April 29, 2025
By Bob O'Donnell
As powerful as AI may be, many industries are still struggling to find clear-cut uses that would make a measurable, demonstrable difference. Thankfully, when it comes to chip design software, however, that is definitely not the case. In fact, since their early introduction a few years ago, AI-powered features have become a mainstay of EDA (Electronic Design Automation) tools, like those from companies such as Cadence and Synopsys.
Silicon designers quickly discovered that many of the complex yet potentially tedious tasks they need to engage in as part of their overall process—e.g., the grunt work portion—could be automated or dramatically simplified via intelligent AI algorithms. From automated layout of certain IP blocks to enhanced efficiencies in IP block interconnects, these AI features could speed up the less creative (but still important) parts of their efforts and let them concentrate on the more interesting parts of the design process.
In addition, AI-powered tools can drive impressive improvements in chip performance and energy efficiency. In fact, vendors like Cadence have indicated up to 60% performance improvements on specific blocks within a chip because of AI enhancements. Power improvements of up to 38% have also been made possible thanks to these tools. Along the way, silicon engineers also discovered that AI-powered features could reduce the amount of time necessary to finish a chip design—in some cases, up to 10x faster. In short, these AI-powered EDA programs provide the kind of ideal AI-enhanced scenario of increased productivity and more engaging work that many organizations are looking for.
Not surprisingly, this has also led to significant growth in the usage of these AI-powered capabilities in modern chip design tools. In fact, based on public data of the number of chip design tapeouts that major companies like Cadence and Synopsys have disclosed, as well as their estimates on the usage of these AI features, the industry is now crossing a critical barrier. Specifically, just over 50% of advanced silicon designs (those made with 28nm process technologies and smaller) are now believed to be AI-assisted. Looking forward, it’s easy to speculate that those numbers will grow to a significant percentage of total chip designs over the next few years.
Given that there were zero AI-assisted tapeouts just four years ago, that’s impressive progress. More importantly, it's a great example of how applied applications of AI technology can have a profound impact on a business’ evolution. The fact that it happens to be in the chip industry (and, appropriately, likely involves a significant percentage of chips that are designed to accelerate AI computing!) makes the moment even more relevant and consequential.
According to Cadence, these AI features can reduce chip design times by as much as a month, which is a significant positive impact. Plus, as mentioned earlier, it’s a benefit that can be directly tied to the AI features—about as concrete an example of the technology’s benefits as you could ever want. The power and performance improvements alone make the enhancements enabled by AI incredibly valuable. However, toss in the increased efficiency of the work that silicon engineers can achieve with these tools and the story gets that much stronger. In fact, it’s easy to see why so many people in the world of semiconductor design—including industry leaders like Nvidia, AMD, Qualcomm, Mediatek, Samsung Semiconductor, Marvell, Broadcom—are so excited about the possibilities for AI in their product creation tools (as well as for the AI accelerators they’re going to be designing with those tools!).
The timing of the crossover point also ties in very nicely with a number of other semiconductor industry developments. Most notably, the past few years have seen a big increase in the kind and number of companies who are working on advanced chip designs. From major cloud computing providers such as Google, Microsoft, and Amazon’s AWS division to device makers like Apple, Samsung, and more, there are many organizations pursuing the custom silicon route as a critical means of differentiation. However, the number of skilled chip designers in the world is still relatively limited, so having more advanced AI-powered tools that can enable even junior designers or others with limited experience to take on more sophisticated chip layout tasks is critically important to keep the semiconductor industry advancing forward.
Even for the long-time semiconductor players mentioned earlier, these enhancements create important new possibilities, including the ability to create more designs, build more customized options, and run more projects in parallel. The ability to create more customized designs, in particular, is something that many in the chip industry (and their chip-buying clients) have wanted for a very long time. Unfortunately, the practical realities of doing so with traditional design tools has kept that from becoming possible. Now, however, all of these capabilities and more can translate into major advances and important opportunities to build on the rapid growth the semiconductor industry has seen over the last few years.
Another important point is that as semiconductor designs move into smaller and smaller process nodes and the number of transistors per chip continues to expand, AI-powered chip design features are quickly evolving from a nicety to a necessity. The number of factors, permutations, and connections that chip designers face is quickly growing, and the work to create these sophisticated new chips demands the enhanced intelligence that a well-designed AI-powered tool can enable.
While it’s true that the speed of AI adoption and the extent of its influence hasn’t been as fast or as profound as many first expected in certain industries, it’s also becoming very clear that in targeted applications, it’s proving to be even more impactful than many hoped. With the transition to AI-enhanced chip designs crossing over this important 50% barrier, it’s apparent that EDA tools from companies like Cadence and Synopsys are unquestioned beneficiaries of these advances. From a semiconductor industry perspective, it’s also clear we’re entering an exciting new AI era.
Here's a link to the original column: https://www.linkedin.com/pulse/chip-design-hits-ai-crossover-point-bob-o-donnell-zedzc/
Bob O’Donnell is the president and chief analyst of TECHnalysis Research, LLC a market research firm that provides strategic consulting and market research services to the technology industry and professional financial community. You can follow him on LinkedIn at Bob O’Donnell or on Twitter @bobodtech.
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