AI continues to be an investment theme in all investor’s minds. NVDA’s recent results only added fuel to the fire. We’re continually analyzing the AI stack to find other companies that will benefit. We’ve identified companies such as Nvidia, AMD, Microsoft, Taiwan Semiconductor, which we’ve written about on several occasions, plus ASML and Super Micro, more recently. Note: We also covered Marvell on our forum, which you can read here.which you can read here.
Summary of Cadence: AI-Based EDA
Cadence Design Systems provides semiconductor chip design and printed circuit board design tools. In the era of AI, the company has expanded to AI-Based EDA, which stands for AI-Based Electronic Design Automation.
Cadence’s tools eliminate defects in semiconductors early-on during the verification process, which is quite valuable, as catching a bug too late can be exponentially expensive and problematic for design companies.
In addition to improving product quality, there is also a large opportunity for Cadence in reducing the time required to design chips, especially as design companies move quickly toward smaller node sizes. In the past, semiconductor companies, pursued time-consuming methods such as transistor-level and cell-based designs. More recently, Design Reuse has been a popular design method to increase productivity, which is taking an IP block and dropping this into the new chip.
Cadence’s AI-Based EDA will greatly increase productivity by leveraging AI to augment human engineering. This is especially important as the nodes are shrinking, which increases the costs for the physical design process (dark blue in the chart below) and verification (dark green in the chart below).
Source: Cadence Design Systems, CadenceLIVE
Management made these general comments on the demand environment:
“Exploding chip and system design complexity will drive a significant non-linear growth in the workload requirements, opening up a massive opportunity for computational software to help realize these innovative products by investing more of the R&D spend in automation.”
Cadence offers a wide range of AI-based tools for digital implementation, regression and verification testing, or cell library characterization. However, more importantly, Cadence is moving into AI platforms which wrap the AI-based tools into frameworks. This expands the ways Cadence’s tools can be used.
Stock attributes that we like
- Exposed to mission critical capex – Chips are shrinking, which adds complexity to the design. Before actually spending millions on a chip or system, hyperscalers and design companies need to ensure that the chips and systems will work. Computational Software is an integral part of that process to design and verify that the chips will operate as intended. Clients include not only the semiconductor heavyweights such as Nvidia, AMD, Samsung and Broadcom but across other industries, such as Electric Vehicle systems.
- High switching costs for customers – After a client designs a successful chip using Cadence software. It’s less likely the client will switch to another software provider to design the next generation. This increases the likelihood that the client will renew the software licenses
- Subscription model – 85% of revenue is recurring with a visible backlog which contributes to consistent FCF generation and steady operating margins
AI Platforms for EDA:
Cerebrus: Digital Design and Optimization – 28% of Revenue
Uses training data from reinforcement machine for full flow optimization. What reinforcement machine learning does well is trying new options and learning what works, which lowers the human effort in the design process. This platform can be used for both at the system level and for silicon optimization. If you think of a car, it has a large system that combines many components and it has chips and circuit boards. In this case, Cadence can be used for the system or the chips.
“Among others, [Cerebrus] is now deployed at 10 of the top 20 semiconductor companies, including 7 of the top 10 semis and at several major hyperscalers. In Q4, we successfully delivered an advanced HPC design and a CPU design using our digital full flow and Cadence Cerebrus on TSMC N5 process technology, delivering 8% reduced power and a 9% area improvement while significantly improving engineering productivity. In 2022, several market-shaping customers, including Intel, NVIDIA, Broadcom, Samsung and Renesas shared their remarkable successes with Cadence Cerebrus at our CadenceLIVE user conferences.”
Optimality: In-Design Optimization
The optimal electro-mechanical system needs to run many cycles of simulations until the system reaches its performance goals. This platform provides the parameters to reduce the number of simulations required. In a case study the company provided, Optimality reduces the number of simulations from 3125 to 79 simulations. This reduces the number of required engineering hours from 66 hours to 1.6 hours. In a second case study, it reduces the number of simulations from 625 to 58 simulations, with a reduction of hours from 110 hours to 11.3 hours.
Verisium: AI-Driven Verification and Debugging – 26% of Revenue
Combines the AI-enabled tools for simulation, emulation and prototyping and uses AI techniques to find the failures. It can be very time consuming to figure out the cause of a failure. By using AI, the engineer can go quickly to the root cause of the failure and to the bug fix.
Per management, the verification business grew 28% year-over-year, and is used for mobile, hyperscale, high performance computing and electric vehicles. It was also stated that Verisium delivered up to 30X improvement in efficient root cause analysis.
Allegro X AI: AI-Driven PCB – 12% of Revenue
Allegro X helps to optimize circuit boards and systems using generative AI. It particularly helps with automating the placement and routing of components on a board.
Virtuoso: AI-Driven Custom Layout – 22% of Revenue
Virtuoso is a platform that helps move existing chips design and intellectual property over to a new generation through AI trained algorithms. One reason that Cadence is defensible against large language models is that these platforms require large data sets, whereas Cadence’s platforms can be trained on a much smaller yet highly specialized data set. This is ideal for chip designs, which are proprietary and are not shared. Cadence is ideal as the platforms can train using the smaller data sets that a company owns (and does not share).
Regarding the two generative AI platforms, Allegro and Virtuoso, the following was stated on the earnings call:
“Generative AI design tools are revolutionizing chip and system development by delivering unprecedented optimization and productivity benefits. Customers have already been benefiting from our ground-breaking generative AI solutions in the digital, verification and systems areas, and with the recent introductions of Virtuoso Studio and Allegro X AI, we now have an unmatched chip to package to board to systems generative AI portfolio.” Several customers including MediaTek, Renesas, Analog Devices and TSMC provided testimonials for the launch.
IP – 12% of Revenue
IP offerings consist of pre-verified, customizable functional blocks, which customers integrate into their ICs to accelerate the development process and to reduce the risk of errors in the design process.
JedAI: Joint Enterprise Data and AI Platform:
In September, the company released a platform to fuel the AI platforms called JedAI. This horizontal platform provides a knowledge repository between projects and serves as the big data analytics layer. There are many tools and projects required for hardware designs (listed above), and Jed AI provides the fuel (or a knowledge base) to improve efficiency for future designs from past projects. This is done by leveraging data from previous design projects, workload data (runtime, memory usage) and workflow data.
Below is an engineer’s view using Cadence’s chip design software.
Helping to improve Nvidia’s GPU Performance
For example, by integrating Computation Fluid Dynamics (CFD) with System Design Automation, Nvidia was able to improve the speed and power of efficiency of its GPU chips. Computation Fluid Dynamics (CFD) is an aspect of multi-physics system analysis that stimulates the behavior of fluid and their thermodynamic properties using numerical models.
Per the management team:
“Recently with our collaboration with NVIDIA, Jensen talked about that Cadence CFD on GPU for the same cost is giving a 9x improvement in speed up and 17x improvement in power efficiency. And GPUs are slightly more expensive than CPUs. I mean, typically, I would guess, at least 3x to 5x. So, you're getting 30x to 50x speed up on GPUs that normalized for cost is still getting 10x or 9x improvement in speed. So that's a huge improvement based on our special algorithms, because we have a long history of massive parallelism in CPUs and now we are applying it to GPUs, especially in SDA, both for electromagnetic and CFD. So, I think that can also provide a lot of growth. I talked about AI and all for chip and system, but this acceleration on GPUs, accelerated compute for system analysis is another big vector.”
Evolving from EDA to SDA
Currently, there is a shift in electronic design systems from EDA (Electronic Design Automation) to SDA (System Design Automation). No longer can components be designed in isolation and integrated by systems engineers. As a result, electronic and mechanical design are increasingly becoming intermingled which requires the co-design and co-optimization of every component in an electronics system and every aspect of the physical nature of the systems.
Traditional EDA can already solve system simulations measured in billions of transistors. In the future, SDA will be able to process simulations involving 1 trillion transistors. Per the management team: “[…] some of these chips have 100 billion transistors, right, on 1 inch by 1 inch. And if you look at by 2030, they will have 1 trillion transistors, okay? So just in terms of size, it will be 10x more. And then the chips are more complicated and then you add software on top of it. So the design complexity that our customers need to do will go up by at least 20, 30x in the next 5 to 7 years. So the only way to meet that is by more (SDA) automation. That’s the history of our industry, And the best way to do more automation right now is using AI.”
Business model + Historical Growth (2016-2022)
Between 2016 and 2022, on the back of these positive secular tailwinds Cadence delivered a revenue CAGR of 11.9% and non-GAAP EPS of 23.4%. Despite selling products into an industry that can be cyclical, Cadence has been insulated from the profitability ups and downs of its semiconductor customers.
Because of its subscription model, 85% of Cadence’s revenue is recurring. Typically, Cadence enters into time-based license arrangements which grants customers the right to access and use all of the licensed products at the outset of an arrangement and updates are generally made available throughout the entire term of the arrangement, which is generally two to three years. Cadence’s updates provide continued access to evolving technology as customers’ designs migrate to more advanced nodes and as its customers’ technological requirements evolve. Payments are generally received in equal or near equal installments over the term of the agreement. Clients have the option to renew the license at the end of the agreement.
The 2022 revenue breakdown and 22/21 YoY growth rates for the 5 software segments:
The remaining 15% of revenue is upfront revenue mainly from the sale of Emulation and FPGA Prototyping hardware equipment. Typically, it’s the same clients who use the software and then use the hardware for testing and verification purposes.
China contributes 17% of revenue.
Profitability:
Similar to its Revenue and Non-GAAP eps, Cadence’s non-GAAP operating margins have steadily increased from about 26% in 2016 to ending at 36% in Q422.
Additionally, FCF generation increased coupled with share buybacks and healthy balance sheet.
Q123 Financials:
For q1fy23, Cadence reported revenue of $1.02b (+13.3% y/y) which matched the upper end of their guidance of between $1b-$1.02. Non-GAAP eps was $1.29 which beat their guidance of $1.27 and consensus of $1.25.
For q223, Cadence guided revenue $960 million to $980 million, non-GAAP EPS $1.15 to $1.19 and GAAP EPS $0.73 to $0.77.
For q1fy23, GAAP operating margin was 31.6%, right in the middle of company guidance, vs 35.4% last year. While non-GAAP operating margin 42.1%, lower than 44% in the prior year, but exceeded the upper end of management’s 42% guidance.
For q223, Cadence guided non-GAAP operating margin 40% to 41% and GAAP operating margin 29% to 30%.
Looking at the balance sheet, Cadence finished q1 with $917 million in cash and debt of $678m. Operating cash flow was $267 million. And repurchased $125 million worth of Cadence shares.
The Q123 backlog stood at $5.4b vs $5.8b Q422
2023 guidance
Cadence updated their 2023 full guidance. The revenue range was increased from $4.03b to $4.07b vs $4.0b to $4.06b. GAAP eps in the range of $3.26 to $3.34 versus $3.24 to $3.34 and non-GAAP eps in the range of $4.96 to $5.04 versus $4.90 to $5.00
GAAP operating margin in the range of 30% to 31% vs 30.5% to 32% and non-GAAP operating margin in the range of 41% to 42% vs 40.5% to 42%.
Operating cash flow in the range of $1.3b to $1.4b. Expects to use approximately 50% of free cash flow to repurchase Cadence shares.
Cadence’s full year 2023 guidance represents of +14% sales growth and 17%, non-GAAP EPS YoY. Full year guidance was very modestly raised by $20, spread evenly between hardware and software. Despite the better-than-expected hardware sales in Q1, Cadence is taking a wait and see approach and didn’t want to further raise the FY guidance at this time.
Initially, the stock reacted negatively to the Q1 earnings report likely on 1) disappointment that 2023 guidance was not raised higher, 2) sequential q4/q1 backlog decline and 3) q1/q2 decline in sales guidance. It has since recovered with the Nvidia’s blowout earnings likely driving it higher. See below for questions from analysts on these points.
Earnings Call:
Analysts were concerned about the 5% sequential decline in revenue provided in the guidance:
Question
Oh, sorry, yes. And then, my second question on the guide, it looks like second quarter is going to decline sequentially by about 5%, consistent with what we saw last year too. So maybe just a quick refresher on what's driving the seasonality here?
John Wall
Yes, when you look at it, it will be — it'll show up in the functional verification number next quarter. I think the — when I look at — like Q1 was great. I mean, functional verification was up 30% year-over-year. The quarter was up low teens. When I look at Q2, Q2 also looks great. It's going to be up low teens again compared to Q1 '22, but functional verification will be up probably closer to 20% rather than 30%.
But — so, it's our expectation that in our guide, we're assuming that the recurring revenue mix for the year stays at 85-15, 85% recurring, 15% upfront, same as what we experienced last year. Now, in Q1, it was 80%, 20%, so there was a lot of open revenue for the hardware deliveries that went out in Q1. In Q2 — we expect Q2, Q3, Q4 to be less than the 80-20, and it will average 85-15 for the year. So, you're seeing that in the Q2 guide”
Regarding Q4/Q1 decline in backlog from $5.8 to $5.4b. Bookings and renewal environment continue to be solid. H1 tend to be weaker. Q123 was also impacted by timing and big renewals in H1 2022.
“The second half of this year is very heavily weighted for bookings for the total year. We're very light in the first half of the year for software renewals. The second half of the year, Q3 and Q4, are very strong for software renewals this year. Unlike last year, last year, I think the first half was we had a number of big software renewals in the first half, we don't have that this year. And when you look at the second half, of course, I mean any big renewal you have in Q3 — at the end of last year, we had like nine months in RPO for that and now it's only six months at the end of this quarter. So, it's really just a function of renewal timing and the fact that the first half of the year is light for renewal timing [..] What I was trying to convey was that we had very few software renewals that came up for renewal in Q1 and therefore bookings were expected to be light. The beauty of the recurring revenue model that we have is that the timing of those renewals is not especially important, but it's the annual value of those renewals. And that we continue to see growth in the annual value of those bookings, and we see growth across all of the businesses.”
Key Risks
Currently, the biggest risk is Cadence’s 17% exposure to China where they sell software and hardware such as Emulation and FGPA Prototyping hardware equipment. Hardware sales to China were strong in Q123. Total hardware sales have ranged from 15-20% of sales and are recognized upfront.
Similar to other tech sectors, if the US gov’t restricts hardware citing competitive reasons, this will adversely Cadence.
However, at the moment there is nothing to indicate that this will happen. But any headline suggesting that will impact the stock price.
Conclusion:
Taking into consideration the recent AI rally that lifted Cadence stock price, coupled with the sequential decline in Q2 and premium valuation, we are sensitive to entry price levels.
We expect it to be a medium and long-term winner. Cadence occupies an important piece of the AI stack and is at the intersection of secular megatrends such as 5G, hyperscale computing and AI/ML that are driving sustained investments by its semiconductor and systems customers. Despite the macroeconomic uncertainty, Cadence’s clients continue making significant investment in their next-generation products, resulting in robust design activity.
Ultimately, we will want to see revenue accelerate beyond the mid-teens. It’s a matter of deciding if the story is strong enough to buy in advance of an acceleration or wait for higher revenue to be reported and forego an earnings pop.