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Comment by hasbot | Hacker News Reader

The title is a bit misleading as the first sentence says "to help chip designers with tasks related to chip design, including answering general questions about chip design, summarizing bug documentation, and writing scripts for EDA tools."

Still pretty cool though.

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Isn’t that what chip design is?

The title suggested to me, and I see other commenters here, that the LLM was doing the chip design which isn't the case at all. So misleading title.

I remember seeing a tweet from an AI guy at Nvidia saying they were using AI for chip layout. Presumably not LLMs and I’m not going back on X to find the tweet, but just to say I think they are doing this (at least experimentally).

The entire field of chip-layout is considered an NP-complete problem.

Any computer program trying to solve NP-complete problems is in the realm of what I call "1980s AI". Traveling salesman, knapsack, automated reasoning, verification, binary decision diagrams, etc. etc.

Its "AI", but its not machine learning or LLMs or whatever kids these days do with Stable Diffusion.

There are plenty of researchers using machine learning for NP-complete problems. Are you saying that this work is fruitless or just that the current state of the art is still in “1980s AI” territory?

1980s style AI, such as Binary Decision Diagrams for automated reasoning, have continued to evolve over the last 40 years. The state of the art, today, remain BDD-based methodologies.

I guess in theory machine learning could take a swing at the problem. And sure, some professor out there is probably trying to mix the fields and find new solutions or something. But the bulk of the work, and problem-solving, is BDDs for a reason.

Or 3SAT-solvers, or... CSP solvers... etc. etc. Lots and lots of highly successful algorithms here. There's obviously open-questions for how to improve a CSP solver (faster, less RAM, more accurate estimations) and I've seen machine learning techniques applied before.

But the bulk of the methodology remains in whatever solver model you're going for. Even today.

I don’t know much about chip design, but am researching automated mechanical design and your description fits there too: most of the more successful approaches are based on old-school algorithms. I suspect machine learning can help speed these up but it isn’t clear yet how effective this will be.

That's called "Classical AI".

Or GOFAI?

Qualcomms newest desktop ARM chips with much better performance were using AI for the design as well.

It would be very surprising if a Large Language Model trained to speak English could adequately specify chip architecture...

why not? a netlist can be easily 'tokenized'. its already in parse-able format. you can just could just chop off the English input portion and it will consume. in fact I am sure you could write a 'read & speak' type program to read the RTL spec and feed it in but I'll suspect they'll a custom trained LLM on millions of generated RTL examples.

You'd need to spend a long time training it on something other than English... at which point it's not an LLM. I definitely agree that the LLM architecture is useful for a lot more than just language per se assuming you can appropriately tokenize your inputs.

Why? Chip architecture specifications are written in English!

Are you surprised that ChatGPT can write Python?

It's a bit like "assistant manager" vs "assistant to the manager".

Probably excludes qualitative tasks like architecture and apportioning resources.

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