Short answer: Hardik Kabaria says Vinci trained a half-billion-parameter physics model from the ground up, at GPT-2 level capability, for thermal and thermo-mechanical problems. Next it will add phenomena like electromagnetics and RF and make inference faster.
Watch this answer from Episode 50 (starts at 18:20) · 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 18:20.
Jordan Metzner [18:19]: Right? So okay. Just, maybe one last question. I get where we are right now. It seems like we're really early in this life cycle because even implementing your software seems like some efficiencies are gained, but it still feels like the early days of the potential of this as the models get better, as the chips actually that you run your software on get better, etcetera, and then the downstream impact of that. So just paint us a little bit of vision of where's the future, where is this going, where are people gonna see, chip development in the next few years?
Hardik Kabaria [18:50]: Yeah. I mean, so just I would say we start with something basic. It's like language as a modality is well understood. A model can understand it, provide intelligence with creates a useful information and downstream actions for us. We are going after physics as the modality, something as basic as, okay, I want to predict temperature, I want to predict the heat flux, heat dissipation, the mechanical stress. But we built a ground up model to do that for a couple of physical phenomena, thermal, thermo mechanical, and the model is the same level of capability as GPT-2 was. So it's a half a billion parameter model, We have trained it ground up. So we are the pre training team and we are the product team. Where we go from here is to cover wider range of physical phenomena. So of course we are now focusing on scaling the model, adding more phenomenas in it which is what our industry partners are pulling for.
Hardik Kabaria [19:50]: The ultimate goal is make physics intelligence available to anybody who has a question at compute capacity or what we would call it token maxing. Basically you should see your GPU is completely utilized and somebody should be asking a question and they should be getting accurate deterministic answer. We are seeing that in some of our deployment. They have allocated some compute and it's 100% utilization. So there is enough in blonde of queries that's going to happen, right? And that means for us we have to do both the jobs, we want to make the inference faster, so on the given compute allocation we can solve more and more queries And we also have to do the job of expanding the model capacity so you can not only get answers for thermal questions, but electromagnetics questions, RF questions, and so on. So those are the two vectors that we are focused on, but quite a bit in sync with our industry partners. Like, they are the ones guiding our roadmap. Like, hey, you solve thermal problems, can you solve this? Then you do system level cooling problems and so on because at the end of the day, they don't care about just one perfomp.
Hardik Kabaria [20:54]: They care about all of it. Why? They wanna ship a better product faster. They don't wanna ship just thermally better product. They wanna ship a holistically better product. So they care about multiple phenomenas that they want to cover in the design process.
Jordan Metzner [21:07]: Yeah. So it comes down to then, even though you know that certain behavior might cause an increase in thermal, it might be a trade off for something else. And so inevitably, you're faced with an inevitable list of trade offs, right, all the time.
Hardik Kabaria [21:19]: Absolutely. And universe is multiphysics. Every part we create is gonna, enact in a multiphysical world. So, yes, engineers care about it. These disciplines are complex, so there's no workflow today that ties everything. But, ultimately, that is the need.
Jordan Metzner [21:35]: Yeah. That's awesome. Okay. Really great demo. Awesome. Thank you so much. Alright, Sam. Shall we jump into the news?
