And I think when you compare the annual value at the end of this year with the annual value of backlog at the end of last year, the thing to remember is the fact that there’ll be so much less hardware in it, I would expect, because we have the production capacity now to deliver on the hardware.
Jason Celino: Okay. Perfect. No, it’s super helpful. Thank you.
Operator: We go next now to Vivek Arya at Bank of America.
Vivek Arya: Thanks for taking my question. I appreciate it’s early for a ’24 outlook, but Anirudh, I was hoping that you could give us some color given that your model is 85% recurring. So just conceptually, what is the likelihood Cadence can maintain this kind of mid-teens growth rate? And what would make ’24 different or similar to ’23 from a growth perspective?
Anirudh Devgan: Yeah. Hi, Vivek. Like before, in Q3, we don’t comment on the next year. We are diligent. We want to make sure we finish out the year, see what Q4 looks like, and then we’ll be glad to share our assessment in our next — in the full year, in the February earnings call. And that’s what we have done in the past, and that has worked out well, right? So…
Vivek Arya: Okay. On the IP side, I think John, you mentioned that you’re expecting a strong quarter for IP in Q4. I was wondering how much would your two recent acquisitions contribute to that? And just longer term, do you think IP has a category over or undergrows the EDA? And does that influence your growth prospects? So both kind of near- and longer-term question on the IP business.
John Wall: Let me take the first part of that, and then I’ll hand it over to Anirudh for the second part. I think in relation to the IP business, like I say, we’re expecting a strong Q4 for that group. I mean, if you look at the guide we’ve given for the year, essentially, we’re guiding to 14% to 15% revenue growth for the year, which means Q4 over Q4 is going to grow kind of between 15% and 20%. Now, largely that’s due to the strength of our IP business in Q4. Do you want to talk about the longer term?
Anirudh Devgan: Yeah, Vivek, I mean, as you know already, more customers are outsourcing their IP need, and we have always participated in that, and we have always said we want to participate in that in kind of a star IP portfolio so that it’s more and more profitable. And the profitability of our IP business has improved over the last few years. And so, I think we are overall happy with the profitability of the IP business. So, now we’re trying to see, okay, what other areas can it grow and maintain profitability? And I think the areas that are emerging, which are strong are this whole chiplet-based design and 3D-IC, and which are used for a lot of AI and hyperscaler applications. And that’s also the reason we bought the PHY assets of Rambus, which is HBM and GDDR based IP.
So, I feel now that our IP portfolio is in the right areas. And also the use of this automotive and hyperscaler and AI IP — and most of these markets are evolving into chiplet-based and 3D-IC-based designs, which also has certain new IPs like UCIe and other things. So, as a result of that, we are investing more in our IP business as you saw, and then we expect a strong Q4, and then, we’ll see what happens in ’24.
John Wall: Just to clarify, the contribution from acquisitions is likely to be immaterial for this year. So, the strong Q4 that we’re expecting is really from organic business.
Vivek Arya: Thank you.
Operator: Thank you. We go next now to Ruben Roy at Stifel.
Ruben Roy: Thank you. Anirudh, I wanted to ask if you could maybe talk a little bit more in detail about the collaboration with Renesas and kind of incorporating Generative AI, LLM into chip design. I think you mentioned some expectations for quality improvement, efficiency improvement. I would think that longer term, you’d be thinking about productivity improvement as well. Are those milestones that you’re expecting to have answers about within the next year, two years? It sounds like this is sort of a longer-term collaboration and sort of testing going on today. Just wondering sort of what you’re thinking about timeframe in terms of incorporating some of these types of tools into chip design. Along with that, just the final part is, would you consider this a leading-edge design that Renesas is working on? Or if you could talk a little about the type of design, that’d be great. Thank you.
Anirudh Devgan: Yes, absolutely. So, I mean, we are very pleased with the collaboration with Renesas. And I think they have a whole initiative, if you follow them or if you look into the AI for their design process. And we are glad to be very close partner with Renesas as we are with other companies, right? So, we just wanted to highlight Renesas this time and the collaboration is broad based. I think they’re using almost all of our AI tools, whether it’s Cerebrus for digital or Verification, Verisium and other tools. And also, we are doing some new collaboration with them on LLM, like we mentioned. And the LLM collaboration is fairly broad-based. It can be applied to any kind of design, especially Renesas has a range of design all the way from advanced node to mainstream nodes.
And the other — you may know all this already, but the key thing, one benefit of AI is that there’s a, of course, the quality of results can be better, productivity can be better, but there are other benefits which are also true for large kind of global companies like Renesas. And the two that I would like to highlight, which came to the forefront with our partnership with Renesas. One is, all these large companies have geographically diverse teams, right? It’s not that the team is only in one location. Typically, they are in multiple locations. So, the good thing with AI is that, and we can do, in a lot of cases, design better than a human can do, but also it depends on the starting point, right? So, if you have a geographically diverse team, not all teams are super experts.
So, if the AI tool is same or better than your best team, then the reason to deploy it is that wherever — just by the nature of human productivity, there’s a variation across the organization, the results can be even greater in your teams which are historically not performing as well as you would like. And the other thing is also true in terms of experience. And this will happen, I believe, in AI in other industries as well, but it’s definitely happening in chip design. So if you have three years’ experience doing chip design versus 20 years’ experience in chip design, okay, with AI, that gap is narrowing. So, less experienced engineers can be almost as productive as more experienced engineers. So, apart from like productivity and quality of results benefit, it has this other kind of almost workforce management benefit for large organization like Renesas, because they have organization in multiple locations and also a wide experience range from young engineers to experienced engineers.
