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L-R: Breker\u2019s Kelf; Silvaco\u2019s Rhines; Verific\u2019s Wong; Moores Lab AI\u2019s Henry; Silimate\u2019s Wu; ChipAgents\u2019 Cui. Photo Credit: Paul Cohen, SEMI ESDA<\/p>\n
SE: The hot new topic in EDA is agentic AI. Where will it help with semiconductor design and verification, and where do you see the potential problems.<\/p>\n
Cui: Agentic AI will reshape the entire design flow. It\u2019s already happening now. We\u2019re talking about a lot of use cases, from DV to RTL generation, UVM, formal, and moving forward, from frontend to backend. It\u2019s already helping a lot of engineers to speed up their design cycle. So it\u2019s happening and it\u2019s mature now. As for future challenges, we\u2019re dealing with thousands of users globally, and we see the real challenge is not only the technology itself.\u00a0 It\u2019s the organizational transformation \u2014 how to educate the engineer to get used to the new solution and to build teamwork. There are still a lot of things to do in that area. I see the entire industry will have to work together to help us get ready for that.<\/p>\n
Wu: The tagline for our company is the co-pilot for chip designers. Today, what we do is build AI models and tools and agent harnesses that chip designers use. It\u2019s enabling a much more directed and massive search of the design space. In previous generations of chip design, it was constrained because of the manual and compute constrained nature of it. Now you can generate a ton more experiments. You can do a lot more trial and error. One of the challenges we see, in addition to organizational, is how do you determine the quality of what you\u2019re experimenting with? And how do you do that really quickly and accurately?<\/p>\n
Henry: I\u2019m coming from a user perspective. We have all these amazing tools from Cadence, Synopsys, Siemens, and the rest, and the engineers still take two years to build a chip. Why does it take two years? What is actually going on in those two years? How can we use AI to compress that down to three months? There are a lot of things we can do. We can generate designs. We can generate verification collateral. We can do debug. At the end of the day, it comes down to, \u2018What\u2019s the proof?\u2019 You generate RTL code. How do you know that\u2019s correct? And you generate a spec, and you generate code for that. How do you correlate and make sure that what you generate is correct for everything, even when you\u2019re doing a testbench? How do you know that the testbench is correct? When you\u2019re closing coverage, how do you know that nothing is getting accidentally thrown into the exclusion list. There are intricate things we do as engineers on a day-to-day basis. How can we get that automated through AI so that eventually it becomes a compressed schedule? The whole idea is how to build chips faster using AI.<\/p>\n
SE: How much of that is on the design side, and how much of that is the ability to get on a fab shuttle, particularly at advanced nodes with test chips? Part of this is due to the fact that you can\u2019t get capacity because it\u2019s absorbed by all the big guys who are building chips.<\/p>\n
Henry: All these big companies are building chips on a massive scale \u2014 hyperscalers like Google, Microsoft, Meta, Intel, AMD. And the fabs are all set up to cater to these big companies. If you\u2019re a small company or a startup trying to build a chip, you\u2019d be lucky to get a shuttle on TSMC in the next six months. That\u2019s how the ecosystem is built. But I believe that AI technology will democratize the whole space, and there will be more people able to build chips cheaper and faster. That will bring up an ecosystem of fabs on the production side, as well.<\/p>\n
SE: Back to the original question of where AI will help and where will it create problems?<\/p>\n
Wong: On the plus side, it\u2019s going to be much more productive for everybody. On the minus side, it\u2019s too much trust in AIs. The AIs aren\u2019t ready for us to just hand off everything and let AI do its thing autonomously. That\u2019s the goal, but unfortunately, that\u2019s not the reality. At this point, a lot of the flow cannot be automatic. It needs to have fixed points where human interaction is part of the pipeline. I don\u2019t think there\u2019s much emphasis on that today, but it\u2019s something that\u2019s going to creep up on us, and it should be a part of every methodology.<\/p>\n
Rhines: You\u2019re talking about accelerating the design part. There\u2019s another part, which is the process, and that has been overlooked in this whole thing. The complexity of processes, and the amount of time it takes to run a prototype wafer, have made the traditional methods of physical prototyping impractical. We have to be able to build surrogate models for unit processes \u2014 the ability to do process integration on a computer instead of in a laboratory. That\u2019s an enormous opportunity, because people who pay $25 billion for a wafer fab really place a lot of value on a one-month earlier ramp-up. They have a lot of money, and they spend money, unlike the EDA budgets that are controlled by R&D budgets of our customers. So it\u2019s an enormous opportunity if you have 40 years of simulation data that you can put together in those surrogate models.<\/p>\n
SE: This is really divide-and-conquer of a much more complicated design, right? There are a lot more elements here, particularly with agentic AI. How do you put all that together?<\/p>\n
Rhines: A lot of us in the EDA industry are going to end up selling or renting agents, or providing our databases to customers who don\u2019t want to reveal their proprietary information. We can give them an agent to work with their proprietary information to calibrate our synthetic data and make it fit with predictive norms for a process or a process capability.<\/p>\n
Kelf: Over the last 30 years, we\u2019ve sat on stages like this and said, \u2018Chip design is moving along so quickly. How do we keep up? Where do we get the resources? The world\u2019s falling in. What are we going to do about it? What\u2019s the next stage?\u2019 And all of a sudden, we have it. There\u2019s no question that AI is going to make a huge, dramatic difference to our business. The question is, \u2018How do we get there?\u2019 So on one hand, a number of us are building AI into our tools. We\u2019re improving efficiency and making these incremental changes. On the other hand, it seems there\u2019s an opportunity to just throw away everything we\u2019re doing and make a completely new flow. That\u2019s been done before in EDA, and it\u2019s usually horribly unsuccessful for a bunch of good reasons. Nevertheless, maybe this time something like that could be done. The problem is that chip design is not a business like recommending you watch the next 10 videos, where if it misses one it\u2019s not really a big deal. This is an exact science. You can\u2019t have a 1% error when you\u2019re building a chip. It\u2019s got to be right on. So the problem we\u2019ve got to solve is how to get around that. How do we make sure these AI devices are exacting?<\/p>\n
SE: How do you measure success with agentic AI?<\/p>\n
Rhines: These are quality-oriented much more than they are quantity-oriented. Certainly, we\u2019ll do designs in much less time. We will catch a lot more bugs than we would normally catch, and we will remove an enormous amount of drudgery of documentation, and the initial RTL generation, and the creation of the testbench and running testbenches. Those are all things that will relieve people to pursue the more creative aspects. But the quality that you generate in the ultimate design \u2014 or at least the quality in the intermediate steps \u2014 is what differentiates agentic AI from doing it the old way.<\/p>\n
SE: Is the measurement of success at time zero, or is it over time?<\/p>\n
Cui: The metrics will be an evolving system. In the past, AI could help one engineer to speed up. But with agentic AI, it can help the entire engineering organization speed up with trust. I call this a trusted acceleration. But moving forward, talking about T-0 and over time, this will eventually become a self-evolving engineering system. We really can build a system that can learn from all the previous experience, keep collecting feedback, and keep evolving from there. That\u2019s the future AI solution for the industry.<\/p>\n
Wu: It is going to be evolving, especially as the capabilities of systems that we\u2019re designing evolve. There are outcomes and metrics that need to be anchored, and ultimately it\u2019s outcome-based. From the software industry perspective, which is a leading indicator for us, and then even for our industry, there was this period where it was like, \u2018How do we use AI? How do we ramp up AI usage to 100%?\u2019 And now it\u2019s, \u2018The token budgets are out the window. Where\u2019s the ROI? Where\u2019s the efficiency?\u2019 In my opinion, it\u2019s not so much about evolving or specific metrics, but how can we do things that were previously not possible before agentic AI. That\u2019s the litmus test for me and for the customers we work with. You can have speed-ups of 2X to 5X. You can automate certain processes that theoretically you can throw headcount at. We can talk about the talent gap as an issue, but the real issue is how do you compress timelines from 12 to 18 months down to 6 or 9 months. The answer to that is going to be new, hyper-efficient processes, and that\u2019s going to come from the entire stack. Given this massive space that agents are set up to explore and propose ideas, how do you ascertain quality in a very short amount of time such that you can do this exploration, validation, and convergence in a fraction of the time.<\/p>\n
Henry: This reminds me of a story a long time back. There was this math genius. He was on stage, and people in the audience were giving him three-digit numbers to multiply, like 342 times 287. The guy was giving the answer, and they were looking at the calculator and saying, \u2018Oh, that\u2019s correct.\u2019 It was going well. And then there was this one guy in the audience who was drunk, and he got up and said, \u2018Hey, I can do the same thing.\u2019 Then someone from the audience gave him two numbers, 137 times 784. He immediately answered 7,432. They said, \u2018That\u2019s wrong.\u2019 He said, \u2018Hey, but I was fast.\u2019 You can get a lot of things very fast, but how do you trust that? Can we build a chip out of it or not? We have the same problem today. We have different kinds of engineers. Some of them are really good, some not so great. How do we trust the output from these different engineers? That is still a problem. The way we try to solve that is we have different kinds of checkpoints and proof points and checklists. That is very ad hoc. It depends on the company you\u2019re working at and the methodology they\u2019re using. It would help if there is a consensus in the community about these qualities \u2014 like some benchmarks or proof points and things like that, which all these agents can strive toward \u2014 so that we have a consistent way of saying, \u2018Okay, what is coming out is correct or benchmarked to a quality level.\u2019<\/p>\n
Kelf: I was going to mention benchmarks. We\u2019re cooperating on this, trying to figure out a bigger flow and how we can work on this. One of the first things we came up with is, \u2018Okay, if we\u2019re going to cooperate, what kind of benchmark can we put in place to see how we\u2019re actually going to improve things by working together. This is going to be a pie-growing exercise, where if we cooperate and work together to build in these different pieces, we can really make a big difference for the industry. If we all compete with each other, then it probably will all fall apart. Putting in place some metrics, some understanding of flows and what we achieve, and then seeing where we all fit into that, is a really critical part of it. If we can add to that and grow upon that, we stand a much bigger chance of making this really successful.<\/p>\n
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