Short answer: Hardik Kabaria says a thermal engineer might run 10 to 20 analyses a day and with Vinci can run thousands. Making the same capability available to designers cuts iteration cycles that now take weeks.
Watch this answer from Episode 50 (starts at 9:08) · Episode 50 notes and transcript
From Episode 50: Physics Simulation for Chip Design Used to Take Days. This AI Does It in Seconds., starting at 9:08.
Jordan Metzner [9:07]: Okay. And tell us a little bit kind of how where it works in the workflow. So, you're developing a chipset. Obviously, well before it's it's gonna get to fab, you're, trying to build, I guess, like a digital twin of this chipset, right, inside your software so then you can then run these simulations. Right? I guess that's like the step forward, and then you run these simulations and have kind of a concurrent loop and then kind of go back and reiterate, etcetera.
Hardik Kabaria [9:30]: So the existing workflow pretty much goes like you described. There's somebody who designs the part, then somebody who performs physical performance evaluation, then you check for manufacturability, and then you sort of circulate. Right? And quite often, these are not only not the same people, they might not sit in the same team. In some cases, they might not even work for the same company. So the innovation cycle is never in days, it's often in weeks. Right? But you care about the performance across the board. So even as the designer who is making changes, they want to know if the changes would have effect on the physical performance or manufacture. So we are making this available across the loop. So let's make it more concrete. Of course, today, there is a loop, there is a thermal engineer who works for a semiconductor company. Now instead that engineer, she might be performing 10 to 20 analysis per day, exploring different configurations that came to her from design. With us, she's able to do thousands. So that is your productivity and efficiency gain.
Hardik Kabaria [10:35]: That's the story of AI. You make your engineer a super duper engineer. But there is a second thing that happens is now you can take that capability and make it available to the designer. And you see that playing out in front of us with large language models driven product for software engineer. Your UX engineer can make some changes. Everybody can build some level of software engineering features. So why can't we make that physics intelligence available to the design engineer? Now the productivity and efficiency again is not going to be from seconds to hours, it's going to be weeks. Concretely, your power engineer can make changes in the power capability of the chipset and start assessing, is this going to be air cooled? If it if this CPU cannot be air cooled, I have to think about other reasons, other ways to achieving this power cycle. Otherwise, the downstream customer who are gonna take my CPU and gonna put it in the board, they aren't gonna be happy because they don't want to deploy, say, robotics, a liquid cool system. So physics is once if you make it available as a digital physics infrastructure across the life cycle, the thing that I didn't foresee when we started the company is that you will make this intelligence accessible to broader set of roles and personas who all work together to deploy a product.
Narrator [11:57]: Planning a team off-site sounds fun until you actually have to plan it. Flights, hotels, schedules, coordination, Offsiteio handles all of it.
Jordan Metzner [12:07]: Yeah. So the analogy here is just how cloud design has helped, non designing engineers build front ends. This is helping non testing engineers improve the simulation and testing process of the develop.
Hardik Kabaria [12:21]: Yes. And then that means that insight is a lot more usable in action right away. Right? And one is like, analysis came by, oh, the hotspot is higher than I thought. Then you have to think, a different person thinks through what decisions or changes I want to make. Instead, if you make that analysis available to the person making the design, then the iterations cycle is much faster. Not to say the other roles are not important. You're just making the capability available to different level of people in the system.
