Hi, everyone. Welcome to join us at the 20th Annual NEON Technology Media and Consumer conference. On the virtual stage with me today are Nimish Modi, Senior Vice President and the General Manager for Strategy and New Ventures, as well as Richard Gu, Vice President of Investor Relations, from Cadence Design Systems. Before we begin, a couple of housekeeping items. I'll first read the following safe harbor statement on behalf of the Cadence team. Today's discussion will contain forward-looking statements, including Cadence outlook on future business and operating results. Due to risks and uncertainties, actual results may differ materially from those projected or implied in today's discussion. Next, since this is a virtual fireside chat, please feel free to submit your questions in the Q&A box. We'll have some time, near the end of this session, to allow our guests to address your pressing questions, directly to Nimish or Richard. As usual, we'll keep your questions anonymous. All right. Nimish, really welcome back. Last year, you were here with me at the same conference. That was the first time, I think, we had, we've known each other for a little bit longer than that, for sure. But, welcome back. A lot has changed, over the past 12 months, right? It feels like Cadence hasn't really changed much, I mean, in a positive way. The business is still growing. The top line in low double digits, your operating margin is still in the 40s and still expanding, right? While there's this increased worry about the macroeconomic conditions as it relates to maybe AI infrastructure build-up cycle, and maybe more specifically the semiconductor cycle as of today, partially due to some really meaningful change. As we have seen today, the volatility in the policy landscape. Let me ask the first question this way, Nimish. You guys definitely talk about resiliency of the business through any market uncertainty. Tell us why Cadence is uniquely positioned in such a way in the semiconductor industry, and what leads to this resiliency, which looks like a structural inherent to the Cadence business. Sure, Charles. First of all, thank you for inviting me back. It's good to be back. You know, at the macro level, as you said, in many ways, since the last time we had this chat, a lot has changed, a lot of movement at the macro level. At the same time, this just underscores some of the cornerstones about our business that makes us that much more buffered and resilient. If we kind of break it down, first of all, you know, we are essential to our customers. You know, the semi and system companies are primarily our customers. We are tied to the customer's R&D budgets and their design cycle. You remember that the customer's design activity of today is translated into the product which is coming out in the market two to three years down the road, right? They want to be ready to come out with, you know, their competitive products on the other side of any potential downturn, which may be, you know, in the offing. Core R&D is usually one of the very last things which gets impacted. Secondly, from a Cadence perspective, we are very diversified in terms of products. You know, we got all kinds of products, right? End-to-end core EDA products. We got IP, growing IP portfolio, which we'll talk about later, system products, hardware. You know, we have a very diversified customer base, you know, across our semi and systems customers. We do not have any 10% customer. We are also very broadly geographically diversified as well. Lastly, and very importantly, you know, we have a predominantly recurring business model, right? It drives a lot of, you know, visibility, predictability. You know, very high gross margins, strong exit, you know, Q1 backlog. When you and the radical model, which is predominantly recurring, put it all together, we feel pretty good about, you know, how we are set up to withstand this. Now, again, to be clear, we're not completely immune to, you know, if there's a significant and prolonged downturn, you know, we'll see. Otherwise, we are situated very well. You can see the multiple elements that are driving the structural resiliency in the business, which is why, you know, this environment notwithstanding, we beat and raised when we reported Q1. Thank you, Nimish. Maybe moving on to some of the more recent events, I want to talk about the product announcements you guys made at Cadence Live, which happened a couple of weeks ago. One is Millennium, and the other is about the Tensilica. I want to ask you more about the Millennium here. I think Jensen Huang, the NVIDIA CEO, he showed up, and he had a fireside chat with Anirudh. He talked about buying 10 units of Millennium from you guys, right? I thought that because thanks to you guys, Millennium, I think you guys launched not this year, but probably in the prior year. I thought that it was a hardware product for something called CFD, computational fluid dynamics. I recall the target customers were more like car companies, aerospace, defense companies, but now NVIDIA is a customer. Can you tell us what Millennium is about and why Millennium today feels more like a new kind of EDA hardware and much, much more relevant for the semiconductor customers? How should we think about, like, product positioning between, let's say, Millennium and other existing Cadence hardware products, like famously the Palladium and the Protium? Yeah, great questions. So, you know, we are super excited about the Millennium M2000 supercomputer, which we launched last week. I mean, it's got tremendous potential to really bring some significant change to our customers, you know, across the engineering workload, science workloads. You know, we have a very strong and growing partnership with NVIDIA. And as you mentioned, Jensen on stage, you know, announced that the, you know, the PO for the 10 units, but there's a lot which goes, you know, has been leading up to it. We've integrated our massively scalable solvers with NVIDIA Blackwell Systems. You know, and the numbers are in terms of performance, energy efficiency, you know, 80x that we talk about in terms of performance, 20x, you know, more energy efficient. So, as you pointed out, you know, last year, we launched the first application on Millennium, which was CFD. But your question about, you know, how is it extensible beyond, you know, CFD? Without going into too much detail, but, you know, take a step back and let's look at the architecture, the software elements of traditional GPU-based systems, right? Historically, GPUs have been, excuse me, very, very good. They've been exceptional for highly parallel, very dense computation, right? Especially matrix multiply operations. It makes them very ideal, you know, for AI workloads, training deep neural networks that involve billions of dense multiplications. CFD simulations are very similar in terms of structure over there, you know. Lends itself, these kind of simulations lend themselves to more regular computation. EDA is different. EDA workloads are fundamentally different. They involve what we call sparse matrix operations, require double precision floating point, FP64. Memory accesses are very irregular. All of these characteristics have made it very hard for, you know, EDA to be ported over and do well optimized on GPUs. What has happened over time is GPUs themselves have become more and more general purpose, if you will. When you have architectures like Hopper, like Blackwell, these GPUs, they support, you know, very efficient sparse matrices. They have very large on-chip memory, very high bandwidth. They also support these irregular flows, which are data dependent, which is very common in EDA. Anyway, the point being that, you know, the GPU architectures have evolved to make themselves more in line with what the requirements are for EDA workloads, number one. Number two, Cadence, instead of just porting over our EDA, you know, workloads over to the GPUs, we re-architected the solvers, right, to exploit the GPU parallelism, to minimize the data movement bottlenecks and the like. Thirdly, I think NVIDIA has just done a phenomenal job with their GPU optimized CUDA libraries that they help tremendously, you know, in terms of just out of the box, you know, kind of performance improvements alike. You put it all together, and then that's where you see all those kind of very strong numbers, you know. On EDA workloads, it's very impressive, you know, particularly impressive, not just the speed up, but you can do things which you were just not able to. MediaTek said that, that they can now run voltage drop simulation that was just not possible before. That's, you know, what's changed, and that's why it's so exciting. Now you can apply Millennium across, you know, EDA, SDA, and Bio as well. I mean, we gave some numbers on Bio with Trinean Biosciences as well as a reference. Now to your question about Palladium and Protium and Millennium, how are they all different? They're used for very different purposes. They're very fundamentally different. Palladium and Protium are for, you know, what you would call accelerating kind of Boolean operations, you know, Boolean computations, which are very prevalent in functional simulation, you know. The architecture is much more structured, much more simple, and Millennium is more for numerical simulation. These two are complementary to each other, you know, specific different purposes, and, you know, both are very much needed for different elements of the workflow. Lastly, on your question on Tensor, we also announced that Tensor got NeuroEdge. I mean, it's really a co-processor designed to complement any neural processing unit and handle tasks which are best offloaded, you know, by the main NPU. It does very well. We talked about seeing 20-30% smaller area, 20% dynamic power savings. Exciting as well. Yeah, I mean, this was a good, you know, good batch of excitement, of exciting announcements. Millennium, especially, we feel really, really, you know, given the breadth of the applications that it can help accelerate, we and our customers are very excited about that. Thanks, Nimish. If I may, I do want to ask a follow-up on Millennium as it relates to EDA. It sounds like you are basically talking about GPU is moving closer to EDA workloads, and you guys are pushing EDA workloads closer to GPU. It looks like increasingly, it sounds like EDA can be run on GPU platform. Millennium is a hardware product. I just want to ask you this follow-up question. Do you more imagine Millennium to be a hardware business, or maybe it's more like a cloud business, or what do you think about the future here? Yeah, yeah, yeah. Charles, the way to think about this is, you know, Millennium is actually not just a hardware product. It's a hardware plus software co-optimized offering that we are providing. You know, this is where we, you know, talked about our solvers, EDA solvers that we have re-architected to make them much more, quote unquote, GPU friendly to take advantage of some of the GPU enhancements in the Hopper and Blackwell platforms, you know. Now, when you provide that in that context, we are still offering our solvers standalone for traditional deals that you have, the two-year, three-year, you know, term deals that you have with the software, and customers can use it that way. In this context, we also apply or have these solvers available in the public cloud, you know. In the public cloud, you know, Amazon or other hyperscalers, they have CPUs available, they have GPUs available and the like. That can also be, you know, and then we also have our own Cadence on cloud. This is our own data center. You know, we offer this, you know, offering through the cloud as well. You can either buy the Millennium box as an on-prem appliance, or you can also access it through the cloud. A lot of flexibility in terms of use models. Of course, the business models will, you know, evolve with that as well. The key thing is the Millennium is not just think about the Millennium itself as a, you know, it's the sum of the parts. That's where you get, you know, exponentially good value when you're co-optimizing all these things, the solvers, the GPU, the libraries, all together creating that box and you're providing that. That's where you're getting that special extra in terms of performance and energy efficiency. Thank you. This is such a new thing and many, many optionality possibilities. Definitely excited about that. We'll see as we go and see how you guys really lead in this category. Maybe the next question, we talked about hardware, right? I want to switch ggears a little bit to the more traditional hardware. Palladium, Protium, you know, these are, I would say, upside drivers for Cadence for a while, right? You guys launched a Z3, X3 last year. I want to ask you this, which ending are we in over that Z3, X3 product cycle? I mean, the reason why I ask this is very simple, right? When we look at, when we think about the product life cycle, how much revenue opportunity it can be for Cadence, we look at the Z2, X2, right? Z2, X2 launched in, I think, April 2021, right? Right. Revenue, right, you guys are not giving the disclosure, but we can look at upfront revenue, right? It is ramping up throughout 2022 very strongly into at least the early part of 2023, right? It looks like now you guys transition to Z3, X3. I am sure you guys have done some market sizing. How much bigger do you think the Z3, X3 product life cycle can be versus Z2, X2? Plus my earlier question, which ending are we? Yeah, so I think the way to think about this, I mean, again, the emulation, the prototyping, which is really for, you know, verification. Verification is a very, very significant, probably the most complex, you know, kind of problem which the customers deal with because, you know, there's a lot of push on first-time pass silicon and, you know, the cost of respins, the market opportunity and all, you know, it's just something which is driving more and more of this verification to be done as early upstream as possible. While there are multiple ways to do verification, I mean, simulation and virtual prototyping and formal analysis, it's really the hardware accelerators which really, you know, are the ones which can run at tremendous speed. You know, for the complexity of the design. Verification is an NP-complete problem, right? You're never done with verification. You're never done with verification. You just got to get enough confidence before you tape out that you've really flushed out, you know, all the significant issues and the like. With that, you look at the workloads which are out there and you look at the systems and all, you know, AI super cycle, a lot of these chips which are, you know, the AI chips are all, you know, most advanced nodes, reticle limited, you know, and then all the software workloads on top. You're verified not just at the chip, but, you know, at the system level with the software. All these drivers are what's driving the need for more and more, you know, verification and emulation and prototyping are very key cornerstones of the verification kind of portfolio. As you said, I mean, you know, the last cycle or the last Z2 and X2, 2021 is when we launched them. And then, you know, we came out with Z3 and X3 in three years, you know, after that. I mean, the demand has just been incredible. I mean, you know, we can't build them fast enough. When you talk about the innings of this, you know, the innings statement is actually, you know, kind of relevant if the game is constant. The game itself is changing. You are, you know, the innings is changing as well, if you will. I would still feel like we are still in the early stages, you know, of this cycle. You know, we are seeing customers, you know, again, very broadly diversified, but it doesn't matter, you know, whether you're a big digital customer, AI customer, or you're doing mixed signal design and the like. Verification is something you really, really, really got to continue, you know, kind of really flexing and making sure that you're, you know, kind of pushing through the system level piece of it. I think stay tuned. I mean, I think we crossed over from Z3 to, sorry, from Z2 to X2 sometime late last year. We're continuing to kind of ramp up, you know, the first half of this year. We honestly believe based on customers' feedback, you know, that, you know, the best system out there in the market from emulation is Z3 and the next best one is Z2. I mean, and then just talking about the previous question that you asked, same thing. We offer the emulation as on-prem devices, but we also offer them in the cloud, you know, as well for our customers to avail themselves. Yeah, I think, I think, you know, this is, we feel very good about how we are situated and our customers' feedback just underscores the importance of these systems. Yeah. So just to really check some of the statement that management previously made, right? I think 2024 was the record hardware revenue year and 2025 will be another record year. Is that still the case? Yeah, we've had multiple record years in a row and yeah, that is correct. We expect 2025 to be another record year. Got it. Thank you. Now let's ask, let's discuss something more around the core EDA software. One of the key debates among investors, I would say since March, is what Lip-Bu becoming Intel CEO means for Cadence. I'm sure you have stories to tell as you worked under Lip-Bu's leadership at Cadence for many years, right? From the investment community, I'm hearing two sides of the arguments, I would say. On one hand, some folks think Intel is going to do a lot of cost cutting. They are doing that and not going to. And headcount reduction, right? This sounds like possibly a negative for overall EDA spending by Intel. On the other hand, Intel still runs legacy in-house EDA, has historically favored one EDA vendor, your competitor. Anirudh has multiple times said Intel is a low watermark in terms of your EDA market share, right? I mean, amongst all the global customers, which could mean upside for Cadence going forward. That was the bull case, right? Between the bull and the bear cases, which one do you think will prevail? In your opinion, why? Yeah, I mean, there's obviously we get asked this question a lot these days. And, you know, let's just say, you know, we are obviously very pleased with the announcement of Lip-Bu becoming the Intel CEO. I mean, you know, in a bigger picture view, we all know about, you know, his incredible track record in the industry. You know, he drove this remarkable turnaround with Cadence, you know, all these years ago. And we think there's a great deal of excitement about what he'll bring to Intel. Now, last week, you know, at Cadence Live, we were honored to have him, you know, for a fireside chat with Anirudh. During the chat, I mean, Lip-Bu, you know, stressed his priority, you know, to focus on innovation, to reformulate their strategy on the AI strategy particularly, and the importance of first-time pass silicon, increasing the productivity and the effectiveness of the engineering teams, right? You know, he also stated expressly his desire to move away from custom EDA, from custom IP, and embrace standard workflows, embrace standard IP. You know, I think this approach, this mindset really, I mean, should enable Intel to, you know, not just rationalize the internal investments they are making in an informed manner, but more importantly, it increasingly allows them to move to broad, you know, industry-tested best-in-class technology to help, you know, with their development, with their designs. Now, from a Cadence perspective, as you said, Anirudh has mentioned this before, you know, we have made progress, of course, at Intel on the foundry side, as well as on the product side. A lot more to do over there, you know. You know, Charles, I mean, our solutions are being used by the marquee companies at the most advanced nodes and other foundries. We think there is much more opportunity for us to help Intel, not just on the chip design side, but, you know, even beyond that on packaging. I mean, Intel's got a great packaging solution. We have the best, you know, 3D IC and Allegro solutions out there on system analysis. We look forward to the opportunity to help and work with Intel. As Lip-Bu said, you know, there's a lot more opportunity for us to do stuff together and to learn from each other. That's basically, you know, stay tuned. Yeah, stay tuned. We're definitely looking forward to more updates. Let's pivot to IP. This has been an area Cadence seems a little bit behind your closest competitor, Synopsys. In recent years, we've been hearing from Anirudh on earnings calls about this Star IP strategy. I think maybe it's a little bit not fully elaborated to the investment community. Maybe tell us what a Star IP means and why Cadence think this is the right strategy. Yeah, yeah. IP, again, huge opportunity with us. You know, you look at what the trends are, again, kind of like the previous question, more and more customers want to outsource IP, you know, outsource their, you know, standards-based interface IP, for example. There is really no good reason for them to continue doing it in-house. You know, more and more customers want to focus on their own unique innovation. These kind of things, okay, more outsourcing is happening. That is number one. The foundry ecosystem, you know, build-out is happening. That as well. I mean, you got now four big foundries, right? We are all focused on the most advanced nodes. They all need, you know, IP, which is optimized for their technology. That is happening. From a Cadence perspective, you know, we have kind of built out our portfolio over time. You know, about a few years ago, you know, our focus changed, if you will, or was more refined to move not just from growth by any means to scalable and profitable growth. What that meant was that, you know, we made the decision to say, okay, this is what we're going to really focus on. We think the biggest opportunities were with, you know, let's say AI HPC customers that were designing chips at the most advanced nodes. You had systems companies like hyperscalers building their own silicon who needed outsourced IP. I mean, it's one thing for the other companies and the semiconductor guys who have been building IP over the years and want to outsource it. Hyperscalers have nothing to outsource. They didn't have it in the first place. All this was very synergistic, you know, this whole thing on AI, HBM, most advanced nodes with our digital business, where we also focused on providing tools for the most advanced node designs for customers, the most bleeding edge, you know. That is what we mean when we talk about Star IP, high-value differentiated IP targeted for specific verticals, you know, and delivered at the most advanced nodes. Think about this as titles like PCIe, all the different, you know, versions of the DDR, high bandwidth memory, and also our Tensilica computing IP. What Cadence has done then, you know, over the past few years has methodically built out this portfolio. You know, customers have been giving us feedback and our IP is also getting better. It is getting, you know, giving better PPA, better quality. Based on customer needs and guidance through organic investments, and we've been investing more and more over the last three, four years, and also inorganic, meaning acquisitions. You know, we augmented our portfolio with high bandwidth memory IP from RAMBUS. We added, you know, internally chiplet connectivity IP like UCIe. And then security is becoming a key care about. We signed this agreement with Secure-IC earlier this year. You know, and then I mentioned the foundry ecosystem, you know, Samsung and TSMC, of course, but then Intel Foundry, Rapidus. We had this big deal. We talked about it in Q4 of last year. Most recently, you saw the announcement on the Artisan Foundational IP, right, as well. Standard cells, IO, memory compilers. These are all going to be optimized and validated at different process nodes at different foundries. You know, we are in good shape, IP group, you know, 40% at Q1. Yeah, we feel like we're in a really good place. We continue investing, continuing delivering and growing the portfolio. Yeah, we're excited about where we are on IP. Thanks, Nimish. Maybe let's move forward to the next topic. I want to ask you about China. We know that last year, probably not a good year for Cadence overall China business. You guys began the year thinking maybe China could be flat, I mean, 2024. And I think ended the 2024 at minus 16% growth for the overall China revenue. But still, kudos to the team for really maintaining and actually slightly raising the growth outlook through the year despite that China headwind. What's the outlook for this year? Mind if you reiterate some of the things that you guys recently talked with the investors about? How should we think about what could lead to the upside or the downside for your overall China business compared with the baseline outlook you just laid out at the last earnings call? Yeah, Charles. I mean, I think the key thing over here again is that, you know, we are driven by design activity, right? And design activity continues to be strong. I mean, that's what we said in the earnings call. We continue seeing that, you know, across, you know, not just China, I mean, broadly as well, but also in China. We continue seeing strong design activity. I mean, again, keep in mind, you know, that customers are designing products of today that are going to hit the market, you know, tomorrow, you know, two years, three years down the road. Dynamics are very similar. There's a lot more silicon being built, you know, domain-specific workloads. I mean, a lot of accelerators, many systems companies wanting to build their own silicon, hyperscalers and others. I mean, Alibaba, ByteDance, Tencent, right? All those dynamics are very similar, you know, as we see in other parts of the world as well. That is driving much more design activity. The other thing to point out is that, you know, we sometimes kind of, you know, given all the hype and the excitement around AI, kind of just focus on, you know, data center and AI and the like. Of course, that is there. That is a key part of it. It is not just that, right? It is also the physical AI, right? The emergence of physical AI. In there, you are seeing, you know, autonomous vehicles, robots, and drones. There is a lot of design activity, which is happening over there as well, right? Autonomous, especially on EV, tremendous activity happening in China on that. From a Cadence perspective, you know, we are pleased, you know, with the 19% year over year Q1 growth in China. And as we have said, I mean, we are, you know, we think it's prudent to be prudent. We'll see how it goes. We, you know, we are assuming at this point that China 2025 revenue stays flat year over year. We'll see, obviously, as we go get deeper in the year, we'll provide more updates. At this point, happy in Q1 and maintaining our assumption of being flat year over year for the year. Thank you, Nimish. I want to spend the last question on a little bit of a more kind of, maybe you can call it a blue sky kind of discussion, a little bit longer term for sure. Anirudh in the past has laid out how AI benefits Cadence, right? Roughly three main aspects. More companies designing more AI chips, that's number one. More customers adopting Cadence AI products, that's number two. And Cadence expanding into adjacent areas such as drug discovery, the area that can be disrupted by AI, right? That's number three. I think investors are largely thinking that the first one, more companies, especially system companies, hyperscalers designing more AI chips have not fully played out in a way that meaningfully re-accelerate Cadence growth. Imagine this, right? I mean, this is the thinking behind that. If Magnificent Mag Seven companies, Magnificent Seven companies become more like semiconductor companies each. From From chips and from the beginning to the end. Each one of them can, let's say, put a number there, contributes 5% of Cadence revenue. I know it's probably not there, probably not for most of them. Seven times five, that's 35%. That could be 35% of Cadence revenue. I'm sure you guys really not quite there. When will that happen if that happens at all? What is Cadence doing to make it happen? I think this is a top long-term question I think everybody have in their mind. Yeah, that's a great question. Big, big, big, like you said, blue sky, big picture question, right? I mean, first of all, you know, just reemphasizing, we got a very diversified customer base, no 10% customers, you know. And we've said this in the past as well about that the top 40 customers bring in roughly 55%-60% of our total revenue. Now, when you talk specifically about AI, I mean, we've said this, right? It's a generational megatrend. I mean, we are still, I think, in the very early stages of this secular cycle. It's going to be a multi-year, some say multi-decade super cycle. Of course, you know, as I said, you know, HPC data centers are what folks typically think about when speaking about AI. The demand for hyperscalers is ginormous, right? I mean, massive training requirements, the scale at which they are operating. Everyone's designing their own custom chips, AI chips, very complex chips, reticle limited, most advanced nodes. And they're requiring very sophisticated design tools, very sophisticated verification tools, you know. And that's what Cadence has been providing. And some of those things that we talked about, the AI-driven tools that we are providing, you know, be it for PPA optimization, be it for, you know, verification like Verisium or system analysis and the like. And so all, so that's in the context of, you know, the chip. Now, we've talked about the needs are being beyond just silicon. Chiplet architectures are becoming more prevalent. You know, means you need advanced packaging. And it's not just packaging by itself. You know, you're running into more and more thermal issues, electromagnetic issues, warpage issues, right? It requires a lot of in-design analysis. You know, so it's the workflow itself, the way that the engineers, customers are designing these things are different as well. You know, it's not just designing the chip and then, okay, ship it over to the, you know, the package or the systems guy. It's in design that you got to kind of go and do all this stuff. The type of designs are changing. The way you do the design development is changing. You know, so even, you know, when we talk about hyperscalers and the way they are doing the design, I mean, obviously they're one of our fastest growing verticals, you know, and but their flows are evolving. We see Hyperscalers going from doing ASIC flows, right, where you do the front of the micro architecture, the logic verification, then you ship it over to the ASIC vendor for the implementation. You know, some of the Hyperscalers and more and more of them are looking at, you know, doing it in-house, bringing it all in-house, doing a COT, a customer-owned tooling flow. Even that workflow is changing, you know. As I mentioned, the need is not just in data center and Hyperscalers. It's also physical AI, you know, autos, robots, drones. There are several applications across all these multiple verticals there. Anyway, the point being, there's a lot of demand, much more AI chips being built. It's a virtual cycle. The more the demand for AI silicon, the more the demand for our tools. It pulls in our tools and our AI-driven tools to deliver to those complex designs, which allows them to do even more complex designs, you know. That is basically this virtual cycle that we see. Beyond electronics, you know, as you mentioned, we also view life sciences as an area, which is, you know, pretty ripe for disruption. I mean, we have talked about this in the past, Charles, that, you know, 99% of design in chips is done, right, virtually, digitally, you know, about 20-25% in systems. In life sciences, only 1% is being done with in-silico methods. I think this is, we think, a huge opportunity five years, seven years down the road for us to kind of look at that. Anyway, that is the way we look at this. We think we got several exciting growth vectors for different phases of the AI cycle as it plays out. I think, you know, we'll keep reporting on how the progress we're making over there. Thank you, Nimish. I think, folks, we have probably four more minutes, and we can take some questions from the audience. Once again, if you have a question, feel free to type it in, and I'll read your questions. So far, there's no question in the queue, but maybe, Nimish, let me start a question. As a quick follow-up, you mentioned about you guys acquired the Artisan business from Arm, and it's a, they call it, I think they call it a physical IP. Can you tell us where does it fit in the Cadence portfolio? Quite frankly, Arm is also an IP company, right? They're number one in IP. You guys are number three in IP. Why Artisan, why Cadence is a better owner of Artisan than Arm? Why do you guys make that transaction? What's the rationale? Why it's good for both of you? Yeah, yeah. From our perspective, from Cadence, like I said, we've been building out a portfolio on IP. You know, we kind of layer on these different capabilities to make it a more full-featured portfolio. You know, we have the compute IP with Tensilica. You know, this interface-based IP, the Star IP that we talked about earlier. We added on high bandwidth memory, you know, which was being driven by AI. Then we got the security IP, Secure-IC, although the acquisition hasn't closed yet. When we looked at it from that perspective, foundation IP is something we have had our eye on in the past, but never felt that there was a compelling need to add it to the portfolio because we were focused more on, you know, layering on these other things which are much more mainstream, number one. Number two, we felt the market was adequately serviced by what was available out there at the time for the opportunity. Now, the opportunity has changed. Now you have the foundry ecosystem building. When you have these new foundries coming in, they are looking for their own, you know, the standard cells, the memory compilers, the IO buffers, and all these, what we call foundational elements to be optimized for the foundry. They need help with that. When the opportunity came along from Arm's perspective, and Artisan is a very well-known brand, you know, in the market, very prevalent out there. From an Arm's, you know, strategic perspective, you know, they were willing to divest that. We had these discussions. The opportunity came along, the need was there, and we had this, you know, the great opportunity to pick up some really well-established IP. You know, we signed a definitive agreement on that. Yeah, it's not closed yet, though. Yeah. Thank you. Let me double-check again, but my screen shows there are no further questions from the audience. I think we are almost at the end of the session. Maybe it's a good time to wrap up. Thank you, Nimish. Thank you, Richard, for joining us. Thanks everyone on the line. Please enjoy the conference. We'll talk soon. Thank you very much, Charles. Looking forward to doing it again next year. Thank you.