Best Options for Using AI in Chip Design

I would love to see a future where the barrier of entry for purpose-built chips is 100x lower. That said there's an interesting observation in the interview:

> We essentially have rolled out an L1 through L5, where L5 is the Holy Grail with fully autonomous end-to-end workflows. L1 is where we are today, and maybe heading into L2. L3 involves orchestration and then planning and decision-making. When we get to L5, we’ll be asking questions like, ‘Are junior-level engineers really needed?’

We're seeing this in the software development world too, where it's becoming harder and harder for junior engineers to both learn programing and to be successful in their careers. If the only thing that's needed are senior engineers, how do people grow to become senior engineers? It's a harrowing prospect.

  > the barrier of entry for purpose-built chips is 100x lower.

A real issue here is lack of training data (at least for LLMs). There's lots of high quality (and plenty more poor quality) open source software that can be used to train on. There's significantly less open source hardware and often the stuff that does exist is mostly front end design. Good examples of complete test benches (ones you'd close verification on and go to a production tape out with) are few and far between and there's basically nothing for modern physical design and backend considerations (i.e. how you take your design and actually manufacture a chip with it).

Commercial companies who may be interested in AI tools for EDA do have these things of course but are any going through the expensive process of fine tuning LLMs with them?

Indeed perhaps it's important to include a high quality corpus in pre training? I doubt anyone wants to train an LLM from scratch for EDA.

Perhaps NVidia are doing experiments here? They've got the unique combination of access to a decent corpus, cheaper training costs and in house know how.