I'm no expert in the matter, but for "holistic" things (where there are a lot of cross-connections and inter-dependencies) it feels like a diffusion-based generative structure would be better-suited than next-token-prediction. I've felt this way about poetry-generation, and I feel like it might apply in these sorts of cases as well.
Additionally, this is a highly-specialized field. From the conclusion of the article:
> Overall we have some promising directions. Using LLMs for circuit board design looks a lot like using them for other complex tasks. They work well for pulling concrete data out of human-shaped data sources, they can do slightly more difficult tasks if they can solve that task by writing code, but eventually their capabilities break down in domains too far out of the training distribution.
> We only tested the frontier models in this work, but I predict similar results from the open-source Llama or Mistral models. Some fine tuning on netlist creation would likely make the generation capabilities more useful.
I agree with the authors here.
While it's nice to imagine that AGI would be able to generalize skills to work competently in domain-specific tasks, I think this shows very clearly that we're not there yet, and if one wants to use LLMs in such an area, one would need to fine-tune for it. Would like to see round 2 of this made using a fine-tuning approach.
I cannot help but think there are some similarities between large model generative AI and human reasoning abilities.
For example if I ask a physician with a really high IQ some general questions about say anything like fixing shocks on my mini van … he may have some better ideas than me.
However he may be wrong since he specialized in medicine, although he may have provided some good overall info.
Let’s take a lower IQ mechanic who has worked as a mechanic for 15 years. Despite this human having less IQ, less overall knowledge on general topics … he gives a much better answer of fixing my shocks.
So with LLM AI fine tuning looks to be key as it is with human beings. Large data sets that are filtered / summarized with specific fields as the focus.
> The AI generated circuit was three times the cost and size of the design created by that expert engineer at TI. It is also missing many of the necessary connections.
Exactly what I expected.
Edit: to clarify this is even below the expectations of a junior EE who had a heavy weekend on the vodka.
It kind of grosses me out that we are entering a world where programming could be just testing (to me) random permutations of programs for correctness.
Most people are wrong that AI won't be able to do this soon. The same way you can't expect an AI to generate a website in assembly, but you CAN expect it to generate a website with React/tailwind, you can't expect an AI to generate circuits without having strong functional blocks to work with.
Great work from the author studying existing solutions/models- I'll post some of my findings soon as well! The more you play with it, the more inevitable it feels!
I don't know how feasible it is. This would probably take low $millions or so of training, data collection and research to get not trash results.
I'd certainly love it for trying to diagnose circuits.
It's probably not really that possible even at higher end consumer grade 1200dpi.
They are already far ahead of many others with respect to next generation EE CAD.
Judicious application of AI would be a big win for them.
Edit: adding "TL;DRN'T" to my vocabulary XD
"If we make a really really good specialty text-prediction engine, it could be able to productively mimic an imaginary general AI, and if it can do that then it can productively mimic other specialty AIs, because it's all just intelligence, right?"
TLDR: We test LLMs to figure out how helpful they are for designing a circuit board. We focus on utility of frontier models (GPT4o, Claude 3 Opus, Gemini 1.5) across a set of design tasks, to find where they are and are not useful. They look pretty good for building skills, writing code, and getting useful data out of datasheets.
TLDRN'T: We do not explore any proprietary copilots, or how to apply a things like a diffusion model to the place and route problem.
* Failed to properly understand and respond to the requirements for component selection, which were already pretty generic.
* Succeeded in parsing the pinout for an IC but produced an incomplete footprint with incorrect dimensions.
* Added extra components to a parsed reference schematic.
* Produced very basic errors in a description of filter topologies and chose the wrong one given the requirements.
* Generated utterly broken schematics for several simple circuits, with missing connections and aggressively-incorrect placement of decoupling capacitors.
Any one of these failures, individually, would break the entire design. The article's conclusion for this section buries the lede slightly:
> The AI generated circuit was three times the cost and size of the design created by that expert engineer at TI. It is also missing many of the necessary connections.
Cost and size are irrelevant if the design doesn't work. LLMs aren't a third as good as a human at this task, they just fail.
The LLMs do much better converting high-level requirements into (very) high-level source code. This make sense (it's fundamentally a language task), but also isn't very useful. Turning "I need an inverting amplifier with a gain of 20" into "amp = inverting_amplifier('amp1', gain=-20.0)" is pretty trivial.
The fact that LLMs apparently perform better if you literally offer them a cookie is, uh... something.
Edit: Better at chain of thought, long running agentic tasks, following rigid directions.