All right. Good afternoon, welcome to the third day of JP Morgan's 51st Annual Technology Media and Communications Conference. My name is Harlan Sur, I'm the semiconductor capital equipment analyst for the firm. Very pleased to have Aart de Geus, Chairman, Chief Executive Officer, and Shelagh Glaser, Chief Financial Officer at Synopsys here with us today. Synopsys is a leader in electronic design automation or what we call chip design software and hardware solutions, also a leader in Software Integrity solutions to the software development community. It's been a busy earnings season, I've asked Aart to maybe start off with some opening comments, then we'll kick it off with some interesting Q&A. Aart, Shelagh, thank you for joining us this afternoon.
Thanks for having us and good afternoon. Thank you for joining us. As you may know, Synopsys is actually doing quite well because we're in a market that is reflecting enormous changes in the world. By now, be it hype or actually a lot of it is not hype, but just great promise, we see a horizon of smart everything. Of course, the advances that you've seen recently in the AI software are just a teaser for what is to come.
Mm-hmm.
Underneath all that is an enormous amount of computation. The computation, of course, is prepared by the people that develop the systems and the chips that do this. These are all our customers, because what we provide is, A, the design tools to design the chips, and that's about 65% of our business. B, the big building blocks that you can reuse, such as the USB on the back of your computer, it comes from Synopsys. That's about 25% of our business. Lastly, we also have a part of the company that's focusing on security in the software that increasingly becomes more and more important as it also intersects with all of this, and that's about 10% of our business.
One of the reasons for the positive and optimistic outlook is because there are a number of breakthroughs that are very fundamental in terms of what will be possible in years to come. You're all familiar, of course, with Moore's Law, which has been for 60 years, this notion of smaller transistors and more of them, but you've also heard maybe it's slowing down. There's an alternative that is just now starting to open up, which is the notion of taking big chips, breaking them into smaller ones, and actually bringing them very closely together. This is sometimes called multi-die, multiple chips or chiplets.
Mm-hmm.
Be it as it may, the challenge of that is when you take multiple chips and try to bring them very closely together, it's all about the speed and the amount of energy of communication. These are difficult problems. We love difficult problems because Synopsys, for 37 years, I would say for 75% of our products, has been the state-of-the-art of providing design tools. Here we are, we have a whole additional decade or two of opportunity to have many, many, many more transistors becoming available. We see rapid progress in that. The second is, of course, that all of the vertical markets have discovered this notion of smart everything as a great opportunity at the intersection of big data and computation.
Because of that, a lot of companies are investing in the technology that is necessary to run this because it will take an enormous amount of computation. If you just take a ChatGPT search, for example, it's roughly 5x the energy that you get out of a normal Google search. You can see that as this spreads, there's gonna be a lot of challenges. Challenges are opportunities, and so we are in the midst of that because Synopsys has invested in the lowest level of verification and simulation at atomic level for chips. We work at the design level, we work at the system level, we work at the intersection between architecture and software, and we work in the software level.
Bringing all of these together in a fashion that will support the ambitions of the world going forward is precisely why we see great opportunities. That begs the question, of course, well, is it not true that Synopsys also is doing AI itself?
Mm-hmm.
Yes, indeed. We have been fortunate to invest at an early time, so about six, seven years ago, in a fundamental push on AI as used in the design of chips. The results have truly been fantastic. Meaning that on entire design flows, we've been able to reduce the time of design from literally months to weeks, while at the same time improving the quality of results. Quality of results on a chip means how fast is it, how low power it is, how small can you make it. These have all improved. At the very moment where there's a sharp shortage of designers in the world, being able to drive productivity forward substantially is of very high value. As a matter of fact, for this decade, our intent is to deliver about 1,000x in improvement.
By the way, for the previous 35 years, we delivered 10 million x. At least we know how hard it is, we will try again to deliver on that. There's one difference between the AI that you see now frequently in the press, sort of the generative AI and ours. That one difference is that with generative AI, you get fantastic context and ideas and so on, but maybe everything is not completely correct. We have no such freedom. There can be only zero mistakes. A single transistor doesn't work, a whole system can fail.
The way we have approached this is to make sure that all of our tools are working well together, and the AI is sitting on top of that, literally as a governor to run the tools and get better results than were possible before that. This is still at the beginning, and yet we see a very rapid adoption. Our AI capability, which in late January had about 100 chips already going to manufacturing, just last month already moved to 200. You can see a very rapid move with that. Going to manufacturing is a very big decision for companies because if they fail, it costs them many, many millions of dollars.
We're in the midst of this grand story with many interesting technology challenges, all of which we say we're gonna lick, but it's gonna be some work that will proceed over the next couple of decades. A great outlook. You may ask, of course, "Well, how well is the company doing?" I'll pass it on to Shelagh to give you a sense of how we closed the quarter.
Just a quick update. We just finished Q2 earnings. We were able to beat the high end of both our revenue and EPS inside the quarter. That confidence allowed us to raise the full year 14%-15% in revenue. Importantly, improvement of operating margin, by 150 basis points, so that was an improvement. That flow through 21%-22%. As Aart said, we're executing very well, with superior financial results in a very big opportunity space.
Perfect. I appreciate the opening commentary. Let's start off at a high level. You know, there's been a lot of questions around the adoption of AI. We'll talk about Synopsys' usage of AI, but as it relates to the semiconductor industry and the tech industry, is it hype or is it reality? What I can say is covering 22 semiconductor companies and looking at their chip design activity, it's real, right? It's gonna be manifesting itself at some point in revenues, big revenues at some point, but the design activity on AI processors, but AI processors pulls in the need for more high-performance networking, more high-performance storage, SmartNIC, DPUs.
We're seeing a big acceleration not only in chip design starts, but for some of your hyperscale and cloud titans that are doing their own chip designs, they're pushing their ASIC semiconductor partners to move up their programs, right? I'm wondering, and we try to do our best to sort of track chip design activity, but, I'm wondering if this is manifesting itself in terms of higher levels of accelerated business engagement and so on with the Synopsys team. Is that part of what's driving the strong sort of full-year growth outlook for the team?
Well, the outlook obviously is driven on the one hand by the customer side, on the other hand, what are the capabilities.
Mm-hmm.
We offer them. On the customer side, there's no doubt that, despite all of the ups and downs and outlook on economy that is, to say the least, a little nebulous, the semiconductor industry is impacted by that, but is in no doubt that investing in the new chips is absolutely essential, to be there as the next positive wave of the economy happens. In that context, there's an additional driver. While for many decades, semiconductors have always been driven technology up, for the first time now in many years, you have a massive pull from the vertical markets down because everybody who wants to do smart everything in their domain has different data, has different needs.
Mm-hmm.
different speed requirements, different safety requirements. A lot of individual architectures are being developed or optimized specifically for these markets. This is all visibly in front of us starting to happen in so many different dimensions. They all have something in common, and we like to sort of put our mission under the banner of smart, secure, and safe. Of course, security is not only in the software that traditionally has been the place where things get hacked, but also increasingly in the hardware, in the building blocks. The word safe is actually an increment on top of that, which specifically for industries such as automotive that are massively going in this direction.
Yes.
as well, is absolutely essential. We have made sure that we've invested in all three words from a value proposition. At the same time, the technologies that underneath have to play very closely together.
You know, you brought up a good point about the move to multi-chip, chiplet-based architectures, right? All the leading edge chip companies are moving to multi-chip heterogeneous packaging architectures, right? It's a way to drive Moore's Law-like performance scaling without having to rely solely on semiconductor process technology scaling. Here, the team benefits with your 3DIC Compiler platform for the actual 2.5D, 3D layout and packaging design, but that also pulls a lot of critical IP, right? Like your chip-to-chip connectivity portfolio. Can you just give us an update on the revenue contribution, momentum of your solutions sort of targeting this new and up-and-coming trend?
Yeah. This is a super promising area because it's in many ways the natural place to go when it becomes harder and harder to cram more transistors in a single chip.
Mm-hmm.
By the way, I'm by no means saying that that is over.
Right.
There are people now going to, your single- ångström numbers.
Mm-hmm.
size transistors, which are really, really small. At the same time, the notion of taking certain chips and splitting them in multiple ones or actually bringing, let's say, processing and memory together with maybe some sensors and some communication chips, and if you can bring them really close together, you have essentially a super chip that is made out of different chiplets. Which opens up, by the way, also market opportunities for companies that may be focusing on just providing certain chiplets that others can then integrate.
The word integrate always sounds simple, but making things work really well together in very high degree of proximity is one of the areas where we have seen just tremendous advances in just the last five years, I would say, by making more and more connections smaller and smaller and make them also faster and use less energy. It's all about these things that have to work together. In that context, we have massively invested in providing a complete design system for this, and this will evolve for years to come because there's so many capabilities. We're working with the absolute top advanced people working in this with around 140 or 150 already in action multi-die systems. I think that the opportunity for us is great because it's actually complicated to do.
If you had asked this question two and a half, three years ago, you know, most execs would say, "You know, that's not gonna go there. It's too expensive, too difficult," and so on. Now here the wave is coming.
You do have quite a number of IP building blocks, right? To support that as well.
I'm glad you asked because, I mentioned this notion of how do you connect these things.
Yeah.
Well, just like you connect your computer to your printer, you have a cable, and you have this thing called USB on both sides, well, there's a USB equivalent-ish thing in multi-die. It's called UCIe. I won't go into the alphabet soup here. These are essentially the connectors that make it possible to have very high exchange rate between chips. Synopsys is absolutely the leader in providing the building blocks for that. Think of 25% of our business as, you know, we're super Lego blocks for electronics.
Mm-hmm.
All the main interfaces we provide. For example, in the processor world, Arm would be the leader with providing a large collection of those and a good partner to us. These things have become way more complex in the last few years, and having them work well together is absolutely essential for this type of problem.
Your overall revenue is expected to grow at a double-digit CAGR. I mean, clearly it's gonna grow strong double- digits this year, grow at a strong double-digit CAGR over the next several years, and you're benefiting from the emergence, right, of the custom chip or ASIC chip design adoption by the large cloud titans, large compute networking, industrial auto OEMs, right? Google, Microsoft, Meta, Cisco, Apple, Nokia, Ericsson, right? All these guys have amassed significant design teams engaged on a lot of their own chip design activity. This is catalyzing obviously some acceleration in design starts. It's also expanded the same opportunity, right, for the team, 'cause all of these guys need design engineering teams.
Of the double-digit % annualized growth target that the team has set, how much of the incremental growth is coming from these systems, cloud titans, OEMs that are engaged in their own ASIC opportunities? Roughly what % of your EDA mix do these cloud and systems OEMs represent?
I'll let Shelagh answer the numbers part a little bit, just to give some color, all the names that you mentioned are, of course, close customers. It's been quite remarkable how system companies that in the past were very much, you know, software only, are starting to push down, and they all realize the same thing. You know, the game is won at the intersection between software and hardware. At this point in time, we have long passed beyond the early days where hardware was built, and then once you have that, you do software on top. Now it's almost flipped over. To give you a sense how much it has flipped over, you know, today a car is referred to as a software-driven device. Software-driven vehicle in this case.
The software is actually now essentially the description of what you want to optimize for. Don't do anything else. Just make it as fast and low power as possible. That intersection is really where this all comes to bear. I always like to cite a small example of how crucial this is because, you know, the CTO of Mercedes-Benz gave me some simple numbers. He said, "For every 100 watts of computation in the car, you just reduce the reach on the battery by 10-15 miles." Can you imagine? Right now people are talking about one or two kilowatts of computation. You can just barely make it to the neighbors. You'll drive very safely to there, but there's an issue there that's off balance.
That optimization all sits between the software and the hardware, and the thing at the intersection is called the architecture. How do you design this? The architecture just got this new thing, multi-die. There's all set of new opportunities and that is why people in automotive or the hyperscalers-
Yeah
...are so much in need.
Yeah. I would add in, about 45% of our revenue comes from system companies, so some of these new entrants. We see growth actually in both-
Mm-hmm
...traditional companies and in systems companies. The one thing that's not changing, which is the other opportunity for us, is hardware engineers and semiconductor engineers. I think Aart touched a little bit on AI. We'll go a little bit deeper, but the capabilities that we're giving allow our customers to get more productivity out of those key talent people that they have. I think that's gonna be an ongoing trend that's gonna help continue to drive our growth.
Your software solutions and hardware-based platforms are touching, I believe, almost all of the highest performance SoCs that are being designed in the market today, right? Therefore, your customers, you know, these are 40, 50, 60, 70 billion transistors on a single piece of silicon. It's very, very complex, right? Your customers' chip design cycle times are extending, what used to be 18 months, 24 months, maybe extending to 36 months. They're looking for as many efficiencies on the chip design cycle, chip design flow as much as possible, right? This sort of leads. You know, your prepared commentary would, which is the Synopsys team has been developing and integrating AI and machine learning-based methods into your tools for many years now. Your AI-based Design Space Optimization platform called DSO.ai was the first solution you rolled out back in 2021.
Talk about the use cases, adoption of DSO.ai solutions. At your recent users group conference, you rolled out a few more solutions that leverage that AI framework. If you could just maybe talk about some of those new products as well?
You know, to put it in perspective, our history has been a history of automation, and Synopsys sort of changed digital design by luck or hard work, whichever way you wanna look at it, 35 years ago. In an interesting way, I think we're seeing a very similar phenomenon happening. This time it's not an individual tool doing a step better, it is a whole set of tools that have been harnessed together, we call it Fusion, meaning they work really well together, they share information. On top of that sits this thing DSO, which stands for Design Space Optimization, which may be a mouthful.
Mm-hmm.
It essentially means look at all the opportunities how you could design something and rein into the ones that are best and then optimize them until you have a really good design. A really good design is measured typically in at least two metrics, which is the speed of the chip and the power, and very often also the area, because if you can make it smaller, you'll have higher yield, you get better economics. Our capability of DSO that sits on top of now an entire design flow passed muster already a few years ago.
I think this was an AI pioneering step of being able to get quality of results that was better than what the traditional design flow with the best tools in hand could achieve by quite a number of percentage points in both speed and power. The other step it did though, it did this in a matter of weeks versus months. That is a very big productivity increase, and we're quite familiar with those because in our history we did about 10 million of X of that. We have a big ambition to keep on that track by necessity because the complexity of the systems will continue to increase exponentially with the new opportunity space.
At the same time, you heard it earlier, the design community has not grown all that much, and therefore there's actually a shortage worldwide, not just in the U.S., of engineers. Not only can we move these steps from months to weeks, we also do that with typically on the customer side, only one or two engineers versus a dozen or so, including many specialists. These are steps that are revolutionizing. The adoption rate has been very, very rapid. The only way to measure that really is to go all the way to, well, did they go to production?
Yeah.
Right? Because it's easy to say, oh, well, you do have successful experiments, you do some test chips and so on. No, no. What chips have gone with AI, DSO.ai all the way to manufacturing? There, I think we passed the number of 100 in late January. We passed 200 already in April, and we see a continuation of adoption, which is faster than normal because customers are very prudent. Remember, there's no room for errors, so they want normally to test out things for many years before they use them in production. In this case, the value proposition is so great, and so much of the testing is built into what we do, that the adoption is very fast.
Automotive sector, there's obviously continue to be a lot of focus on accelerated compute and AI, but automotive sector is a market where there's significant step up in silicon content, right? As you mentioned, I mean ADAS, autonomous functionality is driving more leading-edge compute and networking chips into a car and a significant amount of software that sits on top of that. On the analog side, power side, you've got vehicle electrification, strong step up in power transistor, power control. Silicon and software complexity is always good for the team. Where specifically does Synopsys have strong differentiation in automotive?
Well, first, your description is correct. There are two major changes in automotive. One is sort of obvious by now, everybody is going as fast as they can, which is electrification. By the way, one has to realize that such a change has massive impact on an entire industry because, you know, automotive supply chain has the OEM, the automotive company, and underneath a whole set of Tier 1s that do sub system assembly, you have the semiconductor vendors. As of a few years ago, suddenly there was more and more direct contact between the OEMs and the semiconductor companies because they realized at that point in time, "Hey, we need to know more about chips and software." The initial fear after hearing about autonomous driving was, "Hey, we're never gonna sell cars again to individuals.
You know, people are just gonna call for their car to come, for a car to come, and it's gonna be rentals and selling to fleets." Now obviously things have not developed quite like that, but the fact is still there that the push for autonomous driving has been quite substantial and is computed in level one, two, three, four, five, where five is really truly autonomous, truly safe, and, you know, go to sleep at the wheel, so to speak, and you should be fine. We're not there. We're not there.
Aside of electrification, this focus on autonomous driving has proven to be an extremely complex problem requiring a very large amount of computation because the car also absorbs a large amount of data on an ongoing basis, all of course, having to function in real time and all of course having to function with minimal energy consumption as it trades off versus the battery life. In that context, Synopsys started to invest in automotive, I want to say well over 10 years from a very different angle, which is in our IP collection, all the building blocks.
Mm-hmm.
The building blocks had to be safe. Automotive has always had a very strict set of rules and standards that you had to pass, and we have now a large collection of IP that satisfy that. Secondly, we started to invest in security in the software, which initially was not really on the radar scope for automotive, but in the context of safe, you can imagine, is actually very, very essential. Lastly, we have now become more and more a central catalytic point in between the semiconductor people, the OEMs, some of the Tier 1s that will be playing in this, as conglomerates are being formed to actually develop the next architectures that actually will make it to probably level 4 by, I wanna say, 2028, '29.
That's sort of, the sense I get from the automotive, folks. The race is absolutely on because it's gonna be challenging. We're in the midst of that, and it's been interesting because Synopsys had the benefit of literally touching way down the foundries. We, we know everything about the silicon being provided. And, and way up in the software, some of these companies. They themselves are all in, let me call it, active learning mode. You know, for an automotive company doing their own chips, that was completely inconceivable a number of years ago, and now the question is, how much should they do and how much can they actually delegate?
Any questions from the audience? If you have a question, just raise your hand. Got one right over there.
I had a question in regard to your, like, 10% of revenue comes from, like, security testing. I'm just kind of like wondering like, can you give us like the puts and take in that industry, why you guys are in it? Is it like in a silicon level design sort of part or is it just like software? What is it?
This is about margin? That.
I think it's Software Integrity.
Yeah, Software Integrity.
Yeah. Mm-hmm.
It was hard to hear the end of your. What was the question? Is it economics?
No, it's not so much economics. The question is like 10% of revenue comes from Software Integrity, portion of the business. If I look into that, like maybe 3 years, 5 years from now, like what is it? What is the puts and takes into that business? Like, what are the factors? Is it like just the hardware side of like security testing or is it just the design? What is it? Like, how do you think about it?
What are the driver? What's the profile?
Yeah.
What are the drivers? Yeah.
Initially, very initially, we got into that because in the mid-2000s, I had observed that even in semiconductor companies, more than half of the engineers were software engineers. With that comes a quick conclusion, which is, A, some of the value is created in software rather than the hardware. B, software was much less disciplined, I should say, than hardware. For things that mattered, that could be a danger. We decided to invest in the quality of software that then somewhat unexpectedly, maybe in the early 2010s became, the security is just as important.
Mm.
as the safety. That business started to grow rapidly. Initially, we're very much in the space that was between hardware and software, so embedded software. Today, a large portion of our business comes from a broad set of companies that as an EDA company, we would never have talked to. Be it an oil company, financial companies, health companies, and so on. In those companies, these are invariably agendas are driven top-down, and all it takes is, you know, one mishap and the whole board of directors tells the management, "Do something." The "do something" is, of course, check what went wrong, but also invest proactively in making the software better.
The reason I put it in this context of, you know, smart, secure, and safe, is because for us these are puzzle pieces that gradually come together because only the whole will be successful over time. It is about 10% of our business. It's a relatively new TAM for us. The rest of our business has been growing very strongly. All parts of Synopsys are evolving, and I think they hold great promise individually, but also together.
Yeah, we do get a number of questions on the diversification into SIG. However, you brought up a good point, and in some of my prepared remarks as well, is that you sort of have this intersection now of software development and chip development happening exactly at the same time, right? I would argue as more of your cloud and hyperscale and OEM titans that are developing these platforms, the chips and the software, they're probably a customer for you on the EDA side as well as on the SIG side, right? You all of a sudden become a one-stop shop, not only for chip development, but for software development as well. Is that how your customers are sort of thinking about the team right now?
That is how they start to think about it.
Yeah, right.
because they're starting to discover that from very different perspectives, these two things intersect. You, you're bringing up another really interesting point, which is, and I think I sort of mentioned before, that between a software and hardware sits really the notion of architecture, which is architecture is how do you think about your problem-
Mm.
How do you think about your implementation? The intersection tries to optimize that.
Yes.
One of the capabilities that Synopsys has also pioneered is this notion of being able to prototype things that sit right at that level.
Mm-hmm. Yes.
so that you can run the software on a prototype of chips that don't exist yet. There's enormous benefit of that because just imagine a car manufacturer that's gonna have some very sophisticated software multiple years before the chips are finished can start testing if the software will work, what are the boundary conditions and so on. By the way, learn if something in the hardware conception-
Yeah.
is not gonna live up to the real needs of the road, so to speak. This notion of prototyping, which in other fields sometimes is called a digital twin.
Mm-hmm.
We are doing that exactly at this intersection. Specifically for automotive, there's a very high degree of interest. Many of our other large customers acquire quite a bit of our prototyping, which by the way comes both in a software version, so you build models of all these chips and you run the software in it. But in some cases also hardware accelerated, which these are big machines that can run way faster on certain types of designs. This is a substantial market for us, growing rapidly, and most importantly, to me, it's like it's in the middle of the story. That's a great place to learn and place to have impact.
Absolutely. On that same note, on the hardware verification, emulation, and prototyping, that's been a really big contributor to the growth for the team. You know, 15 years ago, it was a nice to have to do hardware-based acceleration, right? 'Cause the software and the compute capability was enough for these few hundred million gate sort of you know, chip counts. Now when you're talking about billions of transistors, it's almost a necessity to do hardware-based verification. Because the cycle times are so long, you need to buy these things to also develop your software, as you mentioned.
The question does become, you know, after two strong years of hardware sales, will customers have enough hardware and compute emulation and prototyping performance capacity, or does the team just envision continued growth beyond this year just driven by just the sheer complexity increases in chip and software complexity?
Well, just jumping off the last part, I think, Aart will always talk about verification as being an unbounded problem.
Mm-hmm.
It's infinity space because these things go out into the world, and there's a lot of potential use cases to test on. That complexity actually leads people to want to invest in this because it creates such tremendous value for them so early in the design cycle to allow them to fix the design, both, hardware and software, so that when they reach the tape-out stage, they're gonna have a good tape-out. If you wait to find those issues.
Yes.
after you tape-out, you not only waste those dollars, but you waste those engineers. I think we see it as a continued growth opportunity for us just because so much of that complexity is accelerating.
On the financials, you know, the team's target financial models to drive double-digit % annualized revenue growth accompanied by 100 basis points of operating margin expansion. This year you're targeting 14%-15% growth, 150 basis points of op margin expansion, in a tough macro environment, right? It's a strong reflection of the resiliency of the business model, strong operating leverage and just overall demand for these leading-edge chip designs. What are some of the opportunities to continue to drive higher incremental operating margin fall-through and drive greater than 100 basis points of annual operating margin expansion longer term, right? Maybe driving higher revenue scale and therefore operating margins and SIG is maybe one thing that I can think of, are there any areas of margin improvement opportunity or more accelerated margin improvement opportunity?
As you said, we're committed this year to $150-
Mm-hmm.
basis points improvement. We're committed to the long-term double digit and long-term greater than 100 basis points. Literally, as we look across the company, we're looking at how some of the things we're delivering to our customers.
Yes.
we can actually, Aart calls it eating at our own restaurant, that 1,000x that he talks about, how can we take advantage of that, both in our R&D, but then also in our infrastructure? It's an area we spend quite a bit of time talking about as a leadership team, how do we optimize and drive leverage as we continue to scale this business?
Perfect. Well, we're just about out of time. Aart, Shelagh, thank you very much for the insights and your participation today.
Thank you, Harlan.
Yeah, thank you.
Thank you, Harlan.