 From Sand Hill Road in the heart of Silicon Valley, it's theCUBE, presenting the People First Network, insights from entrepreneurs and tech leaders. Hello everyone, 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. Again, I'm John Furrier with theCUBE. Our next guest is Pradeepsson, who's the former co-founder of Juniper now, the co-founder and CEO of Fungible. Let's 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 the 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. It was 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 actually connected into the IoT that we're seeing today. That world is 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 or 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 that want to make it 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 fungible as well. Now, one of the things that we've been having great conversation with as part of this 50th anniversary of People First is the role of entrepreneurship. What motivated you to do that startup? You had that itch you were scratching, you know, it's a 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 motivation? 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 at least the 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 there was a back then, you know, 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 gains that it once was. And that as a result of that, transistors that once 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 asked 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 it's interesting, we've been seeing a lot of posts out there, you know, some cases criticizing Intel, some saying Intel has a good strategy, you see Nvidia out there doing some great things and earnings are doing fantastic, the graphics. My kids want the new GPU for their games. You know, even they're 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 is going? So 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 von Neumann in the late 1940s. This was just the concept that you could conceive and build something which is Turing equivalent, just completely general, in other words, any program that any computer you could conceive could be run by this one general purpose thing. This notion was new, the notion of a programmable computer. This notion is incredibly powerful and it's kind of taken over the world and Intel today is the best proponent of that idea and they've taken it to the limit. You know, I admire Intel hugely but so many people have worked on the problem of building general purpose processors faster and faster and better and better. I think there's not a lot left in that tank that is the architecture is now played out. We've gone to multicore. Further, the base technology on which microprocessors are built which is CMOS is now reaching, is beginning to reach its limits. We think actually general consensus in the industry and I particularly also think that five nanometers is probably the last CMOS technology because technology is 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 in clock speed and it's actually probably even less than that. And even the increase in the number of transistors generation to generation is not what it 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, something, 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. So 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 gonna happen is people are going to start to specialize computing engines to solve particular problems. 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 build 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 to do something different, I cannot do it. So it's not agile, but it's very fast. Yeah, it's like a tailor-made suit. It's like a tailor-made suit, it does one thing. Yeah, if you put it all away, you got 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 in performance every couple of years, there's no incentive to come up with new architectures. General-purpose CPUs are perfect. Well, what you've seen recently is the specialization of the engines for computing. First was GPUs. GPUs were invented for graphics. Graphics, the main computation 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 the GPU, which does these computations very well, but also a 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 was invented a long time ago, and then the other one, which is called a single-instructional 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 needs data at the edge. Correct. Like what the GPU did for graphics, you're thinking for data. That is correct. So the DPU, so 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. The whole, everybody's familiar with packets, you know, net is built using packets. So that one is no surprise. Second one is that a given server typically serves in many, many hundreds, maybe thousands of computations concurrently. So there's a lot of multiplexing of work going on. So that's the second characteristic. The third characteristic is that the computations are stateful. In other words, you don't just read a memory, you read and write memory, and the computations are dependent. So you can't handle these packets independently of one another. That's interesting because stateful applications are the ones that need the most horsepower and have the most inadequacy right now. I mean, APIs, we love the APIs. Restless APIs, no problem. 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 the ratio of input output to arithmetic, it's high for data-centered calculations. Now, to give you- Which high? High or oh, it's higher, both. IO. IO, so the input and output, 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 IO, lots of computation. Now, we live in a world which is very, very richly connected, thanks to the internet. And if you look inside data centers, you see the same sort of, it's a 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, they're 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. So the same thing is happening inside data centers also. So the fact that things are connected east-west in this any-to-any way is what leads to the computations becoming more data-centric. You know, 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. Yes. The global world has been not horizontally scalable. We've had data warehouses, you know, put it into a database, park it over there. Yeah, we've got Hadoop, I've got a data lake, and then what happens? Now you've got GDPR and all these other things out there. You've got a regulatory framework that people don't even know where their data is. But when you think about data and the way you're talking about it, you're talking about making data addressable, making it horizontally scalable. Yes. And then applying a DPU to solve the problem rather than trying to solve it here in the path of, or the bus, if you will. I don't know how to call it, but is that... The thing to call it is it's the backplane of 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 of the data center. So this is a game changer. No doubt. I can see it. I'd love to get in. I can't wait to see the product announcements. But what's the impact to the industry because now you're talking about smaller, faster, cheaper, which has been kind of the Moore's Law. Yes. Okay, the performance hasn't been there, but we've had general purpose agitably. 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 does that fit into the overall general architecture? Great question. Well, the way it blends into cloud architectures 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, putting lots of them because I can't make individual ones faster. So the next best thing is to put lots of them together. Connect them by a 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. 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 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 it's homogeneous 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 TensorFlow 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 is 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 nearer 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. 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. Well, my take is that there is pressure on networking and network equipment vendors to deliver better and better cost per bit per second. However, networking is not going out of style. Let's be very clear about that. That it is the lifeblood of the industry today. If I take away the internet or DCP IP, for example, everything falls apart, everything that you know. So the audience should know that. Yeah, well, we've been really banging on the drum. We've seen a real resurgence in networking. In fact, I'll cover 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 like cloud natives taking it over and you mentioned serverless. I mean, the heterogeneous environment is essentially serverless, Lambda and other cool things are happening. But what we're seeing now is, again, this ties back to your apps conversation 10 years ago and you're mentioning about the DPU and Edge, is that the paradigm at the state level is a network construct. You have concepts 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, the navel in a lot of workloads to be containerized, but now they need to talk to each other. It sounds like a network problem. Well, it is. These are network problems, your thoughts. So, you know, 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. So 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, i.e. resistant to failures. The third piece, the puzzle, is networking. And the kind of networking that is the most useful is any-to-any networking, which is what TCPIP 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 place different values, but if you look down at the fundamentals, these three things go hand in hand. You know, what's interesting to me in my observations, we have an internal slide that we use in our company. It's a content, our content pillars, if you will, in their concentric circles. Data center, cloud, AI, data, and blockchain, crypto. Data being like big data, now AI. 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. So at the end of the day, what we want to build is engines and platform for data processing taking to its 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- 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 can come back 100 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 DPU is 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 a glue layer in the heterogeneous world of cloud architecture because if you're offloading and you have a fabric- That's one role. The glue layer that is enabling a fabric to be built 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 DPU on those calculations. And to the extent that those calculations are really important, and I think they are, the DPU will be a necessary component. You know, I've been getting a lot of heat on Twitter and while I'm on social media, I know you're not, but I've been saying GDPR has been a train wreck. I mean, 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. You're doing an IoT transaction in your self-driving car that needs data from what you just bought, the idea of getting that data is almost impossible. They would have to know that you'd want the data. Now that's just two databases. Imagine bringing in 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. That's, 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. Silicon Valley is coming back. It's 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. So software-defined chip sets coming online. Perdue, thanks for spending the time. I'm John Furrier here at Sand Hill Road for the People First Network that CUBE presents. I'm John Furrier, thanks for watching.