a chip that has 10 billion of these on it economically. It’s really a question of economics because we know we can build smaller devices, but can you make them work and sell them cheaply enough to actually be worth doing? The other thing to note, is that, as we started to run into atomic limits when we were scaling down the transistors, the old standard, very regular structure of a transistor wasn’t possible anymore. I mean, it used to be a very simple thing that had, you know, three bars of material with something sprayed on top, and wires connected to some of those terminals. Now, these things are actually very intricate 3-D structures. So, they’re just getting more and more complex as we try to scale them down, and of course, that’s to control quantum tunneling of electrons, so we’re not leaking elec… when the switch is off, it should be off. And there’ve been challenges all along. And we always find interesting ways to deal with it, and people are doing incredibly creative things now. It’s just getting really hard.

Host: I suspect, even as you look at this complexity of the transistors getting smaller and smaller, that you’re looking at other ways to keep the progress…

Doug Burger: Absolutely.

Host: So that’s what I want to ask you next is, let’s just assume that you’re right, and what are you doing a parallel universe to keep the progress of computing power and speed and efficiency moving forward?

Doug Burger: We’re now in an uncertain era where, you know, in one generation your cost gains might be smaller than expected or your power gains might be smaller, you might not get any speed and you’re trading these things off. There’re still lots of advances going in the other parts of computing things like memory density, flash memory, network speeds, optics… So, there’s all the ancillary parts. People are also working on new types of computing. Try to bucketize what some of these might be, so programmable biology is a super exciting one, right, like DNA is a computer. DNA is a stored program that can actually replicate itself.

Host: Yeah.

Doug Burger: But there’s a program encoded in it, and there’s lots of rules about it. And so, we’re getting actually better understanding those programming languages. They’re more statistical in that you’re dealing with, you know, stuff interacting in probabilistic way. So that’s a whole different paradigm for computing that 50 years ago we didn’t even know existed, and now, we’re actually starting to leverage. There’s another one, which you could think of as just digital computing that’s not silicon-based. So, people have been looking at carbon nanotubes, different materials… None of it looks very close to me. It looks like we’re kind of 10 years away from any of it getting competitive. And of course, silicate has had so much investment.

Host: Right.

Doug Burger: Like, you know, hundreds of billions of dollars, if not trillions. It takes… you know, something that always worries me as a researcher, I know I’m jumping around a little bit here, is, if you’re on technology X, and then there’s technology Y that is not only better but will take you farther, but if you don’t get Y started early enough, X gets advanced far enough that the amount of money you need to bootstrap Y is just too high, and it never happens.

Host: Absolutely.

Doug Burger: Right, and so that’s going to be really interesting to see that play out when we think about silicon and post-silicon technologies. It may be that there are magical, much better things out there that will never achieve because we’ve invested too much in silicate.

Host: And that seems to be one of the drivers, is the cost of something. I mean, if you made a compelling case for something and it was cheap, people would adopt it.

Doug Burger: Absolutely, yeah. So, these computing systems we have, have become exponentially cheaper for many decades. They are also very general, you know, they do everything. And that’s based on something called von Neumann computing.

Host: Right.

Doug Burger: And that’s a paradigm, you know, you write software, it’s run on CPU. That’s kind of the paradigm we’ve been with for a very long time. And as the silicon gains are slowing, the gains you get from that paradigm are also slowing, and so we’re starting to see even the digital computing ecosystem fracture and diversify because of the huge economic need to do more. Let me roll back a minute and get to the other bucket. So, there’s neural computing then. They are also a programmable (albeit learning) machine and they’re incredibly interesting. I mean, just profoundly interesting. We don’t really understand how and why they work yet despite all the progress we’ve made in digital AI.

Host: Yeah.

Doug Burger: There is something super magical there, that may not even be understandable at the end of the day. I hope it will be, we don’t know. And then of course, there’s chemistry and… So, there’s just all these other ways to compute. And of course, the nice thing about the paradigm we’ve been on is, all of the levels are deterministic. You know exactly what they are capable of, what they can express is bounded but very powerful – Turing Complete, if you are a computer scientist – and so, it’s tractable and so you can… each layer hides the complexity underneath presenting you with a relatively simple interface that allows it to do all its wonderful stuff. And then now, things are getting more complex. And interesting, but also harder.

Host: Well, and I, as, you know, a non-scientist here, look at the simplicity of the interface and the underneath part is intimidating to me in terms of trying to get my brain around it. But like you say, when you unpack what is in the box people go ah, hah it’s, you know, I don’t have to ignore that man behind the curtain anymore.

Doug Burger: That’s right.

Host: I am that man behind the curtain.

Doug Burger: Pay no, attention… I didn’t say that.

Host: Suddenly, I join the technopoly.

Doug Burger: That’s right. Yeah, let me make a comment on that. These things are both incredibly simple, and incredibly complicated, at the same time. But they’ve got interfaces that are very clean and the concepts are pretty simple like switch, and-gate, adder, add two numbers, binary arithmetic, right? It’s just math with zeros and ones instead of zeros through nines. But then the number of things we do to optimize the system… it’s insane. And the complexity of these things… they’re some of the most complex things humans have ever built. I mean you think about five billion switches on one chip, it’s a small postage stamp-size thing with five billion switches organized in a way to make stuff go really fast. I mean, that’s amazing.

Host: Simon Peyton Jones said that computers, and software, and architecture are some of the most amazing structures people have ever built.

Doug Burger: They are amazing structures. And in the architecture field, when you’re designing one of those interfaces and then deciding what you put in the chip underneath to support that interface, the cool thing is it’s, unlike software, where it’s much more open-ended… I mean, to me, when I write software. I feel too free. There’s no guardrails. I can do anything. I have too much freedom. And when you’re doing the hardware, you have a budget. You have an area budget. I have this many square millimeters of silicon. And you have to decide what to fill it with, and what the abstractions are you expose, and how to spend on performance optimization versus features. If you want to put something else in, you have to take something out. So, your bag is of a finite size and you’re trying to figure out how to fill up that bag with the right components interconnected in the right way to allow people to use it for lots of stuff. You want it to be general, you want it to be efficient, you want it to be fast, you want it to be simple for software to use… And so, all of those things you have to balance, so it’s almost like an art rooted in science. There are a small number of people, and I don’t count myself among them, who are the true artists. You know, Jim Keller is a very famous one who is active in the area. Bob Colwell, retired from Intel. Uri Weiser also from Intel. I mean, these are some of the more recent examples, but these are people who just have the scientific and engineering discipline, but also that deep artistic understanding. And so, they create new things by arranging things differently in this bag. And when you see a design that is done well, it’s beautiful. It’s a beautiful thing.

(music plays)

Host: Let’s talk about a project that you co-founded and co-led called Project Catapult. Tell me about Project Catapult.

Doug Burger: Before I moved to Microsoft, I had started some research with one of my PhD students, Hadi Esmaeilzadeh who is a professor now at the University of California, San Diego. And, at the time, the community was moving towards multi-core. And there was a, not a global consensus, but definitely it was the hot thing and people were saying if we just figure out the way to write parallel software we’ll just scale up to thousands of cores. And Hadi and I really… I said this is, you know, when everyone is buying it’s time to sell.

Host: Right.

Doug Burger: And so, we wrote a paper that ended up getting published in 2011 and got some pretty high visibility. It didn’t coin the term “dark silicon,” but it popularized it. And the observation was because the transistors aren’t getting more power-efficient we can keep increasing the number of cores, but we’re not going to be able to power them. So even if you have parallel software, and you drive up the number of cores, the benefits you get are much lower than you’ve gotten historically. And what that really said to me is that that’s a great avenue, but we’re also going to need something else. And so, that something else started to be specialized computing, where you’re optimizing hardware for specific workloads. And the problem with that, is that building custom chips is expensive. And so, what you didn’t want is, say a Cloud, where people are renting computers from us, to have 47,000 different times of chips and try and manage that and have that be your strategy going forward. And so, we took a bet on this hardware called FPGAs. Now, we’re… to your acronym soup… it stands for field-programmable gate array. What they are is programmable hardware. So, you do a hardware design, and you flash it on the FPGA chip. That’s why they are called field-programmable, because you can change them on the fly, in the field. And as soon as you’re done using it, you can change it to something else. You can actually change them every few seconds. So, what we ended up doing was to say, let’s take a big bet on this technology and deploy it widely in our Cloud, and we’ll start lowering important things into hardware on that fabric, which is on a pretty interesting system architecture too. And then that’s going to be our general-purpose platform for hardware specialization. And then, once you have hardware designs that are being baked onto your FPGAs, you can take some of them, or all of them, and then go spin those off into custom chips when the economics are right.

Host: Right, interesting.

Doug Burger: So, it’s sort of a way to dip your toe in the water, but also to get a very clean, homogenous abstraction to get this whole thing started. And then while stuff is evolving rapidly, or if its units are too small, you leave it on the programmable fabric. And if it becomes super high scale, so you want to optimize the economics, and/or it becomes super stable, you might harden it to save money or to get more performance.

Host: So, there’s flexibility there that you otherwise wouldn’t have.

Doug Burger: Yeah, the flexibility is a really key thing. And again, the FPGA chips had been used widely in telecom. They’re very good at processing streams of data flowing through, quickly, and for testing out chips that you were going to build. But in the Cloud, nobody had really succeeded at deploying them at scale to use, not as a prototyping vehicle for acceleration, but as the actual deployment vehicle.

Host: Well, so what can you do now post-Catapult, or with Catapult, that you couldn’t do on a CPU or a GPU?

Doug Burger: Well, let me first say that the CPUs and GPUs are amazing things that focus on different types of workloads well. The CPUs are very general. And what a CPU actually does well, is it takes a small amount of data called the “working set” sitting – you know, for those of you who are architecture geeks in its registers in level-one data cache – and then it streams instructions by them, and operating on those data. We call that temporal computing. If the data that those instructions are operating on is too big or if those data are too big, the CPU doesn’t actually work very well. It’s not super-efficient. Which is why, for example, processing high a bandwidth stream coming of the network, you need custom chips like NICs for that. Because the CPU, you know, if it has to issue a bunch of instructions to process each byte, and those bytes are coming in at 12 1/2 billion bytes a second, you know, that’s a lot of thread.

Host: Right.

Doug Burger: So, what the GPUs do well is something called SIMD parallelism, which stands for Single Instruction Multiple Data, and the idea there is you have a bunch of tasks that are the same, all operating on similar, but not identical, data. So, you can issue one instruction and that instruction ends up doing the same operation on say, eight data items in parallel.

Host: Okay.

Doug Burger: So that’s the GPU model. And then the FPGAs are actually a transpose of the CPU model. So rather than pinning a small amount of data and running instructions through, on an FPGA we pin the instructions, and then run the data through.

Host: Interesting.

Doug Burger: I call that structural computing. Other people have called it spatial. I mean, both terms work. But the idea is, you take a computational structure, you know, a graph of operations, and you pin it, and you’re just flowing data through it continuously. And so, the FPGAs are really good for those sorts of workloads. And so, in the Cloud, when we have functions that can fit on a chip, and you want to pin it and stream data through at high rates, it works really well and it’s a nice compliment to the CPUs.

Host: Okay. So, Catapult is…?

Doug Burger: Catapult is our project code name for Microsoft’s large-scale deployment of FPGAs in the Cloud. Sort of covers the boards and the system architecture. But it’s really a research project.

Host: I was just going to say, is this now in research, is it beta, is it production? Where are you with it?

Doug Burger: In late 2015, Microsoft started shipping one of the Catapult FPGA boards in almost every new server that it bought. That’s in Bing, Azure and other properties. And so, by this point, we’ve gone to very large scale. This stuff is deployed at ultra-large scale worldwide. We’re one of the largest consumers of FPGAs on the planet. And so, of course, there are now teams all over the company that are using them to enhance their own services. When you are a customer using the accelerated networking feature, that speed that you’re getting, which is a huge increase in the speed, both over what we had before, but also it’s faster than any of our competitors networks, is because the FPGA chip is actually re-writing the network flows with all of the polices that we have, to keep our customers secure, and keep their virtual private networks private, and make sure that everyone adheres to our polices. And so, it’s inspecting the packets as they are flowing by at 50 gigabits, 50 billion bits a second. And then re-writing them to follow our rules and making sure that they obey the rules. If you try to do that on the CPUs, which is where we were doing it before, the CPUs are very general, they are programmable. They do a good job, but you use a lot of them to process flows at that rate. And so, the FPGAs are just a better alignment for that computational model.

(music plays)

Host: Talk about Brainwave, Project Brainwave.

Doug Burger: So, Brainwave… there’s a big move towards AI now, as I think you know, just everybody listening will know. A very hot area… And in particular, it was spurred by this thing called deep learning, which I think many of your listeners will know, too. But what they figured out was that, with the deep models, you know, deep neural networks, if you add more data to train them, they get better, as you add more data, you make them bigger, and they get better. And they kind of marched through a lot of the traditional AI spaces like machine translation, speech understanding, knowledge encoding, computer vision… and in replacing each dedicated set of algorithms for that domain that had been developed painstakingly over years. And that’s really what spurred I think a lot of this huge movement because it seemed to be okay, there’s something here, these things are very general and if we can just make them bigger, and train more and more data, we can even do more interesting things which has largely been born out, it’s really interesting. And so, of course, that put a lot of pressure on the silicon, given the trends that we were discussing. And so now, there’s a big investment in custom architectures for AI, machine learning, and, specifically, deep learning. And so, Brainwave is the architecture that we built in my team, working with partners in Bing and partners in Azure, to deploy to accelerate Microsoft services.

Host: Right.

Doug Burger: So, sort of our version of a deep neural network processor, what some people call a neural processing unit, or NPU. And so, for an organization like Bing, who’s really compute-bound, like they are trying to learn more and more so they can give you better answers, better quality searches, show you what you need to see. And so, we’ve been able to deploy larger models now that run in the small amount of time that you’re allowed to take before you have to return an answer to a user. And so, we’ve been running it at worldwide scale for some time now. And now, what we announced at Build, is that we’re bringing it to Azure for our customers to use. And also a private preview where customers, in their own servers, in their companies, can actually buy one of our boards with the Catapult architecture, and then pull models down, AI models down from Azure, and run them on their own site as well.

Host: Wow.

Doug Burger: So, they become an Azure endpoint, in some sense, that benefits from all of the AI that’s sitting in Azure. Uh, one other thing about the Brainwave stack itself that’s significant is that I think right now, for inference, which is asking of the questions, a lot of the other technologies use something called batching where you have to glom, say, 64 different requests together and ship them as a package, and then you get all the answers back at once. The analogy I like to use is if you’re standing in a bank line and you’re the second person but there’s 100 people in line, that the teller processes them all by taking all their IDs and then asking them all what they want, and then, you know, withdrawing all the money, and then handing it to each person. You all finish at the same time, right? That’s batching.

Host: I love it.

Doug Burger: It’s great for throughput on these machines but not so good for latency, so we are really pushing this real-time AI angle.

Host: That leads me to a kind of philosophical question… How fast is fast enough?

Doug Burger: Well, how fast is fast enough really depends on what you are trying to do. So, for example, if you are taking multiple signals from around the web, and trying to figure out that, for example, there’s an emergency somewhere. A couple of minutes might be fast enough, you know, a millisecond is not life and death. If you’re doing interactive speech with somebody, actually very small pauses matter for the quality of the experience.

Host: We’ve all experienced that in, you know, watching a television interview where there’s latency between the question and the answer…

Doug Burger: Exactly.

Host: …and you often step on each other…

Doug Burger: That’s right. Another good example, another piece of AI tech that Microsoft unveiled was its HPU, which goes in the HoloLens headset, and that’s also processing neural networks. That was a very, very amazing team in a different organization, the HoloLens organization, that built that chip, working with Ilan Spillinger’s team. But that chip, if you think about the latency requirements, it’s figuring out the environment around you so that it can hang virtual content that is stable as your eyes are darting around, so, you know, even a couple of milliseconds there is a problem. So really, how fast depends on what you’re trying to do.

Host: What you’re trying to do, yeah, yeah.

Doug Burger: So, speed is one component, and then the other is cost. You know. So, if you have billions of images, or millions of lines of text, and you want to go through and process it so that you can, you know, understand the questions that people commonly ask, you know, in your company, or you want to look for, you know, signs that this might be cancer in a bunch of radiological scans, then what really matters there is cost-to-serve. So, you have a, sort of, a tradeoff between how fast is it, how much processing power you are going to throw at it right away? And how cheap is it to do one request.?

Host: Right.

Doug Burger: The great thing about the BrainWave system is I think we’re actually in a really good position on both. I think we’re the fastest, and we are in the ballpark to be the cheapest.

(music plays)

Host: What was your path to Microsoft Research? How did you end up here?

Doug Burger: I was a professor at the University of Texas for ten years. I worked very closely with a gentleman named Steve Keckler. We were, sort of, two in a box, and we did this really fun and ambitious project that was called TRIPS, where we decided we wanted to build a better type of CPU. And I mean I, this… the Catapult was my first FPGA project, so I’m really actually a hardened chip guy. And so, we came up with some ideas, and said hey, there’s a better way to do CPUs. It’s really a new architecture. You know, a new interface to the machine that uses different principles than the historical ones. And so, we raised a whole bunch of DARPA money that… DARPA was super interested in the technology… built a team in the university, went through the grind, built a working chip. I mean, Steve was an expert in that space. He really led the design of the chip, did a phenomenal job. We worked together on the architecture and the compiler. So, we built this full working system, boards, chips, compilers, operating system, all based on these very new principles. And, academically, it was a pretty influential project and pretty high-profile. But we got to the end of that… it was too radical to push into industry. It was too expensive to push into a startup, you know, semi-conductor startups weren’t hot at the time. But after that, I really wanted to go and try to influence things more directly. And so, Microsoft came calling right around the time I was wondering what’s next. And it just seemed like time for new challenges.

Host: So, your work has amazing potential and that usually means we need to be thinking about both the potential for good and the potential not-so-good. So, is there anything about what you’re doing that keeps you up at night?

Doug Burger: I’ll tell you that I don’t worry about the AI apocalypse at all. I think we’re still tens or hundreds of thousands of times away from the efficiency of a biological system. And these things are really not very smart. Yes, we need to keep an eye on it in ethics… I mean frankly, I worry more about global warming and climate change. That’s kind of my big thing that keeps me up at night. To the extent that our work makes computing more efficient and we can help solve big, important scientific problems, then that’s great.

Host: So, as we close, what’s the most exciting challenge, or set of challenges, that you see maybe on the horizon for people that are thinking of getting into research in hardware systems and computer architecture.

Doug Burger: Really, it’s find a North Star that you passionately believe in and then just drive to it relentlessly. Everything else takes care of itself. And, you know, find that passion. Don’t worry about the career tactics and, you know, which job should you take. Find the job that puts you on that path to something that you really think is transformational.

Host: Okay, so might be one of those transformational end-goals?

Doug Burger: For me, and this is, I think, the first time I’ve talked about it, I think we’re beginning to figure out that there is something more profound waiting out there than all of these heterogeneous accelerators for the post-von Neumann age. So, we’re at the end of Moore’s Law. We’re kind of in the silicon end-game, and von Neumann computing, meaning, you know, the stream of instructions I described, you know, has been with us… since von Neumann invented it, you know, in the forties, and it’s been amazingly successful. But right now, we have von Neumann computing and then a bunch of bolted on accelerators and then kind of a mess with people exploring stuff. I think there’s a deeper truth there for that next phase which, in my head, I’m calling structural computation. So that’s what I’m thinking about a lot now. I’m starting to sniff out that I think there is something there that might be general and be that next big leap in computer architecture. And of course, the exciting thing is I could be totally wrong and it’s just a mess but that’s the fun of it.

(music plays)

Host: Doug Burger, I wish I had more time with you. Thanks for coming in and sharing the depth of knowledge that you’ve got stacked in that brain.

Doug Burger: Uh, my pleasure. Thank you for the great questions, and for all your listeners.

To learn more about Dr. Doug Burger, and the latest research on acceleration architecture for next-generation hyperscale clouds, visit

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