The Software Behind Silicon (with Synopsys Founder Aart de Geus and CEO Sassine Ghazi)

Summary

ACQ2 by Acquired: Detailed Summary of "The Software Behind Silicon"

Release Date: May 6, 2024

Guests:

• Art Digius, Founding CEO of Synopsys
• Sassine Ghazi, CEO of Synopsys

Host:

• Ben Gilbert
• David Rosenthal

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1. Introduction to Synopsys and the EDA Landscape

The episode begins with Ben Gilbert introducing the guests, Art Digius and Sassine Ghazi, highlighting Synopsys as an $80 billion leader in Electronic Design Automation (EDA). Ben explains EDA as the essential productivity software for chip designers, likening it to Microsoft Excel or Figma for their profession. He emphasizes Synopsys's critical role in enabling modern semiconductor innovations and the AI era.

Ben Gilbert (00:00):
"ACQ2 is Ben and David's conversations with expert founders and investors... Synopsys even uses AI now to design the software to design chips."

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2. The Origins of Synopsys: Art Digius’s Journey

Art Digius recounts the genesis of Synopsys in the mid-1980s. Working at General Electric (GE), he and his colleagues developed innovative design tools, including synthesis. The downturn in the semiconductor industry in 1985 led to layoffs at GE, prompting Art and his team to spin out into a startup with GE's support.

Art Digius (02:18):
"We became quickly known through some paper published as being on the frontier of this thing called synthesis... It was all pretty accidental how that came about."

David Rosenthal (04:49):
"Wow. It's so rare that a corporate spin out into a venture style venture goes well. That's amazing."

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3. Synopsys’s Pioneering Role in EDA and Chip Design

The discussion delves into the technical aspects of Synopsys's early tools. Art explains how synthesis tools revolutionized chip design by automating the creation of efficient circuits, significantly outperforming manual designs. This innovation established Synopsys as a leader in EDA.

Art Digius (09:17):
"We understood that the speed was key, and the speed is determined by whatever is the longest path through your design... it made entirely new types of chips possible."

Sassine Ghazi (16:20):
"We are strictly a digital company... before Synopsys, the field was called Computer Aided Design."

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4. Evolution of Synopsys and Leadership Transition

Sassine Ghazi shares his journey from academia to Intel and eventually joining Synopsys. He highlights his familiarity with Synopsys products during his time at Intel and the opportunity to lead the company as CEO.

Sassine Ghazi (11:00):
"When I started my career at Intel, a lot of the stuff that the synthesis create, they were manually verified... That's when the opportunity to join Synopsys came along."

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5. Trust and Verification in EDA Tools

A significant portion of the conversation revolves around the importance of trust in automated design tools. Art emphasizes that Synopsys's tools not only optimize designs but also have stringent verification processes to ensure correctness, which is crucial given the high costs of manufacturing faulty chips.

Art Digius (17:24):
"The cost of going to manufacturing of something that has a bug is, whoa, you made a big decision there."

Sassine Ghazi (18:27):
"The Value of trust is extremely high. But the necessity for verification is also."

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6. Integrating AI into EDA: Opportunities and Challenges

The conversation shifts to the integration of AI within Synopsys's tools. Sassine discusses the initial resistance from users hesitant to trust AI-driven optimizations, a challenge that Synopsys has been overcoming by ensuring robust verification steps.

Sassine Ghazi (17:33):
"In the EDA world, we're all about optimization, massive optimization problems... you have many checks of verification."

Art Digius (18:55):
"Trust but verify. Here's this AI stuff. And then you still need to simulate a lot to make damn sure that you didn't have an error in it."

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7. Systemic Complexity and the Future of Moore’s Law

Art and Sassine delve into the increasing systemic complexity in chip design, highlighting how advancements require unprecedented collaboration between design, manufacturing, and software. They discuss the challenges of maintaining Moore's Law amid these complexities and the shift towards multi-die architectures to continue performance scaling.

Sassine Ghazi (35:01):
"They are sitting in a package electronically, you can design it to function correctly and then you can manufacture it and package it to function correctly... connectivity is the enabler."

Art Digius (32:27):
"Moore's Law essentially was the exhibit of what an exponential is... we've contributed about 10 million x in productivity."

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8. Expansion Beyond Traditional Semiconductor Customers

The episode highlights Synopsys's strategic shift from primarily serving semiconductor companies to engaging with system companies, such as automotive OEMs. Sassine explains that modern system companies require advanced EDA tools to design complex electronics integrated into their products.

Sassine Ghazi (61:31):
"Fifteen years ago, pretty much 100% of Synopsys revenue was semiconductor companies. Today, 45% of our revenue are system companies."

David Rosenthal (62:29):
"Fifteen years ago, I can't imagine the CEO of Toyota would come see you guys, but today they are."

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9. Strategic Acquisition of Ansys: Enhancing Simulation Capabilities

In January, Synopsys announced a significant acquisition of Ansys, a leader in simulation and analysis. Sassine outlines two primary motivations: enhancing Synopsys's core EDA capabilities and expanding into system-level simulation for complex, multi-physics applications.

Sassine Ghazi (63:49):
"The other vector is what we just touched on as well, which is many system companies... ANSYS is the leader in the simulation and analysis of that multiphysics."

Ben Gilbert (63:36):
"I was watching... Jensen arrived, like literally seconds before he was about to run on stage."
(Note: This quote seems out of context regarding Ansys; likely a misplacement in the transcript.)

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10. The Integral Role of Simulation in Modern Engineering

Sassine emphasizes the growing importance of simulation in designing complex systems, particularly as physical testing becomes impractical for highly interconnected and smart devices. The acquisition of Ansys positions Synopsys to offer comprehensive simulation solutions, reinforcing their role from silicon design to entire system architectures.

Sassine Ghazi (67:20):
"Physical testing in the context of whatever that end device that you're physically testing is going to become more connected and smarter... simulation plays a huge role."

Art Digius (68:30):
"With accelerated compute... AI for a further acceleration... it's opening up the door."

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11. Collaboration and Globalization in the Semiconductor Ecosystem

The discussion touches on the necessity of global collaboration in overcoming the industry's challenges. Art and Sassine highlight how trust, collaboration, and continuous innovation are pivotal for sustaining progress amidst geopolitical tensions and evolving market demands.

Art Digius (57:27):
"The foundation is the same technomics... Synopsys emerged as hopefully trustfully good enough, but also needing and intending on collaboration."

Sassine Ghazi (58:19):
"Now it's very different... there's recognition that in order to drive that ambition of software... you're hiring chip architects without an intention to design a chip."

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12. Synopsys’s Societal Responsibility and Future Outlook

In the concluding segments, Art and Sassine reflect on Synopsys's broader role in society, emphasizing the company's responsibility towards community and ethical considerations in technology development. They discuss the balance between technological advancement and societal impact, underscoring the importance of informed and courageous action.

Art Digius (70:59):
"The systemic complexity of a single transistor today is unbelievable... having courage to act..."

Sassine Ghazi (71:47):
"The passion towards innovation... opportunity to be at the center of what you're working on is so relevant to many inflection points."

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Notable Quotes with Timestamps

• Art Digius (02:18):
  "It was all pretty accidental how that came about."

• David Rosenthal (04:49):
  "It's so rare that a corporate spin out into a venture style venture goes well."

• Art Digius (09:17):
  "We became quickly known... It made entirely new types of chips possible."

• Art Digius (17:24):
  "The cost of going to manufacturing of something that has a bug is, whoa, you made a big decision there."

• Sassine Ghazi (18:27):
  "The Value of trust is extremely high. But the necessity for verification is also."

• Sassine Ghazi (35:01):
  "Connectivity is the enabler."

• Art Digius (32:27):
  "We've contributed about 10 million x in productivity."

• Sassine Ghazi (63:49):
  "ANSYS is the leader in the simulation and analysis of that multiphysics."

---

Conclusion

The episode provides an in-depth exploration of Synopsys's pivotal role in the semiconductor industry, tracing its origins, evolution, and strategic expansions. Art Digius and Sassine Ghazi elucidate the complexities of modern chip design, the integration of AI, and the critical importance of simulation. Their insights underscore Synopsys's commitment to innovation, collaboration, and societal responsibility, positioning the company at the forefront of shaping the future of technology.

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For listeners interested in the intersection of technology, business strategy, and innovation within the semiconductor industry, this episode offers valuable perspectives directly from Synopsys's leadership.

Transcript

[Ben]

Hello, acquired listeners. Today is a very special treat. Our guests are the founding CEO of Synopsys, Art Digius, and the CEO today, Sassin Ghazi. Synopsys is the $80 billion company that makes the software that chip designers rely on to do their jobs. It is one of the two big players, along with Cadence Design Systems. The field is called Electronic Design Automation, or eda. It's a crude analogy, but you can think about it as the productivity software for chip designers, like the Microsoft Excel or FIGMA for that profession. And so much of the complexity of chip design these days has been baked into the EDA software that it makes entirely new types of chips possible that you couldn't do without them. They are the essential infrastructure behind the AI era and all the semiconductor innovation that we are experiencing today. No AI applications would be possible without EDA and the incredible optimizations that the software does for chip designers. And in fact, in a full circle moment, Synopsys even uses AI now to design the software to design chips. So with that, onto the interview with Art and Sassine. Art and Sasseen, welcome to ACQ2.

[Sassin Ghazi]

Thank you for having us.

[Ben]

We wanted to do a deep dive for listeners. It's been a while since we were in the land of semiconductors. We've covered Nvidia and TSMC and Apple and arm, so many of your customers and companies that you work with. But we have never hit the world of EDA directly. And so listeners, Art DeGeas is one of the storied pioneer of the semiconductor industry. 37 years ago, founded the company and really evolved to become an essential part with Synopsys of the semiconductor value chain and whole ecosystem today. And recently, Sasin, you transitioned and took the helm going from COO to CEO. So we have the unbelievable privilege of having both of you with us here today.

[Art Digius]

Pleasure to be here.

[Sassin Ghazi]

Yes, thank you.

[Ben]

All right, so this is acquired and we love history. So I think to ground the current state of the semiconductor ecosystem, why don't we wind it back to the beginning of Synopsys? So what did the lay of the land look like then? And how crazy was the idea for what would become EDA when you were first getting started?

[Art Digius]

Well, so what we're talking about here is mid-80s, right? And so just to put, I guess, a little stake in the ground, I was at Gen Electric designing at about 4 micron. I know you don't remember that that existed, but yeah, they were this big. That also says that General Electric was actually in semiconductors at some point in time. They invested in sort of the factory of the future. This was the future, sort of the same as AI is now. Everybody needs to have it. Well, then it was semiconductors. Things went pretty well until they didn't go so well. And didn't go so well meant in hindsight that in 1985 was the worst downturn in the history of semiconductors in the 80s and 90s. And I think it hit General Electric hard because that was a very large company. They had a very steady state, sort of dividend driven investor group. And so these ups and down in the semiconductor industry turned out to not be really their thing. Long story short, we're going to be laid off. And so it was completely accidental. But it was also accidental that in the five years roughly that I worked there, and especially last three, we had developed a number of design tools that actually were very innovative. One of those was synthesis. We were somewhat known because of that. And while on one hand I literally actually interviewed for a job, we're going to be laid off after all. Simultaneously we had this rebellious idea of, you know, what if we took the technology and looked if we could start do a startup. And we did it with, I think great care of thinking because we decided very quickly we're going to do that in full light of General Electric, meaning tell them about it and not take anything. It was a great company, they had taken very good care of us and actually it was just the right thing to do. And there was an opportunity to advocate this spin out with the technology which all was going to be lost. They were going to get out of this field with a small group of total people of seven. We essentially got out of GE with their support, both some financial support and the transaction on the technology for what's the equivalent of a million dollars of value. And fast forwarding, by the time we went public, GE pocketed 23 million. I'm still proud of that because they really deserved it. It was the right outcome, but it was all pretty accidental how that came about.

[David]

Wow. It's so rare that a corporate spin out into a venture style venture goes well. That's amazing. @ GE, were you all designing microprocessors or was this more specialized?

[Art Digius]

No, this was so called gate arrays and these were essentially chips for other customers. It's hard to remember a gate array, but a gateway was essentially a long series of transistors that have been prefabricated. They were sitting in rows. You use the first layer of metal to essentially take those transistors and make certain gates out of It a NAND gate, a nor gate, an inverter. That was pretty much the choice set. And then you use the rest of the layers to connect those to actually create the actual circuit. GE would do that for their customers and then manufacture these chips and gate array.

[Ben]

That's the GA in fpga, right?

[Sassin Ghazi]

Yes.

[Art Digius]

Yeah, I think so, actually. Yeah. Because it's the same concept.

[Sassin Ghazi]

Yes, it is. Exactly the same concept. Yes.

[Art Digius]

I've never heard that question. It's pretty good.

[Ben]

Well, it's funny you said Gatorade. I haven't thought about that in a long time. But FPGA is like. That's the current hotness.

[Art Digius]

You're absolutely right. Well, yeah. So we were hot already then, I guess.

[Ben]

So this idea that you had that you could turn it into its own company, was there a blueprint that already existed for chip designers need great software to do their jobs well, or was that sort of a novel idea that that could be an independent company?

[Art Digius]

Well, you really have two questions at the same time here. Why did we get into synthesis in the first place, which is a technical question, and then how to get to a company. It's sort of funky how we got there, because while I was there, there was a guy at GE who had explained in some seminar that if you used multiplexers, you could actually create circuits that would be denser than just and. Or inverts or nand Nor inverts. And so I talked to one of my designer friends and said, hey, can you put together the footprints that you need to do a multiplexer? And he did, and put that in the library. And then we'll design with those and get smaller circuits. The problem is none of the designers knew how to use them. And so after some reflection, we thought, why don't we just automatically design that? And somehow we managed to write a program called Socrates that actually did that and got quite good results. Although it turned out in the long term that multiplexers were not a good idea, because multiplexers are not restoring logic, meaning you put three in a row, your signal degrades, whereas with all the others, the signal stays a very square wave, which is what you wanted. But in the process, we became quickly known through some paper published as being on the frontier of this thing called synthesis. And of course, by that time, we discovered that IBM had worked on it for a long time, and so did Fujitsu and Toshiba and a whole bunch of large companies. But at the same time, we knew we had something because the results were astoundingly Good compared to what was the manual design done before? And within ge, they used it on the gate arrays with great results. And then the whole notion of, well, now suddenly we're going to be laid off and all of that is gone, gradually morphed into Talking to some VCs, talking to some designers and say, well, what about creating a company? You have to understand, at that point in time, I was a very young person and I had a bunch of way younger people because six out of the seven had all been summer students. That's sort of the cast of characters. And the notion of writing a business plan was an interesting concept. And I still have a couple of the books that I bought in the local Barnes and Noble of how to write a business plan.

[David]

You're like, jensen did the same thing when he had to write a business plan for Nvidia.

[Art Digius]

Well, although he was already closer to the business side when he worked at LSI Logic. Same concept, fundamentally. The one thing I just couldn't figure out was what is the difference between orders, revenues and sales? And to this day, I don't quite understand the difference between sales and revenue and orders. But for that we have people now.

[Ben]

As they say, hey, you invent a great product and those things will figure themselves out.

[David]

Deferred revenue, bookings, billings. One thing I want to understand quickly before we get to the company. Before synthesis and software, how was chip design done? Was floor planning done like architects, like with drafting boards? Was it pen and paper?

[Art Digius]

Oh, you're right with so many of those pieces. But the first thing to understand is there's fundamentally two layers. There's the functional layer and then there's the physical layer. When you talk about the layout, you already have an understanding of what the function is and what the building's blocks are. Now you actually have to physically design them and physically connect them. Right. We were working at the functional level, and there the notion is you have some complicated math function, a digital math function that you want to implement, and you need to choose the right gates. And there's a number of methods to simplify that. But ultimately you build a set of building blocks that you then connect. You typically did it on paper or then gradually on a schematics entry type thing. And then comes the question, well, how good is it? Well, fewer gates is better. Area was not really used. The substitute at that point in time was just the number of gates, because if you knew that, the rest was sort of determined. And then the other thing that was important, and that will turn out to be absolutely crucial. In how we differentiate it is we understood that the speed was key, and the speed is determined by whatever is the longest path through your design. And so we could judge if the circuit was getting better. Not only was it getting smaller, but also was it getting faster. And that combination turned out to be the key differentiator.

[Ben]

Fascinating. So, Sasseen, we have not yet gotten to your role in the story, and so I want to start from sort of your beginning with Synopsys. You joined the company in 1998, but I'm sure that in your jobs at intel and elsewhere, you sort of came across Synopsys before. So do you remember your first experience?

[Sassin Ghazi]

Yeah, I mean, as Art is describing to you, the synthesis, the gates, the function, then the place and route. So my first experience with Synopsys, I was doing my master's in electrical engineering. Actually, I was more on the control system side, so I did not touch Synopsys at all. After I finished my master's degree, I realized that's not the field I wanted be in, because most of the job opportunities at the time were controlling massive mechanical stuff, be it oil diggers or giant satellite or what have you. Then I started my PhD in VLSI design. And this is where Synopsys, my first introduction to synthesis. And as you're describing it, art, how do you build the library, the building block you synthesize? Back to your question, David. The largest design at the time that a single engineer could do was very limited by the number of gates because the actual software, from a capacity point of view, could not manage just the clock time to run and synthesize will limit how much can you design in terms of size of design.

[Ben]

Wait, so the physical on chip limitations were actually not the bottleneck. It was the ability for the design software to handle the complexity.

[Sassin Ghazi]

It's both, right, because first you need how much the software can handle the complexity and still meet your performance target. And Art is right. At that time, performance target was the key. Power area was so secondary, which 10 years later it became performance power, then kind of area. Now you optimize to the end all at the same time in order to make your requirement. When I started my career at intel, believe it or not, a lot of the stuff that the synthesis create, they were manually verified. So you lay out the transistors, you make sure you have the right width, right length, and how you connect them together to create the actual cells. So my experience with Synopsys was grad school. Then of course at Intel, I used many of the Synopsys products. And that's when the opportunity to join Synopsys came along. I was super familiar with the company and the support, the product R and D and the rest of history.

[Art Digius]

And so you joined because we were the only company had no bugs, right? Is that right? Can you confirm that?

[Sassin Ghazi]

Exactly.

[David]

Doing this synthesis class of problem, this is really, really hard to do, right?

[Art Digius]

And what made it particularly hard was there were of course techniques to optimize just the functionality and many of those were algorithmic. We added to that what we then called an expert system, which was look at certain situation in the circuit and say, this doesn't look good, but I know here's a better version. And so you would add so called rules to make it better. And you add a rule, it gets better. You add five rules, it gets better, you add one more rule, it gets worse. Because now you need a rule to manage the rules. I always like to highlight that it was an expert system because that makes us kings of AI 30 years later. But the fact is it was limited in its capability, but it was dramatically better than humans. And so by the time, and this was not even the first product, it was a prototype of the first product that we had as the minute we became a company, we talked to customers and they would give us one of their sockets that had maybe max a couple of hundred gates. They had worked on it for many, many, many weeks and in a matter of few hours we could literally give it back to them. 30% smaller and smaller meant 30% fewer gates and 30% faster, meaning shorter critical path. They would look at it and then they'd say, it's impossible, there's no way you did that. And then it would go away literally for two weeks. And then they would come back and say, well, I've checked and I checked, it's actually doing it. And then the expectations of course, went immediately way higher than what we could do because they had just encountered magic. Right out of that interaction, something very profound happened is for the majority, they became our friend's customers because they could say, yeah, but you know what you did here, that's not that great. And by being able to look at our circuit and say, that's not that great, it made them great. But they gave us a gift of feedback that two weeks later we had fixed based on their input. And therefore they become parents of the tool too, right? Everybody added something. And that whole first generation of two dozen, three dozen companies over time, they all had the same behavior, which is they rooted for us because they could see it happening on their own circuits.

[David]

It doesn't matter what kind of circuit you're making, whether it's a microprocessor or an analog system or a gate array, you need this technology, you need this optimization. And so Intel's happy that you are getting better, even though that's also serving.

[Art Digius]

Ti or you open multiple boxes here. For starters, we were strictly a digital company. Well, today we do a variety of things on analog circuits automatically, but that was far away. Plus, this was a cornerstone to really the digital age. And before Synopsys, and in all fairness, I should say before synthesis and before place and routes, the two go hand in hand. The field was called Computer Aided Design. You did stuff on the computer, but the computer essentially helped you do stuff that you did yourself. What was so great about synthesis? It actually created something. And so we were part of the transformation of Computer Aided Design to Electronic Design Automation. I remember we had a moniker for that is we're the only ones that have license to kill one of 007, because license to kill means we can actually change a circuit. And that was completely taboo before. If a tool did that means they had put some bugs in it. Right?

[Ben]

Right. You wouldn't want software intervening in your own workflow. The creativity was reserved for the human, for the designer. You know, please only aid me. Do not automate for me.

[Art Digius]

Well, it was more than creativity. It was the trust that it actually would work. People really could not trust that tools would change it for the better.

[Sassin Ghazi]

Yeah. And you know, it's amazing, even in 2018, when we introduced AI for the synthesis and place and route, believe it or not, the resistance from our users was, I want to know what the AI changed. We're like. But that's the idea. You cannot. It's just how many parameters do you want to understand? And there was a lack of trust for about the first two years. Even though the outcome, the results were always better using the AI system, they, meaning the users could not trust it or use it because they want to answer the question, you know, engineers, but I need to understand what did it do. Of course, right now is a different story. AI is so well accepted, that question is gone.

[Art Digius]

What I love about what Sassin just said is we are essentially a company that has repeated its own history over and over again. And I was almost tempted to say that we learned it all from Ronald Reagan. Trust but verify. Here's this AI stuff. And then you still need to simulate a lot to make damn sure that you didn't have an error in it. But the Value of trust is extremely high. But the necessity for verification is also.

[Sassin Ghazi]

That's right.

[Ben]

Right.

[Art Digius]

Because the cost of going to manufacturing of something that has a bug is, whoa, you made a big decision there.

[Ben]

Often I don't trust AI tools because when I do look at the output, I'm like, it's not clear to me that this is better than me doing it. And there are many situations where it is and many where it isn't. Do you feel like EDA is uniquely well suited toward a designer just letting go and saying, I trust the machine and I don't have to understand every little input?

[Sassin Ghazi]

Maybe what you're referring to, Ben, is generative AI when it's generating something through a natural language and you say this is 90% accurate, not 100% accurate. I'm assuming that's what you're referring to. In the EDA world, we're all about optimization, massive optimization problems. I mean, we're talking about many billions of transistors that you're jamming in a small silicon area and you're trying to optimize. Where do you place it, how do you route it, how do you get to the performance, the power? And you know, this is no way for a human to do it. So our industry has been very much in the space of using technology to optimize. So what we do for AI, we're doing generative AI, but put that aside for a moment, is using machine learning and AI algorithm to optimize for an outcome. But you always have, as Art mentioned, many steps before you commit it to manufacturing and say it's going to work. Meaning you don't just say, oh, that's what synthesis or AI generated is going to work. I'll go to whomever, your foundry and you spend many, many, many millions and the chip does not work. So you have many checks of verification. What we have pioneered with AI for EDA starting in 2017, right now, is used by dozens of customers in production. Meaning they're trusting it, they're trusting the outcome. But there are all the other checks you have to go through to verify that it's going to work once you manufacture it.

[Ben]

And to be clear, there's basically specific goals around size, around power efficiency and around overall performance. And it's basically optimizing within that set of constraints. So you can sort of prove when it's done that it's better than what happened before the AI came in.

[Art Digius]

Those are the outcome metrics. Right. And you just mentioned three or four. And that's what we optimize for. But you know what that forgets? It's not only that there's the other 10 trillion constraints that you have to meet that tolerate zero error. And the very big difference between many of the AI optimization things that we see in the world, and some hallucinate more than others, there are many very good ones, is that we have a constraint that is much, much harsher, which is absolute correctness in functionality. And by the way, the scene jumped quickly 25 years. And in those 25 years, there's been 25 years of revolutionary techniques and enhancements not only to what we do, but to the circuits that we do with our customers. And, you know, sitting on an exponential rate of change, that is a large number of revolutionary changes. And every single one, every single one has been delivered in an evolutionary way. With other words, you forget one lesson learned in, I don't know, 1997 Crosswall capacitance. You forget that nothing works today. Nothing. Well, he made it sound so simple. Yeah. You know, we know AI the heck out of it, we do. But that AI itself works on unbelievable number of parameters. And the rules specifically for the layout have no tolerance for error. You violate one of those, the yield will go zip down the drain.

[Sassin Ghazi]

But see, that's why, when the question often comes up, why aren't there EDA startups? Why is the market consolidated to just 2? It's that exact point that Art just made. The learning is not just, hey, can I train a model and then create an output? And I'm there. The cumulative knowledge to get to the current state before you look at the future state is massive.

[David]

It strikes me that there's actually a lot of parallels to the foundry business just on the software side. You can't just go recreate tsmc. Obviously, as we are seeing, it's all those cumulative years of learning about how to do this. And this is the same thing.

[Art Digius]

And the software, you know, it's interesting because TSMC was founded three months after Synopsys. That's in hindsight super interesting because that was simultaneously a change in the industry where the focus was going to go towards fabless design and then foundries that would do the manufacturing. And remember, before that time, many companies were IDMs, they had their own foundry.

[David]

And real men have fabs, I think was the quote today.

[Art Digius]

We wouldn't dare say it like that, rightfully so, but it was a very macho attitude. Still to this day, people that spend a lot of capital are really proud of spending the capital, but they also have no choice. Right there's a slight little fun anecdote, which is Morris Chang founded the company, but the first CEO was a guy by name Jim Dykes, who happens to be the general manager I worked under at ge. And when they closed, he went there. So it's one big family enterprise here. And the other anecdote I like to bring up, when we talked about those customers that use our stuff, and we learned from. I don't know if you recognize the names Chris Malachoski and Curtis Cream, of course. Well, they were at Sun Microsystems, and they were among our first customers and actually very good guys to work with. We know them extremely well. And then a few years later, Jensen showed up because he was the caretaker from LSA Logic, because that is where sun manufactured its chips. And of course, then they teamed up. I Forget which year, but 93 or something like that. And so, you know, of those three companies, we are proud to say that we are the ones that have survived the longest.

[David]

That's so great.

[Ben]

So scene. You got to tell us the story. So I was watching, and then we'll come back to everything else that we're talking about here, But I was watching the keynote, and it appeared that maybe Jensen arrived, like, literally seconds before he was about to run on stage because it was maybe concurrent with the week of gtc.

[Sassin Ghazi]

Yes. Yes. It's funny. My team thought I was joking. Like, I was setting it up this way. I'm like, no, I mean it. When I went on stage, he was not there yet. He was not in the building. But he was texting. He's like, I'm in the parking lot. I'm like, okay, great. I'm about to hop on stage. So, you know, initially the idea was about four or five minutes. Then I bring him on stage, and I'm looking at the clock ticking. I'm like, let me burn more time. Then I'm like, all right, should I continue with my presentation or wait, yeah, it was live.

[Ben]

It's wild. I'm sure this exists in other industries, but it has to be a very special thing in your industry that sometimes there's a company that becomes an unbelievably important company in the world, and you sort of get to be a huge partner to them in that success. I mean, it's absolutely fair to say that there's no chance Nvidia could do what they do without Synopsys software. How do you think about the role and sort of the importance in the world that you've really become?

[Sassin Ghazi]

As I mentioned in the keynote, I want to say, even in Jensen's words, because I was not putting words in his mouth, that the synopsis is mission critical to Nvidia's success. We don't take that lightly when we know we are mission critical to many billions of dollars of our customers revenue. It's a huge responsibility. It's a responsibility to continue on innovating because they're aspiring to build the bigger product, the next big thing. And if our software and our support, our ecosystem engagement is not able to stay ahead, I don't want to say with them ahead, so when they're ready, we have it, it won't happen. And right now where it's happening is with our chip customers as they're architecting the future product and with Foundry. And those two are becoming so important. So there's a triangle, always us customer, Foundry, that we are working on architecture, on physics and manufacturing and software to bring it all together. And I want to say that has accelerated in terms of being interconnected in the finfet world, you know, when the transistors start moving to more complicated manufacturing. And of course the last five, six, seven years when we say it's impossible to design and manufacturing those chips without our contribution as an industry, it's not an overstatement.

[Art Digius]

There's sort of also a historical perspective that, yeah, we can only be thankful for that we were part of this while now it feels old. Moore's Law essentially was the exhibit of what an exponential is. And an exponential is easily the toughest mother of mathematical function because staying on that sucker. Damn, it's going fast. Right? We were lucky that we had seminal technology at a moment where seminal technology was needed again to move forward and continue to move at an unbelievable speed. Not necessarily exactly the exponential that Gordon had predicted, but still the exponential that changed mankind. Right.

[Ben]

I think this is an important point that I don't want to just gloss over here. People take Moore's Law as if it's some derived from the natural universe property. The same way of F equals MA or something. It's not. It literally relies on companies like Synopsys getting clever again. Like every time Moore's Law happens, it's because somebody got clever again and oh my God, we just barely made it. You're the cause of it, not the result of it.

[Art Digius]

Well, it's interesting because Moore's Law, of course started just as an observation, right? He had seen this curve as moving up rapidly and then he made some prediction that it probably would continue for a while. That prediction then became sort of the. Well you have to do it, because otherwise you're not with the team here. Right? The race is on. And then that race itself started to self time itself against that. And by the way, that includes also the different switches to the different sizes of wafers, the ability to manufacture. And it was not always the perfect exponential, but the gestalt of it was absolutely. And this is not something new in humankind's history, the printing press had exactly the same characteristic. How in essentially 50 years, from virtually zero books, it went to 20 million and changed the world. And of course, in many ways, the industrial aids have the same characteristic. Again, what is so exceptional about this one is if we look at what we have done, synopsys, we've contributed about 10 million x in productivity. 10 million x. And you say, well, you know, are you going to do another 0.5 now? Hell no. We need to do another 10 to 100 2000x. And of course that's not going to be possible with just doing that on one chip. And I'm sure we'll get to the whole notion of sysmore and how that is changing things. But what is important are two things. One is that in order to stay on that exponential, you need to race like crazy. And the way you do that is you race with people that are crazier than you. With other words, you go to those customers that are even more paranoid of not being successful and that are thankful but never happy. That's the polite version, that's a good version. And they drive you crazy. And we've had the good fortune to, I want to say, for, I don't know, 75% of our products always be the state of the art for all this time, right? So we've been there. And I like to compare it sometimes to the Tour de France. You see all these guys biking like crazy and then suddenly there's three guys that move away from the peloton. And by the time those three guys are a couple hundred yards away, the others will never catch up. And there's a reason for that. The others cannot team up well enough. Whereas those three guys, by necessity and by scale, you know, every, you know, 15 seconds or whatever it is, they change the guys up front, but they chase each other until the last hundred yards, and then it's, you know, everybody's on their own. But in our case, the race never finishes. Our Tour de France is now 37 years and you need to keep going at it. But that combination is, has been unique in this industry.

[Ben]

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[David]

Yep, Vanta is the perfect example of the quote that we talk about all the time here on acquired Jeff Bezos. His idea that a company should only focus on what actually makes your beer taste better, that is spend your time and resources only on what's actually going to move the needle for your product and your customers and outsource everything else that doesn't. Every company needs compliance and trust with their vendors and customers. It plays a major role in enabling revenue because customers and partners demand it, but yet it adds zero flavor to your actual product.

[Ben]

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[David]

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[Ben]

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[Art Digius]

Well, I would say it always felt this hard. I think it's different than it was. But in the description that you gave, you have a relationship between the foundry and the equipment vendors. And you mentioned asml, Applied Materials. Those would be the ones that essentially focus on so how many atoms specifically and ASML would be focusing on how many photons do you need to get at which frequency in order to get really small lines. By the way, we are in that domain too because Synopsys is the leader in TCAT technology, Computer Aided Design, which is yet another simulation or modeling, if you like, of the truly menus. Cool. And then at the same time, we alluded to the fact that you have to align how you design with the building blocks that you have available and you can say, well, let's optimize the building blocks for what you're designing or let's optimize what you're designing for the building blocks that you have. This is often called DTCO Design Technology Co optimization, where Synopsys is a leader in. And what you notice in this story is they do the designs, they do the manufacturing, but we make it all happen, right? Somebody builds the LEGO block, somebody does the castle, we make sure that all these things hang together.

[Sassin Ghazi]

Well, I agree, Art, it's always been difficult, but I'll say the last six plus years it's been much more difficult. And the reason I'm saying that if you look at Synopsys relationship with Foundry, say six, seven years ago, we used to get input from Foundry called Enablement. And you enable whatever they create in our product and you provide the product to the customer. So you become the bridge between Foundry, customer semiconductor company designing on that foundry through enablement. So you take whatever they created and then you put it in your product and you give it to the customer. The last five, six years, it's impossible any longer to just do enablement. We sit with hundreds of engineers at tsmc, at Samsung, at Intel, at gf, sitting during the process development. The technology development is no longer enablement because enablement is impossible. You have to invent stuff with them to see will your physics, the way you're pushing it, will it handle the design you're aspiring to design on it. And that's a big change that happened. That is different than before actually by.

[Art Digius]

What you're saying, I was coming from the. It's the same because we have always worked as hard as we could.

[Sassin Ghazi]

Yeah, that's true.

[Art Digius]

That does not change. And the pressure for driving but I think what you're introducing is actually the notion that beyond scale complexity, we've now entered systemic complexity for every level. And the systemic complexity at the manufacturing size is that it's not sufficient to just understand sub pieces, you need to understand how the pieces work together. And I think that we benefit a lot from the understanding from the foundries, but they benefit a lot from the optimizations we do that now help them get faster and better transistors.

[Sassin Ghazi]

It's even to continue scaling. I mean, if you look at it now, it's really unbelievable to think about that the industry is talking about 18 angstrom, 14 angstrom, no longer nanometer. Those are not only from a physics limitation, you're hitting the limit once you want to put them into a production on a chip like, you know. The latest announcement from Nvidia was Blackwell 208 billion transistors. Imagine the heat those transistors are generating. So just from a thermal. So in physics, when you're designing that transistor, you say, oh, it will work. But once you jam them together and you run the software at a full workload, the heat that is generating. So even though the physics from a manufacturing it works, there are many aspects that you're hitting the wall that you need to plan design architect for.

[Ben]

Is it reasonable to say that before you just had manufacturing limitations working against the edge of what's possible, and now we're actually bumping up against the edges of physics governing what's possible completely.

[Sassin Ghazi]

When you think of the new wave of designing chips, and this is where art was talking about, you start designing through advanced packaging, multiple dies. Sitting in a package electronically, you can design it to function correctly and then you can manufacture it and package it to function correctly. Once you start running it in the field with the software workload, then you run into all kind of physics issues. Thermal is the biggest one. But thermal, when it creates heat, it may create warpage, it may create cracking. So things will start cracking mechanically. So you need to take into account all these physics effect during the design stage and during the process technology development. So it's absolutely multidimensional design factor. You have to take into account.

[Ben]

If I could just entertain a thought exercise, what do we need to do to get 4x8x16x more performance from here? What are the innovations that need to happen for that to be possible?

[Art Digius]

There are two things. The first one is well understood from many years ago, which is develop the hardware for the specific workload. Somewhat overly simplified. The first years of Moore's law is here Are more transistors, better circuitry? Write your software. And people say, wow, I can now do so many more. And then it's, oh, you need more memory, here's more memory. Write your software and make the world happen. Right? Then came gradually this conundrum of, well, yeah, but can you not make it faster? Like a lot faster. Especially all that visual stuff on the screen, it's so slow. Then out of nowhere somebody says, why don't we not use a general purpose processor to do pixels? And then the thing becomes called the gpu. And what does the GPU do? It loves pixels. It doesn't only pixels. It can do them forward and backwards and sideways and so on and out of that is essentially a specialized accelerator. And then of course they discovered that, well, it would be better to have 2 or 4 or 16 actually multicore, even smaller processors. And essentially what you have is now a workload that has determined the hardware that you need. Now advance that to 15, 20 years later and say the workload is driving a car without accidents. You can imagine that by saying, well, let's take your old 386 and see what you can do with that. You're going to go nowhere, right? You need actually a whole bunch of specialized machines from anything that takes the many sensors, data and compresses it or transports it and so on to ultimately the AI algorithms that can run preferably real time, to drive the car. And so one of the statements on that is called software defined architectures. And I show it sort of as this V from top down, because you're starting with high level functionality, drive it correctly and get there at the same time. And it's literally at the same time you come to the conclusion that chips that are more than 1 1/2 inch square, and I know that some people do whole wafers, but quickly gets to an end and adding another zero in the number of transistors is going to be really long, long haul. And so you say, well, what if we split functionality into multiple chips? What if we brought them really close together? And therein lies the essence, the word close together. Because the notion of having multiple die, maybe on an interposer, which is itself a chip, right, is not new, but A, it was difficult, it was expensive, and B, it was slow. And if you look at the evolution of the last 20 years, the single thing in my opinion that is empowering multi die is connectivity. Meaning we have improved dramatically, dramatically, not only reducing the distance, but the bandwidth, meaning how many pins you can do, how small these pins are. And how little energy they need to flip a bit or to pass a bit from one chip to another. Still way more than keeping it on the same chip. If we could just keep it on the same chip, that would be cool, but that's not going to be possible.

[Ben]

And this is of course the Blackwell, that's an example is the new Nvidia Blackwell chip, is that silicon interposer between two dies that enable super fast information to flow between the two dies.

[Art Digius]

And intel and AMD have very similar constructs and they all increasingly now look like there are 12 to 20 or so chips. And by the way, these chips don't all be processors actually need memories. And the cool thing with memories, you gradually can stack them and they stack potentially better because they don't create the heat that Cesim was creating in his processors. Thermal is absolutely one of the big killers in all of this and a few others. But the enabler is connectivity. And so if you now look at a picture of sort of bottom up from physics, you come to this whole new architecture that's really connectivity driven. You come down from software, as in software driven. The word architecture has a functional perspective and it has a physical perspective. And so that opens an entire new age. We call it Sysmor. So systemic complexity with the Moore's Law, exponential ambition. And I like to use the word exponential because I'm a strong believer that what we see happening is another 20 years of additional complexity and speed may have to be redefined as well. We do a whole bunch of things in parallel, but that's a different form of speed. Right, but any speed you can improve is still valuable.

[Ben]

So with Sysmor, what you're basically saying is we are going to abstract up one level what the notion of the system is. We're not measuring Moore's Law specifically on this one chip anymore, we're measuring it for your whole system. That where the goal might be drive this car safely. Are we able to optimize more components of it, to work together harmoniously to continue to achieve Moore's Law like outcomes?

[Art Digius]

Yeah, except what I would add is it's not abstracting one level. We've been abstracting more levels already for many years and I think that includes the software, the embedded software, the software that connects to other pieces, then ultimately the various forms of AI optimizations, and then still the domain specific knowledge of that. A great example of this is if you were to ask us, hey, if you really wanted to cut another 20% of the power, which layer would you start with? I Can tell you it would not be the transistor, it would be the software somewhere.

[Ben]

It's kind of like whenever I'm tempted to buy a lighter carbon bicycle, I realize that instead of spending $3,000 to shave an ounce, I could probably lose a pound and it would be nothing but advantageous.

[Art Digius]

Yeah, well, example, in case you are the software and you're a little too soft here.

[Ben]

Oh, of course. What's the old phrase about bicycles? N+1 is the right number of bikes to have.

[Sassin Ghazi]

Yeah, exactly.

[Art Digius]

I have a T shirt that says just one more guitar. And it's the same one more guitar and you're going to be a great musician.

[Ben]

The difference between you and greatness is right there. Oh, that's awesome. Okay, so if I could perhaps paraphrase the two things that you said. It's this idea that, hey, what if we admit that density is going to be really, really hard from here to get even more density on a chip. So either A, we can stop making so many trade offs in the hardware to accommodate general purpose computing and just make specialized hardware. And B, we can horizontally scale. We can just connect more dies together. So we basically have more compute. And yeah, it's going to take twice as much space for twice as much compute, but at least we get twice as much computer.

[Art Digius]

The only thing I would slightly tune in what you said is all these things multiply and so you sort of don't care at which layer of abstraction you can have an improvement. If you can improve the transistors by 5%, that's still 5%. That applies to a lot of things. Right? And not all things benefit equally. And so it's been interesting to listen to some of the people that manage big compute centers. They would say, you know, I don't care how much power use on the processor because if you make the processor faster, I can leverage that on all the other chips that are expensive to buy or to run. So systemic complexity is fundamentally defined in the simple math of multiplications, whereas scale complexity is mostly additions. And so yeah, we like to have more transistors, but it's the multiplicative effect that changes what you can do.

[Sassin Ghazi]

In my mind. There are other factors too. Since you listened to my keynote, I called it on two vectors. One is the march to Angstrom. There is always the opportunity to advance on Moore's law. Then there is the march to trillion the trillion transistors, which will only happen through multi die architecture decisions you need to make. Technically they're both doable. The decisions our customers are making are financial decisions. Does it make sense, let's say for your next phone to have a chip that may cost $15,000? The answer is no. But hey, you can run AI on the edge, it's going to be very cool, it's going to be super fast. Yeah, sure, but you cannot afford it. Some of those chips we're talking about, they're selling for 25, 30k a pop for certain applications because the yield is horrible, because you're pushing the limit of everything. So the architecture is not only technical decision, it's a economical decision. You have to decide and then how much do you go down the Angstrom, how much do you go up the architecture for that trade off?

[Ben]

Put another way, this is an N dimensional space where the dimensions are actually different for every customer and use case and yet you are still trying to optimize them and produce sort of the best product suite that you.

[Sassin Ghazi]

Exactly. That's why right now when we are talking to our customers, we're not talking about, hey, we have this product design, whatever you want with it. We have end market specific, we talk to automotive customers. A very different conversation than the mobile, than the data center. For all those reasons that changed again six, seven years ago. We did not have those end market focused discussion because it's the same product. You can develop on the same rhythm of Moore's law and life is great. Now there are all these trade offs that you need to take into account.

[Art Digius]

The foundation is the same technomics, right? Every technical decision is simultaneously an economic decision, be it for the build or for the use side of things. If you go back to the very point we started, which was here's a synthesizer that creates functionality, which is the value and it does that with the performance side and the number of gates. And the number of gates is essentially the anomics that determines how expensive it's going to be to manufacture that. And that has now taken so many dimensions. And if you look at, let's say the manufacturing side, these expansion boosts have been almost order of magnitude over time because okay, are you going to do a 300 millimeter fab now? We did 200 millimeter. Well, the entire industry has to retool for that and you bet it becomes very much more expensive. And right now there's no visibility to do 400 millimeter, partially because it's too hard to coordinate an entire industry to get to that point. And so the economics at some point in time taper off. And this is where innovation comes in, of course. Can you do it differently right and so multi die suddenly is an answer to that.

[Ben]

Fascinating. This question always seems to be divisive for people in this industry. Is EUV lithography a technology that's going to get us through over the next decade or two or do we need to find a new better way?

[Art Digius]

Yes to both. Yes and yes. I mean there's still much to do and there are new generations of these machines coming at the same time. There's also a lot of development in the manufacturing from a material side with as much as possible self aligning devices. So where you don't need a mask for every layer that you depose and also for places where you can actually, let me call it, erode material under other things on a sideways fashion. And so the reason I'm on purpose a little bit open ended on this is because we have learned many, many times that saying no always turns out to be wrong. And being at an advanced semiconductor exhibition or I should say conference as an undergrad student in 1978 and the leaders in the field were all unanimous. Electronics going to be big. And you know, one micron of course is the physical limit. Many years later I had the opportunity to give a medal to one of the guys had said that and of course couldn't resist bringing it up what he had said. But at the same time so great to give the medal to the very person who had predicted impossible and then was an engineer and made it happen to get around it. And this is happening in the core Angstrom race as Sassin mentioned. It is happening right there. And remember it's. What is it? Fifteen years ago, Finfet. It will never happen. These vertical wobbly things that you can barely. It will never happen and for sure they will never be in cars. And here we are. Engineering is very different than science. We work around science.

[Sassin Ghazi]

Yeah. EUV still have a lot of mileage that can serve. I don't know, I'm not too familiar with what will come next after it. But it's still an early adoption from a process technology point of view. And when was the transition happened? I mean number of foundries resisted it for cost reasons, fell behind. Then you're like, you know what, forget the cost. If I want to stay relevant, I need to be on it. What's next? I don't know.

[Art Digius]

Well, I mean the ASML folks, the technology leaders say they see another decade of the delivery. But we all think so, right?

[Sassin Ghazi]

Yeah. It has mileage. Yeah.

[Art Digius]

And if they can't, we synopsys will help work around it.

[Sassin Ghazi]

Yeah.

[Ben]

Engineering.

[Art Digius]

Engineering yes.

[David]

On that front, I'm kind of curious. Synopsys is a wonderful company, Great revenues, incredible market cap, all these things. But at some point along the way, you kind of became something more too. You are one of a few linchpins in the system. Did you see that in the beginning of like, oh, you look at any exponential function and it goes, as long as Moore's Law effectively has, like, it's going to undergird the world eventually. When did this become apparent to you?

[Art Digius]

Well, you know, I think Cecina alluded to one of the aspects on technology side, which is when the relationship with the top foundries started to change, because suddenly they had touched some boundaries that they couldn't get beyond, and we needed to get their information in order to be able to model what the circus actually would be able to do. And so I will put every one of those under the notion of systemic complexity. And by the way, systemic complexity is not a last 30 years, not in future years. Whatever you do, once you reach some boundary, systemic complexity becomes the thing that you have to handle around that thing. And the systemic complexity of a single transistor today is unbelievable. Right? But we wouldn't have thought of it as super simple Lego blocks. And so this has happened. The second thing is when the architectures started to somehow have a wish list on physical behavior, which was far away from where they were. And so suddenly they had certain desires of how fast to access the memories in order to be certain computations, and vice versa. Two domains that had been nicely separate for good reasons. And once they get closer and closer, suddenly they are one. And that is a moment of systemic complexity. There was a movement coming down from that perspective. And then in a whole different camp was the notion of globalization had been successful. And suddenly you dealt with parties literally all over the world that could only be successful by having a chain of participation and collaborations. And so if there's a singular skill that matters more in systemic complexity than anything else, it's a combination of trust and collaboration. And I think Synopsys emerged as hopefully trustfully good enough, but also needing and intending on collaboration. And that was fantastic. And of course, the fact that there's deglobalization in the world in the last, what, seven, eight years complicates things for many people. But at the same time, it's a skill set that's still relevant for the future, and it's going to be way more relevant, not less.

[Sassin Ghazi]

Maybe another way to answer your question, David, if you go back maybe 15 years ago, our industry was not that exciting. It was so hard to recruit. It was so difficult to bring in young, fresh blood out of school into not only eda, EDA and Semiconductor. I remember when I was a GM of the R and D product development, one of the initiatives was how do we excite the next generation to study electrical engineering, to study computer engineering? Because it was like, nah, it's not exciting. I want to study. Be more on the, on the software side. Maybe that's where you were in your background.

[David]

When I started Inventure in 2010, we had a startup EDA type company that was in the portfolio and it was like the black sheep.

[Sassin Ghazi]

We were like, yeah, that's right.

[David]

Yes, we want to fund like Facebook.

[Sassin Ghazi]

That's right, that's right, exactly.

[Ben]

Who now I believe is a customer of Synopsys and designs their own chips.

[Sassin Ghazi]

That's right, exactly. So now it's very different. Very different. Because there's a recognition that in order to drive that ambition of software, of applications, et cetera, you can for sure buy a general purpose chip, but you're not going to be competitive. So how do you customize from the silicon all the way up to the system to the application that you're designing? That's why many companies who can afford it, they're trying to develop their own silicon or architect their silicon, because they know the importance of the silicon in the context of the software and the apps they're building. If you ask me 15 years ago, do I envision we're going to be at this point, I didn't see it. We could see that we're going to march down Moore's Law, but now with AI as a huge opportunity to disrupt every market, then every market needs to go through its own transformation at the software level, system level. The way they're designing their end product and what's powering it is the silicon. But not by itself in isolation. Silicon in the context for each end market application.

[Art Digius]

So just triggered a thought by being in the vertical market and making this vertical movement. What has changed is we started in a technology where it was a technology push and then there was an economic success of the people applying it to software or whatever it was. What has happened now is the technology push continues, but there's an end market and markets, plural pull. And having a technology push and a end market pull accelerate things substantially. Right. And of course, meanwhile, everybody's inundated by big data. What the hell do you do with that? Well, you need to process it somehow. And by the way, it's going to Change your business. Well, those are very big statements, right. And then they come to the semiconductor world and say, you know, I need something much faster, much bigger. And we're like racing forward. But we have direct impact in their P and L on the profit part, on their differentiation. Whereas in the past they partially looked at us well, yeah, expensive tools. And now it's like we open the door with them and for them it's.

[Ben]

A heck of a tailwind as the world has this pull that you're talking about. But also as specialization of computing becomes more and more important to eke out that next frontier, you used to just sell to a handful of companies and now there's a strong incentive for many more companies to design their own silicon. Specifically, I think it's true that eight of the top 10 market cap companies in the world design their own chips. So the only companies that don't, to my knowledge, maybe it's some secret project, are Berkshire Hathaway and Saudi Aramco. Your customer base has exploded.

[David]

Berkshire is half of Apple.

[Ben]

That's true.

[David]

Half of Berkshire is Apple.

[Art Digius]

So do you know anybody there we could call to help them? Right, right.

[David]

Yeah, right, right.

[Ben]

Your customer base has exploded and the sort of level of importance of silicon in their business has also exploded. So you have this like dual access tailwind that's helping you.

[Sassin Ghazi]

Yes, yes. Actually, the number that I typically share that people get big eyes when they hear it. Fifteen years ago, pretty much 100% of Synopsys revenue was semiconductor companies. Today, 45% of our revenue, and of course, we went from a billion and a half to 6 billion in revenue. So the base got much bigger. 45% of our revenue are system companies. System companies, meaning those are end market OEMs that they develop an end market. They don't sell chips, they're selling a product. So that gives you a sense of exactly the point you're making.

[David]

Fifteen years ago, I can't imagine the CEO of Toyota would come see you guys, but today they are. Right.

[Ben]

Or Ford, I think literally is an example of Ford designs their own chips. Maybe every car company does now, but that was always a thing that they bought through intermediaries.

[Sassin Ghazi]

Exactly. The key point, though, even if you're not designing your own chip, right now, you're talking to Synopsys. So say you are an automotive oem that you have no intention to design your chip. However, you need to architect your electronics, given the context of electronics is going to get higher, bigger and bigger and bigger, given electrification, autonomy, et cetera, et cetera. So you're hiring you, the automotive oem, you're hiring chip architects without an intention to design a chip so you can architect your electronics in the car. And those are customers of Synopsys because we have software that enables them to virtualize the entire electronic system. That's the exciting opportunity of the future.

[Ben]

Fascinating. Okay, I can't believe we've gotten this deep in the episode without asking the question. In January, the news broke that you are making quite a large acquisition of a company called Ansys. What is the logic there and how does it all work together?

[Sassin Ghazi]

You know, we touched on number of the why and how the world is changing. I want to describe it in two reasons why we're doing it. Reason number one, deep in our core business, as I mentioned earlier, the challenge of going down the Moore's Law is no longer in electronics. Only challenge is electronics and deep physics when it comes to putting these devices in a chip, thermal, structural, et cetera. And Ansys is the leader in simulation and analysis in those spaces. So that's in our deep core business. The other vector is what we just touched on as well, which is many system companies, let's continue picking on automotive as a system oem. They're trying to figure out how do I design my whole car that has bunch of electronics that is going to trigger a mechanical action, that's going to trigger a number of other physics action. They call it multiphysics, meaning different type of physics analysis that you need to do. How do I design the car? With a way to simulate everything upfront, that is a digital twin of a car, including the electronics, the mechanical, etc. And again, ANSYS is the leader in the simulation and analysis of that multiphysics. So we see the opportunity at the silicon level and at the system level. And that's why we are describing our company as the design solution from silicon to system. And we're looking forward when you bring two great companies to really deliver the engineering platform of the future. It's an awesome opportunity we're excited about.

[Ben]

I might be oversimplifying here, but you know, there's some set of things on the EDA side that Synopsys does really well. There's some things that Cadence does really well, but in simulation there's basically just Ansys. Everybody needs Ansys. Does that feel like it's a reasonable characterization?

[Sassin Ghazi]

Remember the discussion we had in the beginning? There's the cumulative learning that you have in order to be the trusted simulator and Ansys in number of simulation. When I Say they're the industry leader, meaning they're the trusted simulator, because they've had a history of 40 plus years of cumulative evolution of their simulation. To be clear though, in every space, same as you describe in eda, there is Synopsys and there are a number of other companies that we compete with in their space is the same, is the same, but their history of that cumulative learning, they are the leader in having that history and that what's called the sign off trust, meaning once you do the simulation, can you sign off that I can trust that outcome. And that's the key in what they offer and what Synopsys offer.

[Ben]

Fascinating. Simulation is such an interesting area because on the one hand, it can help your customers do better, literally use your own existing software packages better. And then in addition to that, now that all of this new hardware complexity and AI exists, we are going to get better as a species of simulating way more things in the world. And so it also creates more demand for everything else that you make to the extent that the market for simulation broadly is going to grow.

[Sassin Ghazi]

Exactly. So one of the thesis that we explained to our investors after announcing the acquisition is picture the world five plus years from now, that physical testing, physical testing in the context of whatever that end device that you're physically testing is going to become more connected and smarter, right? Because it's going to have some sort of chip in it, because it's interconnected and smarter. It gets too expensive and too long and just not practical to physically test stuff. So simulation plays a huge role then in that same context, when you think of simulation, you think of digital twinning stuff, you think of virtualizing stuff. And this is where we see our core competency of what we've done at the silicon level. Because that's what you do when you design a chip. You virtualize, you model, you simulate, you can do it. Now for much bigger systems than the.

[Ben]

Chip, simulation has always been a good idea. It just wasn't technically possible for that many use cases before. And it seems like we're now getting more and more fidelity on the physical world of simulating more complex projects.

[Sassin Ghazi]

And you have accelerated compute where before it may take you weeks to simulate a very small function. Now with accelerated compute, one of the slides was presented at GTC and in my keynote was 10, 15, 20x speed up due to accelerated compute. That's a massive speed up. Then you layer on top of it AI for a further acceleration. How do you get smarter? In what do you simulate more effectively, efficiently, et cetera, using AI techniques. So it's opening up the door. Exactly what you said, Ben. For more applications to simulate.

[Art Digius]

You're describing the company mostly through technical terms. Right. But the reality is it is a group of people, first and foremost. One of the things that has helped Synopsys, precisely in this notion of all this learning that Sassen was talking about over the years, accumulating, that has been enhanced greatly by having many people here work here for a long time. And both Sasine and I are sort of examples of that. And at the same time continually rejuvenate, both with new people or different people, but also in our own learning of how you do things. And I think that is a fairly unique characteristic. And of course, there are some companies that we admire greatly because they showcase this. You mentioned Nvidia, but I would certainly put TSMC also in that category of never quite being satisfied with yesterday. Yet tomorrow is impossible. But Tomorrow is only 12 hours away, and so you better get going. There's a passion for making the future happen that is probably half grounded in utter paranoia, as in only the paranoia survive, and partially also in a belief that things are possible that we still have to invent. That is a very unique recipe for companies, and I think that that is certainly one of the things that characterizes synopsis.

[Ben]

Well, thank you both for your time. My closing question that I've been enjoying recently is let's flash back all the way to where we started, the episode both of your first experience with Synopsys. What is the same today as it was then? And what's something that couldn't be more different?

[Art Digius]

Why don't you start.

[Sassin Ghazi]

The passion towards innovation. It's always been there from day one, as Art just made the last comment he made. It's always been what we're working on tomorrow is almost impossible, very difficult to do. That's an industry. That's a privilege in our industry, because that's the key to innovate. You talk to our engineers. They love the fact they're working on the most complex things known for humankind. That's not only not stopping the opportunities right now to monetize it. The opportunity to be at the center of what you're working on is so relevant to many inflection points that they're happening in front of us. That's thrilling.

[Art Digius]

In my mind, I probably, maybe not by accident, land on sort of the same as the scene has, which is we've touched the exponential and it's in our DNA and that sucker is not going away. It takes different forms right, but 10x is still 10x, but the next 10x is of the old 10x. Right. That rate of change is just exceptional. And having been in some ways somewhat central and a big piece of that roadmap is a privilege that is amazing. At the same time, if you look at the rate of change for us as a company in terms of size and then of course complexity, but also of the world, we started this where the Far east was not very important yet. And today it's one of the dominant parts of the high tech ecosystem and it's also part of one of the big stress fields in the world that adds an enormous amount of complexity. And so being now a company that is in the middle of these type of things, that certainly needs to have opinions but also careful actions of how you behave in a political minefield. How do you behave in a situation where you see our industry is going to go to about 10x more energy utilization and without all the ramifications of touching what is happening in terms of climate change. An industry that simultaneously we have had multiple countries in various states of doing well or deep war. And how do you deal with that? Brings a set of questions to us that as leaders we have to learn just as much there as anywhere else. And it's interesting to what degree companies are now counted on as both sort of, I don't want to say too strongly moral centers of gravity, but certainly value centers of gravity as people are finding difficulty finding it in the political environments or in some cases finding it really well, some cases not refining it at all in religion. And so now the question is what societal groupings matter. And we've for a long time said that they who have the brains to understand should have the heart to help. In other words, we're co responsible for the communities that we're part of. And by now the community is humanity. Right. And so we've started to modify it a little bit into they who have the brains to understand should have the courage to act. And that is different than before.

[David]

I can't imagine that was part of the business plan that you were going to Barnes and Noble to figure out.

[Art Digius]

Well, you know, we participated in a march for in support of people having AIDS in the 1990s and there were very strong opinions that said, well that's not cool. Because, because, because all things that today we think, as you know, this was middle ages thinking and there's still a lot of middle aged thinking now. And so I don't know where that leads. And I think we have the great fortune to have not only new the leadership that can give the next decade of moving it forward in a company that does well, but is at the same time the question so what position do we take in the world that is more than synopsys as a tech, maybe super tech company, but as a tech company, as a human company? Those are interesting questions.

[Ben]

I can't imagine a better place to leave it. We're going to have to do another episode to explore all of that. Thank you both so much for your time.

[Art Digius]

Thank you for your great engagement.

[Sassin Ghazi]

Yeah, that was fun. Thank you both.

[Ben]

Thank you, listeners. We'll see you next time.