Cadence Design Systems, Inc. (CDNS) Earnings Call Transcript & Summary
August 9, 2022
Earnings Call Speaker Segments
Jason Celino
analystPerfect. So my name is Jason Celino. I'm the vertical software analyst here at KeyBanc. I want to welcome everyone to Day 2 of our conference back in Vail. With me today is Anirudh Devgan, the CEO of Cadence Design Systems. I was going to make a joke about the hot rooms yesterday, but I guess the fire alarm really up -- top level this year. But before I begin, Richard wants me to have -- for the safe harbor. So today's discussion may contain forward-looking statements, including Cadence outlook on future business and operating results. Due to risk and uncertainties, actual results may differ materially from those projected or implied in today's discussion. For information on factors that could cause actual differ, please refer to our SEC filings, including our most recent Forms 10-K and 10-Q. All forward-looking statements during this meeting are based on estimates and information available as of today, and Cadence disclaims any obligation to update them.
Jason Celino
analystSo with that, maybe to begin, Anirudh, even though Cadence has been around for a long time, and you're one of the largest software companies with $3 billion in revenues many folks, especially software investors may not be familiar with the story. So in maybe not too many words, could you just talk about yourself and what Cadence does?
Anirudh Devgan
executiveYes. Thank you, and it's great to be here. So Cadence, basically, we make -- we are a software company. We call computational software. So this is CS plus math kind of software to design chips and electronic systems. So semiconductors and electronic systems. So almost any chip design in the world uses some form of Cadence software. These things are so complicated that basically, they cannot be designed manually. So there's a lot of like optimization, simulation that happens to design these chips. And so we work with all the way from advanced node like 3-nanometer, 2-nanometer to legacy nodes, all different geographies, all different verticals. And then also 45% of our customers now are system company. So 55% roughly is semi companies and 45% is system companies. So system companies are like these phone companies that design their own chips or these car companies or data center companies, and that's also growing pretty well for us. So we are also expanding more into the systems space and I can talk to you about that.
Jason Celino
analystOkay. Perfect. And I'm not an engineer. I don't have a PhD. My colleague, Michael Turits does, but I think it's in literature. So I'll try to keep this in layman's terms and simple for everyone to understand, but the EDA industry has been around since the 1960s. For the past 60 years, EDA companies that develop the tools, which have enabled engineers to make chips smaller and more powerful. While this is achieved by continuous innovation and product development, every 20 years or so, there's a breakthrough technology, which increases the step function in productivity for these design engineers. So why do you think AI can be that next breakthrough EDA technology? And then maybe first, what are 2 or 3 technologies or past EDA cycles that approve those step functions?
Anirudh Devgan
executiveRight. That's a great question. So first of all, I think you have to remember that these things are on an exponential increase for last 40 years, okay? So there are very few technologies in the history of mankind, which are an exponential increase for 40 years. So what could be in a chip like 40 years ago to now is thousands times more, okay. So because if you double every 2 years, you go that for 40 years, that's exponential. So that's very unique to semiconductors and to EDA because you have to adapt to that. You have to enable the design of this super exponential curve over 40 years. And the good thing is I don't see that thing slowing for the next 20 to 30 years. We can talk about that. So EDA had to play a key role to make sure that these things can be designed, which are increasing exponentially in size. So one of the key things, for example, when I was at IBM, this is a long time ago, in the late '90s, we would design these CPU chips, right, that goes into these servers, mainframes or -- and it would take like 500 people like 4,5 years to design that chip, okay? And then IBM would sell. [Technical Difficulty] Now if you go now, you go to one of these mobile companies or auto companies, even more complicated chips than that can be designed in 6 to 9 months by 50, 60. [Technical Difficulty] And one things is we moved up the level of abstractions. So instead of designing at the transistor levels, we design at the gate level, and there is this technique called place and route, Innovus is our platform, which is probably the most sophisticated optimization software written. So one thing here to remember is because it's exponential, we have a lot of these algorithms in CS plus math numerical analysis optimization, which we believe are state of the art. So we are best in the world for this compared to any other software company or. So I think one of the big things, Jason, has been moving up -- for EDA moving up the stack, okay? So now the next opportunity is AI because AI also is inherently computational. So if you look at AI, the basics of AI is matrix multiply and conjugate gradient. So matrix multiply for inference and conjugate gradient for training. So these algorithms we have done for like 30, 40 years in EDA, okay? So this is not that well understood. And sometimes we don't want to explain it too much to our data center friends because they're already hiring a lot of people from Cadence. But we have a lot of expertise in this kind of algorithms. And one thing with the AI is that right now, the designer is designing that there -- we give them a tool like placing route tool that will design a block, but the running of the tool is done by the design. So to give you an example, I talked to Renesas, right? This is public Renesas is a big auto design company in Japan, and they're using our software and they're designing an automotive CPU. And it has 17 wearables, 1-7, okay. And what happens is some of them are design wearables, some of them are tool options. So how do they design? They run our tools 1 time. It runs for 1 or 2 days. And then they see what to change, and they run it again. They run it again. So that takes about 6 months, right? So it's based on like designer intuition. Now if you do it mathematically, okay, if you -- in the old days, if you do it mathematically, you have to do design of experiments. So that would take 4 million runs, okay? So that's infeasible to do. So that's why the best state-of-the-art way of designing these things, even though the tools are very complex is by designer intuition, running it from one run to the next run. So EDA in the past has not automated that. But with AI and reinforcement learning, we can mathematically guide that process. So in case of Renesas, this is our tool Cerebrus, which is our AI-based implementation tool. So instead of 6 months, which the human would do or instead of 4 million runs the old way. In about 200 runs, and that would take about 1 week, we can design the thing using AI and our tool, and this is a massive benefit to the customer.
Jason Celino
analystSo that's a good segue to the Cerebrus tool. You announced it last year, I think you mentioned that the use case right now is replace and route. Are there other use cases for AI in EDA? And how should we think about that?
Anirudh Devgan
executiveWell, there are lots of use cases. I mean I think the way -- first of all, AI is overused, right, right now. Okay. everybody is calling everything AI, okay, that's a big problem, okay. But I think what we call AI, what is real AI is I think it has applications in optimization. So this kind of -- you have all these multivariable runs and you can do that mathematically rather than by the human, right? So there are a lot of applications for that technology. So 1 application is in this chip design space. But as we move to the system design, there's application in the system design space, too. So for example, right, we have all this software now that simulates CFD, computational fluid dynamics, like aerodynamics of a car, for example, or thermal simulation in the car. I just came back from McLaren.
Jason Celino
analystNice. Do you get to ride one of the cars?
Anirudh Devgan
executiveYes, they had -- we have a partnership with McLaren on the Formula 1 racing team. And -- what they do is they use this software and F1 puts all kinds of limits on how much simulation you can do, okay? But they really optimize the wings of the car and all for speed, right? So all that is CFD simulation, computational fluid dynamics, okay. But the thing is that you don't want to just simulate the car or the airplane, you want to optimize that. You want to change the shape of the wing or the -- so all that can be done with AI, just like we did in chip design. So we have this new tool we just announced for optimality that uses AI at the system level to design these kind of things.
Jason Celino
analystAnd I do want to get into the simulation piece in a little bit, but one last question on Cerebrus. So Synopsys, formidable, respectful competitor. They announced their DSO.ai products quarter or 2 before you did. Do they have any advantages given they were first to market?
Anirudh Devgan
executiveWell, I mean, that's a good question, Jason. First of all, I mean, we are -- we have become more conservative in our product launches because you work -- it's -- the timing of the launch is not an indication of the strength of the product. So we have so many customers, and we are working with like all the top 20 companies that typically we do more work before we launch a product. So if you look at all the benchmarks that we are doing, we are doing pretty well. We have the best tool in the market. Now the timing of the launch is you don't want to launch -- because what happens is if you launch too early, then you have to tell a customer, okay, no, the product is not ready. We are just working with the other customer. That's the issue or you say, "Well, I didn't work with you. I worked with another customer. So it's like a bad argument. So we normally launch it when the product is ready. And the second thing is, last year, we had a lot of product launches. The R&D team is doing great. So we had a hardware launch in Palladium and Protium in March. So typically, we will space out these things because we don't want to launch all the products in like 1 quarter. So there were some -- Cerebrus was moved -- we could have launched it earlier in the year. We moved it to July so that the hardware could be launched in March. But long story short, I'm pretty confident where we are in the market and the timing of launches, I mean, that's just a press release.
Jason Celino
analystJust a sell-side, I guess. So you've talked about simulation and you've talked about EDA being the most difficult math problem and you're trying to make it more powerful smaller transistors, what not. So computational fluids, finite element allowances, other types of simulation, must be chump change, right?
Anirudh Devgan
executiveNo. No, not chump change, but we can do it, yes.
Jason Celino
analystSo explain why simulation is becoming more important for the semiconductor cycle?
Anirudh Devgan
executiveThat's a great question. So first thing, like I said, 45% of our customers are system companies. I mean, this is well known, right? So I mean, of course, the semi guys are doing great. The semi companies are doing great, but more and more system companies are doing silicon.
Jason Celino
analystAnd those are like automotive, aerospace...
Anirudh Devgan
executiveAutomotive, like we announced last year. We're working with Tesla for the last several years, all the aerospace companies, all the phone companies. And there is a lot -- I can get into the details if you want. There are a lot of fundamental reasons that's a big trend. If there is enough volume for the system companies, like Cisco. I just talked to Cisco, they also announced they're doing a lot more silicon in-house. So all of these in different verticals, they are doing more and more silicon. So that's -- first of all, that's good for our core business. But also when you go to a system company and they're doing silicon, of course, they have a system, too. I mean, they have the actual physical car. So the way we look at the world is there is -- in 3 circles, okay? So the inner most circle is the silicon circle, then the system circle and then the data circle. The perfect example is Tesla or electric cars. So you have the navigation data, then you actually have the physical car with the hardware, software and then you have the silicon that drives it. So if you're working with those customers, 45% of our customers are system companies, so there is natural coupling between the silicon and the system. So the customers are asking us, even these things, right? I mean if you remember a few years ago, some of the Samsung phones were melting on planes because there's a connection on the thermal analysis of the phone and the chip inside, okay. So naturally, as we work with more system companies, they want us to do this system analysis and optimization. So there is -- so first thing, the reason we do it is there's a lot of customer synergy. Second thing, like you mentioned, there's a lot of R&D synergy because the math and the CS part is very similar. And the third reason we do it is because the margins actually in simulation are even better than EDA. Not only we want to be high growth in revenue. We are very focused in high growth in margin. We have one of the highest margins, I believe in terms of software companies. And we want that to grow and simulation in the system space is even more profitable. So for all these 3 reasons to me, it's a no-brainer to expand into...
Jason Celino
analystSure. So Anirudh, you mentioned margins, I didn't, to really tap some of these really heavy simulation end markets like automotive, aerospace, industrial, manufacturing. How might Cadence need to update their go-to market? And can you still achieve kind of the high margins with your direct sales force?
Anirudh Devgan
executiveYes, I think so. I mean I think we have achieved -- so I mean, like this year guidance is about 17 -- slightly less than 17% revenue growth and slightly less than 40% operating margin. And then what we have done in the last 5 years is our incremental margin is 50% or better. So that means if we make $100 million more next year, the $50 million is profit, okay. And this is why we have invested in the new areas. And I don't see any reason that we can't continue that.
Jason Celino
analystSo you've been building out your go-to-market in simulation...
Anirudh Devgan
executiveAll in that environment. And so in terms of go-to-market, there's multiple parts to it, okay? So there is -- one is the direct sales force and which -- EDA or chip industry is mostly direct, okay? And then I think as you go to the system space, we are now investing in indirect and cloud, right? But one thing to remember is even for this -- so in the systems space, like -- some of the companies can have 80,000 customers, okay. That's the other exciting thing about the system space. There are a lot more customers. In the silicon space, there are probably a few thousand customers. But if you look at the top markets or the top customers for the system space, they are the same household names in the silicon space. And our strategy always is win with the winners, right? So we always go to the high end first. So there is a lot of commonality of customers at the high end or the household names are the ones doing all the system. And then we have to scale it to the 80,000 customers. So the direct sales force can help with the high end and then the indirect and cloud can help with the scaling.
Jason Celino
analystOkay. And I am going to ask about chiplets in a little bit. We'll see if the audience has any questions. But you would -- so if EDA is the most difficult math equation and then simulation is also a different math equation. Where does Biosciences play in here? Because you just announced the OpenEye acquisition. I know it hasn't closed yet, but where does that fit in the road map?
Anirudh Devgan
executiveYes, that's a great question. And I'm very excited about that expansion. So as we go to the -- so of course, the chip space is critical, and I think it's going to do well for years to come. And then as we go to system space, the most profitable part of system space is simulation. And there are a lot of reasons for that. It has the highest growth, highest margin. And if you look at simulation at the system level, historically, there are 2 big areas is, finite element, which is simulating solids like cars or electromagnetics; and then CFD, which is simulating fluids, talked about airflow and thermal. So when we go into -- and these are established markets, that's about $10 billion -- $8 billion to $10 billion market and growing. But when we go to a new market, we just don't want to do what is the current areas. Of course, we can disrupt them with more like parallelization and more CS algorithms. But we also want to do emerging areas in simulation. So what is the biggest emerging area in simulation in my mind is molecular simulation, biosimulation. And this, of course, is going to be important in the next -- for the next 10, 20 years. And so we had a great -- we've been watching that space for a couple of years, and we had a good company like OpenEye. So we -- so I think the whole goal is to do simulation but also do it not just in the current market, but an emerging market in a very important vertical, okay. And the other acquisition we made, of course, is Future Facilities, which is going after the data center market and digital twins for data centers. So to me, data centers and biosimulations are very emerging areas that we should enter.
Jason Celino
analystOkay. Any questions from the audience? Perfect. Switching to chiplets a little bit. There was a Wall Street Journal article mentioned a week or 2 ago, one of your competitors was quoted saying that they've seen a twentyfold increase in chiplet design activity since 2019. Why are so many semiconductor companies trying to pursue this chiplet technology?
Anirudh Devgan
executiveYes, that's a great question. And I mean, we work with -- first of all, when I was an IBM in '90s, we used to do some chiplets interpose. So chiplet basically is -- typically, right now, if you buy a chip like if you see the chip, there is only one -- there's a package and there is one chip inside the package. That's typical. But chiplet means that when you see the package inside, there are multiple chips on a package. So instead of like system on a chip, it's system on a package, right? And this has been talked about for a long time. Even in the '90s, there were discussions. And even with our big TSMC foundry partner, we are very close to all these big foundries, especially TSMC. So we did a whole chiplet solution like 5 years ago, 5, 6 years ago, but it didn't take off at that time, okay? But it's really taking off now. And I think this will be a big trend in the next 10 years. And over the last 1 year, 2 years, has really taken off. And what I want to say is that there are a lot of parts to it, but Cadence is in the best position because what it means is we have to do a system on a package. So we have all the chip tools, but we have also Allegro, which is a franchise tool on PCB and packaging, and we have the highest usage of advanced packaging in Allegro. And then we have all these analysis tools like thermal and electromagnetics. So Cadence is the only company that can put together a chip level, the package level and the simulation level in 1 platform. And the reason that you say only why is chiplets doing well, I mean, there are a lot of reasons to it. Okay. One reason is -- and you can see that in a lot of semi-companies. So let's say, you go from a 1 chip in a package to 6 chips in a package. Now some of them are big, some of them are small. So first benefit is that you can merge different kind of technologies. They don't all need to be 5-nanometer. Some are 5, some are 3, some are 28. And you can also -- one can also do more domain-specific, this heterogeneous integration. The other big benefit is that if there are 6 chiplets or 5 or 6 chiplets, when one goes to the next generation of that same product, you don't have to redesign all 6 of them. You just redesign 2 of them and you keep the other 4 safe. So there's a lot of financial and technical reasons where chiplets is going to be big. And I think Cadence is in the best position to capitalize on that. And we have been investing on this actually -- when we first started investing on the systems space, this chiplet was not a big thing, but now everybody sees the integration of chips and packaging.
Jason Celino
analystSo if you were able to put numbers around it, what was the increase in intensity or complexity with chiplets versus other types of advanced nodes?
Anirudh Devgan
executiveOh, like a -- I mean, I think to me it's difficult to give numbers because -- but to me, it's orthogonal. So the way I look at it is that there is -- so if you're following like Moore's Law or advanced nodes, so we are -- most of our -- most of the customers are manufacturing at 5-nanometer. A lot of our design is at 3-nanometer. A lot of our R&D is at 2-nanometer. So if you look at that thing and then there is one point x and one. So I see at least 3, 4 nodes on the plus -- at least 3, 4 nodes on the classical Moore's Law. So that's at least -- so each node is 2, 3 years. So that's at least 10 years, okay. And then when I look at 3D-IC is an orthogonal way to do scaling because instead of making more things on a chip, you can do multiple chips and you can stack them. So I talked to, for example, Stanford right next to us, and they predict that, oh, there's some papers predicting that, that can extend Moore's Law by 7 generations. Okay. But even if it's not 7, okay, let's say, it's 3 or 4. So that's another 10 years. So to me, they are like orthogonal axis, but I think it will happen in all verticals. But right now, it started more with high-performance computing.
Jason Celino
analystSo I mean that's a good segue to my next question. But chiplets, they're incredibly more power, not power, but the bigger compute, right? But that means that if they're larger in size. They produce substantially more heat. They consume substantially more energy. So when you think about priority in reducing those byproducts, the heat and the consumption, what does that mean for your portfolio? And how does that kind of interplay with some of the products you have?
Anirudh Devgan
executiveYes. So actually, we talked to one of the big foundries and they're investing heavily in 3D-IC and chiplets. And they told me that -- and this few years ago, that the biggest issue they had or had was thermal because you have a lot more chips and then you stack them. So thermal is a big thing. So if you look at it, we have all this investment in system simulation. Actually, we started it for the 3 reasons I talked to you about that it was synergistic for the customer, synergistic in R&D and higher margin, but the chiplet kind of brings it together. Chiplet is the integration of system and chip together. And so we could -- we did -- we have a very good thermal solution called Celsius, which we developed. And there's a lot of technical reasons why it is better because it's a combination of finite element and CFD. So this having this whole stack solution. So with Celsius on the simulation side, Allegro on the packaging side and then Innovus on the chip side. So we are in a unique position to do that. So this whole chiplet move also validated our whole move into the system design and analysis space. And we can apply a lot of those technologies back to the chiplet.
Jason Celino
analystAnd I've got one more question, and then maybe we'll pay in the audience for questions. Any -- yes, sure. We have a microphone. One second.
Unknown Analyst
analystSo just 2 quick questions. First one is on your move into CFD. So ANSYS has been with -- they've done FEA and CFD for a very long time. And one of the things, I guess, is engineers build a lot of their models with ANSYS in mind, they've customized them for years. So do you have any kind of portability tools? Or just how do you convince the engineers to switch from ANSYS to, if you guys move into that -- if the functionality is the same, I doubt the engineers will make that move because it's going to be a fairly painful task. So just wondering how you approach that market? And then second, just on CapEx intensity, on the production side, especially for the fabs, that's been steadily rising over the last few years. You've done a pretty remarkable job on the design side. So at some point, do you think the strides you've made on design, they start lowering CapEx intensity? Or are those 2 just totally separate problems?
Anirudh Devgan
executiveYes, those are good questions. So on the -- so we actually break out our system design and analysis revenue, okay? So it is -- because the best way to answer the question is by results, right? So if you look at our results, it's about 12% of revenue is expected to be around -- our guidance for the year is about $3.5 billion, so 12% of that. And it is growing more than 20% for the last several years. So the customers are adopting our solutions. So even in Q2, the system design and analysis segment grew by 28%. And you talked about ANSYS and some of the other folks. So one thing I also want to emphasize is that we take all our revenue ratably, including in this space, whereas some other companies take it upfront. So we are growing 20% plus in a ratable model, okay? That means the real growth in bookings is even higher than that. And that is just based on -- this is what the customers are doing, right? So they are adopting our tools based on strength of our product. So what happens in simulation is -- so what you have to remember is in EDA, about 1/3 of the business is already simulation. So we have a lot of expertise in that. And also, our products will run like 5x, 10x faster than what's available. And when you go to the system space, I would say there are 2 kind of products. There is interactive products and then there's batch products. Interactive products are like Autodesk, right? Your architect is using some interactive tool to do your floor plan whereas simulation is a batch process. And we have similar tools on the chip side. Virtuoso, which are our franchise analog tools used by almost all analog designers, it's an interactive product. So when you go into the system space, I believe it's very difficult to replace interactive tools or like a best example of interactive tool is Microsoft Office. I was in IBM. IBM launched like Lotus 123. What happened to that, okay? So I think interactive tools are -- and your architect is unlikely to change there. So into -- what tools they're using to do their floor plan. Now simulation -- any tool is difficult to change, but simulation is the easiest thing to change, okay. So when you go to the system space, we naturally don't go after these interactive tools because that's not where the value is. The value is in the simulation tools like FEA and CFD and optimization. And I believe those can be changed. There's a history of that over the years even in EDA, right, simulation tools, people change. And also simulation tools, customers want more than one. See, you don't want 2 Microsoft Office tools or 2 AutoCAD tools. But simulation tools, even in EDA, they will have multiple tools. So I think in that industry, in FEA and CFD, they don't have that much choice, but the customers are demanding choice. So I think, first of all, it is easier to change than interactive tool, and you can see it in our results. And two, the market can sustain multiple players. So I think they can do well, too, and we will do well just -- so I'm not worried about that. As long as we have good products, and it's in the simulation space, I think there will be opportunities for us to do. Now on your second question on fab, we are mostly on the design side. And -- but I think the need for semiconductors is going to be pervasive. I just came back from DC, right? Yesterday, they had a big -- today is the signing, but yesterday, we had a big kind of CEO summit. And you can see like even U.S. government and EU and all these governments are putting a lot of money in it. So I think one thing I do want to emphasize that there may be some cyclicality in the -- and you see some reports from our semiconductor customers, maybe there is some cyclicality in their business. But in the long run, there are strong bull trends for semiconductors. And for us, okay, the reason we are powering through this, right, we expect to power through this is, first of all, we are on the -- we are mission-critical on the R&D side, okay? So there may be some -- the line you have to watch and our customers is R&D spend, not revenue. And then there is more and more R&D spend, and we can also get more and more part of their R&D spend with automation in AI, okay? So -- because we are essential to their product design, okay, that's the first part. And the second part is we are ratable, right? We are mostly ratable business. So we have a lot more visibility than some of the other companies. And third thing is we are highly diversified. So all geographies, all end markets, they all have to use our products. So for that reason, I believe we are much more resilient as we go through good times or bad times. And the fab, I think there is good to see all this investment, and we will benefit indirectly from it as they build more IP and fabs. But what I want to say is that we are highly diversified. So these things are in the end is good to have, and there is some benefit, but we are not dependent on it for the growth side.
Jason Celino
analystYes. Okay. Well, we're almost out of time, but I'll save my hardest question for last. What do you like so far about our Vail conference?
Anirudh Devgan
executiveI love the location.
Jason Celino
analystGreat. Thanks, Anirudh.
Anirudh Devgan
executiveThank you.
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