YC is wrong about LLMs for chip design

LLMs have a long way to go in the world of EDA.

A few months ago I saw a post on LinkedIn where someone fed the leading LLMs a counter-intuitively drawn circuit with 3 capacitors in parallel and asked what the total capacitance was. Not a single one got it correct - not only did they say the caps were in series (they were not) it even got the series capacitance calculations wrong. I couldn’t believe they whiffed it and had to check myself and sure enough I got the same results as the author and tried all types of prompt magic to get the right answer… no dice.

I also saw an ad for an AI tool that’s designed to help you understand schematics. In its pitch to you, it’s showing what looks like a fairly generic guitar distortion pedal circuit and does manage to correctly identify a capacitor as blocking DC but failed to mention it also functions as a component in an RC high-pass filter. I chuckled when the voice over proudly claims “they didn’t even teach me this in 4 years of Electrical Engineering!” (Really? They don’t teach how capacitors block DC and how RC filters work????)

If you’re in this space you probably need to compile your own carefully curated codex and train something more specialized. The general purpose ones struggle too much.

> I couldn’t believe they whiffed it

Why should we expect a general-purpose instruction-tuned LLM to get this right in the first place? I am not at all surprised it didn't work, and I would be more than a little surprised if it did.

> Why should we expect a general-purpose instruction-tuned LLM to get this right in the first place?

The argument goes: Language encodes knowledge, so from the vast reams of training data, the model will have encoded the fundamentals of electromagnetism. This is based in the belief that LLMs being adept at manipulating language, are therefore inchoate general intelligences, and indeed, attaining AGI is a matter of scaling parameters and/or training data on the existing LLM foundations.