(futuristic electronic music) >> From Sand Hill Road in the heart of Silicon Valley, it's theCUBE presenting the People First Network, insights from entrepreneurs and tech leaders. >> Alright, I'm John Furrier with theCUBE. We are here in Sand Hill Road at Mayfield for their 50th anniversary content program called the People First Network, co-created with theCUBE, and with Mayfield and their network. I am John for theCUBE, our next guest is Pradeep Sindhu who is the former co-founder of Juniper Now, the co-founder and CEO of Fungible, a start-up with a super oriented technology we're going to get into, but first, Pradeep, great to see you. >> It's great to see you again, John. >> For a 50th anniversary, there's a lot of history. And just before we get started, we were talking almost 10 years ago, you and I, we did a podcast on the future of the iPhone, only about a year in, maybe half a year. You had the vision, you saw the flywheel of apps, you saw the flywheel of data, you saw mobile. That's actually exchanges to IoT that we're seeing today, that world that's playing out. So, obviously, you're a visionary and an amazing entrepreneur. That's actually happening, so. You saw it and how did you adjust to that? What was some of the things that you did after seeing that vision? >> Well, some of the things that I did, if you recall our conversation, a big piece of that was data centers and the fact that the ideal computer is centralized. There are other things I want to make distributed, but it was obvious back then that people would build very large data centers. And the same problem that happened with the internet, which is how do you connect billions of people and machines to each other, was going to come to data centers themselves. So that is the problem that I prepared myself for, and that's the problem that we're trying to solve at Fungible as well. >> And one of the things we've been having great conversation was as part of this 50th anniversary, People First, is the role of entrepreneurship. What motivated you to do another start-up? You had that itch you were scratching? You were also at Juniper Network, huge success, everyone knows the history there and your role there. But this is a wave that we've never seen before. What got you motivated, was it an itch you were scratching? Was it the vision around the data? What was the motivator? >> It wasn't necessarily an itch I was scratching. I'm a restless person. And if I'm not creating new things, I'm not happy. That's just the way I'm built. And I also saw simultaneously the ability, or this potential, to do something special a second time for the industry. So I saw a big problem to which I could contribute. >> And what was that problem? >> So that problem really was, back then, I would say 2012, 2013, it was obvious that Moore's law was going to flatten out. That this technology called CMOS, on which we've been writing now for 35, 40 years, was not giving us the gain that it once was. And that, as a result of that, transistors that one people thought were plentiful are going to become precious again. And one result of that would be that general purpose CPUs which were doubling in performance, or had been doubling in performance every couple of years, would stop doing that. And the question I ask myself is, that when that happens, what next? And so it's in the pursuit of what next is what led me to start my second company, Fungible. >> So what's interesting, we've been seeing a lot of posts out there, some cases criticizing Intel, some saying Intel has a good strategy. You see Nvidia out there doing some great things. The earnings are doing fantastic. The graphics, my kids want the new GPU for their games. Even their being bought for the people who are doing cryptocurrency mining, so the power of the processor has been a big part of that. Is that a symptom or a bridge to a solution, or is that just kind of the bloated nature of how hardware's going? >> It's not so much the bloated nature of hardware as it is the fact that, see, general purpose microprocessors or general purpose computing was invented by John Mo-noy-man in the late 1940s. This was just a concept that you could conceive and build something which is Turing-equivalent, which is completely general. In other word, any program that any computer you could conceive could be run by this one general purpose thing. This notion was new. The notion for programmable computer. This notion is incredibly powerful and it's going to take on all of the world. And Intel today is the best proponent of that idea. And they're taking it to the limit. I admire Intel hugely. But so many people have worked on the problem of building general purpose processors, faster and faster, better and better. I think there's not a lot in left that tank. That is the architecture is now played out. We've gone to multi-core. Further, the base technology on which microprocessors are built, which is CMOS, is now reaching, is beginning to reach it's limits. We think, actually general concessions in the industry, and I particularly also think, that five nanometers is probably the last CMOS technology because technology's getting more and more expensive with every generation, but the gains that you are getting previously are not there anymore. So, to give you an example, from 16 nanometers to seven, you get about a 40% improvement in power but only about a 5% improvement in performance and clock speed, and, in fact, probably even less than that. And even the increase in the number of transistors, generation to generation, is not what is used to be. It used to be doubling every couple of years, now it's maybe 40%-50% improvement every two to three years. So with that trend and the difficulty of improving the performance of general purpose CPUs, the world has to come up with some other way to provide improved performance, power performance, and so on. And so those are the fundamental kinds of problems that I am interested in. Prior to Juniper, my interest in computing goes back a long ways. I've been interested in computing and networking for a very long time. So one of the things that I concluded back in 2012, 2013, is that because of the scarcity of Silicon performance, one of the things that's going to happen is people are going to start to specialize computing engines to solve particular problem. So, what the world always wants is, they want agility, which is the ability to solve problems quickly, but they also want the ability to go fast. In other words, do lots of work per unit time, right? Well, those things are typically in conflict. So, to give you an example, if I built a specialized hardware engine to solve one and only one problem, like solving cryptocurrency problems, I can build it to be very fast. But then tomorrow if I want to turn around and use that same engine and do something different, I cannot do it. So it's not agile, but it's very fast. >> It's like a tailor-made suit. >> It's like a tailor-made suit. >> You're only wearing one-- >> It does one thing. >> You put on a little weight, you got to (chuckles), you get a new one. >> Exactly. So this trade off between agility and performance is fundamental. And so, general purpose processors can do any computation you can imagine, but if I give you a particular problem, I can design something much better. So now as long as Silicon was improving the performance every couple of years, there's no incentive to come up with new architectures. General purpose CPUs are perfect. Well, what you are seeing recently is the specialization of the engines of computing. First was GPUs. GPUs were invented for graphics. Graphics, the main computation of graphics is lots and lots of floating point numbers where the same arithmetic applies to an array of numbers. Well, people then figured that I can also do problems in AI, particularly learning and inferencing, using that same machinery. This is why Nvidia is in a very good place today. Because they have not only an engine, called a GPU, which does these computations very well, but also language that makes it easy to program, called CUDA. Now, it turns out that in addition to these two major types of computing engines, one which is general purpose compute, which is invented a long time ago, and the other one which is called a signal instruction multiple data type of SIMD engine. This was invented maybe 30 years ago in mainframes. Those are the two major types of engines and it turns out that there's a third type of engine that will become extraordinarily useful in the coming world. And this engine we call the DPU, for data processing unit. And this is the engine that specializes in workloads that we call data-heavy. Data intensive. And, in fact, in a world which is going from being compute-centric to data-centric, this kind of engine is fundamental. >> I mean, the use cases are pretty broad, but specific. AI uses a lot of data, IoT need data at the edge. Like what the GPU did for graphics, you're thinking for data? >> That is correct. So the DPU, let's talk about what the DPU can and cannot do. And maybe I can define what makes a workload data-centric. There's actually four characteristics that make a workload data-centric. One is that the work always comes in the form of packets. Everybody's familiar with packets. Internet is built using packets. So that one is no surprise. Second one has a given server. Typically serves many, many hundreds, maybe thousands, of computations concurrently. So there's a lot of multiplexing of work going on. So that the second characteristic. The third characteristic is that the computations are stateful. In other words, you don't just read memory, you read and write memory, and the computations are dependent. So you can't handle these packets independently of one another. >> I think that's interesting because stateful application are the ones that need the most horsepower and have the most inadequacy right now. APIs, we love the APIs, restless APIs, no problem. Stateless. >> Stateless. Stateful, by the way, is hard. It's hard to make stateful computations reliable. So the world has made a lot of progress. Well, the fourth characteristic, which is maybe even a defining one, but the other ones are very important also, is that if you look at ratio of input/output to arithmetic, it's high for data-centric calculations. Now, to give you-- >> Which high, I is higher, O is higher, both? >> I/O, input/output. >> I/O, input and output? But not just output? >> Not just input, not just output. Input/output is high compared to the number of instructions you execute for doing arithmetic. Now, traditionally it was very little I/O, lots of computation. Now we live in world which is very, very richly connected, thanks to the internet. And if you look inside data centers, you see the same, it's a sort of Russian dolls kind of thing. And the same structure inside which is you have hundreds of thousands to maybe millions of servers that are connected to each other, that are talking to each other. The data centers are talking to each other. So the value of networks as we know is maximized at large scale. The same thing is happening inside data centers also. So the fact that things are connected east-west and is any-to-any way, it is what leads to the the computations becoming more data-centric. >> Pradeep, I love this conversation because I've been banging my head on all my CUBE interviews for the past eight years saying that cloud is horizontally scalable. The data world has been not horizontally scalable. We've had data warehouses. Put it into a database, park it over there. Yeah, we got Hadoop, I got a data lake, and then what happens? Now you got GDPR and all these other things out there. You got a regulatory framework that people don't even know where their data is. But when you think about data in the way you're talking about it, you're talking about making data addressable. Making it horizontally scalable. And then applying DPU to solve the problem, rather then try to solve it here in the path of, or the bus if you will, I don't know what to call it, but-- >> The thing to call it is, it's the backplane off a data center. So the same way that a server, a mainframe, has a backplane where all the communications go through. Well, inside a data center, you have this notion of a network which is called a fabric of the data center. It's the backplane off the data center. >> So this is a game changer, no doubt. I can see it, I'd love to get, I can't wait to see the product announcements. But what is the impact to the industry, because now you're talking about smaller, faster, cheaper, Which has been kind of the Moore's law. Okay, the performance hasn't been there but we've had general purpose agility. Now you have specialism around the processor. You now have more flexibility in the architecture. How does that blend in with cloud architectures? How does that blend into the intelligent edge? How that fit into the overall general architecture? >> Great question. Well, the way it blends into cloud architecture is that there's one and one thing that distinguishes cloud architectures from previous architectures, and that's the notion of scale-out. So let me just maybe define scale-out for the audience. Scale-out essentially means having a small number of component types like storage servers and compute servers, identical. Put in lots of them because I can't make individual one faster, so the next best thing is to put lots of them together. Connect them by very fast network that we call a fabric, and then have the collection of these things provide you more computing and faster computing. That's scale-out. Now scale-out is magical for lots of reasons. One is that you deliver much more reliable services because individual things failing don't have an effect anymore, right? The other thing is that the cost is as good as it can get because you're doing, instead of building very, very specialized things, a few of them, you're building many, many, many things, which they are more or less identical. So those two things, the economics is good, the agility is great, and also the reliability is great. So those three things is what drive cloud architecture. Now the thing that we talked about, which is specialization of the engines inside cloud. So we had, up until now, the cloud architecture was, is homogenous scale-out servers, all x86 based. What we're entering is a phase that I would call heterogeneous specialized scale-out engines. So you are seeing this already, x86, GPUs, TPUs, which are tensor flow processors, FPGAs. And then you're going to have DPUs coming, and in this ecosystem, DPUs are going to play two roles. One which is to offload from x86 and GPUs those computations that they don't do very well, the data-centric computations. But the second one is to implement a fabric that allows these things to be connected very well. Now you had asked about the edge. Specialization of computing engines is not going to be sufficient. We have to do scale-out more broadly in a grander sense. So in addition to these massively scalable data centers, we're going to have tens of thousands of smaller data centers closer to where the data is born. We talked about IoT. There's no reason to drag data thousands of miles away if you don't have to. >> Latency kills. >> Latency kills for some applications, it's in fact deadly. So putting those data centers where both computing and storage is near the source of data is actually very good. It's also good from the standpoint of security. At least it makes people feel good that, hey, the data is located maybe 10, 20 kilometers away from me, not 10,000 kilometers away where maybe it's a different government, maybe I won't have access to my data or whatever. So we're going to see this notion of scale-out play in a very general way. Not just inside data centers, but also in the sense that the number of data centers is going to increase dramatically. And so now you're left with a networking problem that connects all these data centers together. (John chuckles) So some people think-- >> And you know networking? >> I know a little bit about networking. So some people say that, hey, networking is all played out, and so on. My take is that there is pressure on networking and network equipment vendors to delivery better and better cost per bit per second. However, networking is not going out of style. Let's be very clear about that. It is the life blood of the industry today. If I take away the internet, or DCIP for example, everything falls apart, everything that you know. >> Well, this often finds-- >> So, the audience should know that. >> Yeah, well, we didn't really bang on the drum. We seen a real resurgence in networking, in fact, I covered some of Cisco's events and also Juniper's as well, and you just go back a few years, all these network engineers, they used to be the kings of the castle. They ran the show. Now they're kind of like, cloud-natives taking it over, and you mentioned serverless. I mean, heterogeneous environment, it's essentially serverless, Lambda and other cool things are happening, but what we're seeing now is, and again, this ties back to your apps conversation 10 years ago, and your mention about the DPU and edge, is that the paradigm at the state level is a network construct. You have a concept of provisioning services, you have concepts of connectionless, you have concepts of state, stateless, and that right now is a big problem with things like Kubernetes, although Kubernetes is amazing, enabling a lot of workloads to be containerized, but now don't talk to each other. Sounds like a network problem. >> Well, it is-- >> These are network problems. Your thoughts. >> When you look, so networking is really fundamental, at one level, so as I've said, there are three horsemen of infrastructure. There is compute which is essentially transforming information in some way. By doing some form of arithmetic. I don't mean one plus one gets two. I mean generalized manipulation of data. You have some input, you do some computation, you get some output. That's one entity. Another entity is storage, which is general purpose storage. I put something in there, I want to come back later and retrieve it. And it needs to be resilient, ie. resistant to failures. The third piece of the puzzle is networking, and the kind of networking that is the most useful is any-to-any networking, which is what TCIP gives you. So, essentially these three things are three sides of the same coin, and they work together. It's not as if one is more important than the other. The industry may have placed different values, but if you look down at the fundamentals, these three things go hand in hand. >> What's interesting to me and my observations, we have an internal slide that we used in our company, it's a content, our content pillars, if you will, and they're concentric circles. Data center, cloud, AI, data, and BotChain crypto. Data being like big data now, AI. Right in the middle is IoT, security and data. You're inventing a new category of data. Not classic data. Data warehousing-- >> This is agile data. At the end of the day, what we want to build is engines and platform for data processing, taken to it's limit. So, to give you an example, with the engines that we have, we should be able to store data with arbitrary levels of reliability. I really mean that. >> Stateful data. >> Stateful data, that is, I put data in one place, I can keep it securely, in other words, it's cryptographically, it's encrypted. It is resilient, and it's distributed over distance so that I could come back a hundred years later and find it still there, and nobody can hack it. So these are the things that are absolutely necessary in this new world, and the DPUs going to be a key enabler of providing-- >> So just to tie it all together is the DPU, the data processing unit, that you're inventing. Is the glue layer in the heterogeneous world of cloud architecture? Because if you're offloading and you have a fabric-- >> That's one role. That's one role. The glue layer that enabling a fabric to rebuild is one of the roles of the DPU. The second role, which is really, really important, is to perform data-centric calculations that CPUs and GPUs do not do very well. So, on data-centric calculations, the four things that I told you about, we're about 30 times better price performance and power performance compared to either GPU or TPU, on those calculations. And to the extent of those calculations are really important, and I think they are, the DPU will be a necessary component. >> Pradeep, I've been getting a lot of heat on Twitter, well, I'm on social media, I know you're not, but I've been saying GDPR has been a train wreck. I love the idea, we want to protect our privacy, but anyone who knows anything about storage and networking knows that storage guys don't know where their databases are. But the use cases that they're trying to solve are multi-database. So, for instance, if you do a retail transaction, you're in a database. If you're doing an IoT transaction in your self-driving car that need data from what you just bought, the idea of getting that data is almost impossible. They would have to know that you want the data. Now that's just two databases, imagine bringing-- >> Hundreds. >> Hundreds of databases. Everything, signaling in. It's a signaling process problem. Part of the problem. >> Part of the problem is that data is kept in many, many, many different formats. I don't think one can try to come up with a universal format for data, it won't work. So generally what you need to do is be able to ingest data in multiple formats. And do it in real time, store it reliably, and then process it very quickly. So this is really the analytics problem. >> Well, congratulations, the future of Silicon Valley is coming back as a chip, a chip that you're making? >> We are making a chip. What's very important for me to say is that this chip is, or it's a series of chips, these are programmable. They're fully programmable. But they're extraordinarily powerful. >> Software-defiant chip sets coming online. Pradeep, thanks for spending the time. >> You're welcome. >> I'm John Furrier, here at Sand Hill Road for the People First Network, theCUBE Presents. I'm John Furrier, thanks for watching. (futuristic electronic music)