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Testing Generative AI for Circuit Board Design

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Re: Testing Generative AI for Circuit Board Design

#71

Post by ai4ever »

Terr_ wrote: post #53

To recycle a rant, there's a whole bunch of hype and investor money riding on a very questionable idea here, namely: "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?"

investor money is seduced by the possibilities and many of the investors are in it for FOMO.

few really understand what the limits of the tech are. and if it will even unlock the usecases for which it is being touted.

Re: Testing Generative AI for Circuit Board Design

#72

Post by echelon »

surfingdino wrote: post #45
HanClinto wrote: post #14

This feels like an excellent demonstration of the limitation of zero-shot LLMs. It feels like the wrong way to approach this. 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 migh…

> This feels like an excellent demonstration of the limitation of zero-shot LLMs. It feels like the wrong way to approach this. There is one posted on HN every week. How many more do we need to accept the fact this tech is not what it is sold at and we are bored waiting for it get good? I am not say "get better", because it keeps getting better, but somehow doesn't get good.

I'm in awe of the progress in AI images, music, and video. This is probably where AI shines the most.

Soon everything you see and hear will be built up through a myriad of AI models and pipelines.

Re: Testing Generative AI for Circuit Board Design

#73

Post by [deleted] »

surfingdino wrote: post #45
HanClinto wrote: post #14

This feels like an excellent demonstration of the limitation of zero-shot LLMs. It feels like the wrong way to approach this. 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 migh…

> This feels like an excellent demonstration of the limitation of zero-shot LLMs. It feels like the wrong way to approach this. There is one posted on HN every week. How many more do we need to accept the fact this tech is not what it is sold at and we are bored waiting for it get good? I am not say "get better", because it keeps getting better, but somehow doesn't get good.

[deleted]

Re: Testing Generative AI for Circuit Board Design

#74

Post by visarga »

HanClinto wrote: post #27
DHaldane wrote: post #18

Earlier quoted context omitted.

My gut agrees with you that LLMs shouldn't do this well on a specialty domain. But I think there's also the bitter lesson to be learned here: many times people say LLMs won't do well on a task, they are often surprised either immediately or a few months later. Overall not sure what to expect, but fine tuning experiments would be interesting regardless.

> But I think there's also the bitter lesson to be learned here: many times people say LLMs won't do well on a task, they are often surprised either immediately or a few months later. Heh. This is very true. I think perhaps the thing I'm most amazed by is that simple next-token prediction seems to work unreasonably well for a great many tasks. I just don't know how well that will scale into more complex tasks. With s…

> I just want some sort of mechanism where the model can "think" longer about harder problems.

There is such a mechanism - multiple rounds of prompting. You can implement diverse patterns (chains, networks) of prompts.

Re: Testing Generative AI for Circuit Board Design

#75

Post by refulgentis »

surfingdino wrote: post #45
HanClinto wrote: post #14

This feels like an excellent demonstration of the limitation of zero-shot LLMs. It feels like the wrong way to approach this. 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 migh…

> This feels like an excellent demonstration of the limitation of zero-shot LLMs. It feels like the wrong way to approach this. There is one posted on HN every week. How many more do we need to accept the fact this tech is not what it is sold at and we are bored waiting for it get good? I am not say "get better", because it keeps getting better, but somehow doesn't get good.

There's this odd strain of thought that there's some general thing that will pop for hucksters and the unwashed masses, who are sheep led along by huckster wolves who won't admit LLMs aint ???, because they're profiting off it

It's frustrating because it's infantalizing, it derails the potential of an interesting technical discussion (ex. Here, diffusion), and it misses the mark substantially.

At the end of the day, it's useful in a thousand ways day to day, and the vast majority of people feel this way. The only people I see vehemently arguing the opposite seem to assume only things with 0 error rate are useful or are upset about money in some form.

But is that really it? I'm all ears. I'm on a 5 hour flight. I'm genuinely unclear on whats going on that leads people to take this absolutist position that they're waiting for ??? to admit ??? about LLMs.

Yes, the prose machine didnt nail circuit design, that doesn't mean whatever They you're imagining needs to give up and accept ???

Re: Testing Generative AI for Circuit Board Design

#76

Post by cjk2 »

DHaldane wrote: post #52
cjk2 wrote: post #23

Earlier quoted context omitted.

Make two separate signals arrive at exactly the same time on two 50 ohm transmission lines that start and end next to each other and go around a right hand bend. At 3.8GHz. Edit: no VSWR constraint. Can add that later :) Edit 2: oh or design a board for a simple 100Mohm input instrumentation amplifier which knows what a guard ring is and how badly the solder mask will screw it up :)

Right - LLMs would be a bit silly for these cases. Both overkill and underkill. Current approach for length matching is throw it off to a domain specific solver. Example test-circuit: https://x.com/DuncanHaldane/status/1803210498009342191 How exact is exactly the same time? Current solver matches to under 10fs, and I think at that level you'd have to fab it to see how close you get with fiber weave skew and all that.…

Yeah. But you need $200k worth of Keysight kit to test it.

The point is there’s a methodology to solve these problems already. Is this better? And can it use and apply it?

Re: Testing Generative AI for Circuit Board Design

#77

Post by cjk2 »

bmicraft wrote: post #43
cjk2 wrote: post #23

Earlier quoted context omitted.

Make two separate signals arrive at exactly the same time on two 50 ohm transmission lines that start and end next to each other and go around a right hand bend. At 3.8GHz. Edit: no VSWR constraint. Can add that later :) Edit 2: oh or design a board for a simple 100Mohm input instrumentation amplifier which knows what a guard ring is and how badly the solder mask will screw it up :)

It would seem to me that the majority of boards would be a lot more forgiving. Are you saying you wouldn't be impressed if it could do only say 70% of board designs completely?

No because it’s hard enough picking up an experienced human’s designs and work with them. A 70% done board is a headache to unwrap. I’d start again.

Re: Testing Generative AI for Circuit Board Design

#78

Post by seveibar »

HanClinto wrote: post #66
seveibar wrote: post #61

I work on generative AI for circuit board design with tscircuit, IMO it's definitely going to be the dominant form of bootstrapping or combining circuit designs in the near future ( 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 circui…

I'd be interested in reading more of your findings! Are you able to accomplish this with prompt-engineering, or are you doing fine-tuning of LLMs / custom-trained models?

No fine tuning needed, as long as the target language/DSL is fairly natural, just give eg a couple examples of tscircuit React, atopile JotX etc and it can generate compliant circuits. It can hallucinate imports, but if you give it an import list you can improve that a lot.

Re: Testing Generative AI for Circuit Board Design

#79

Post by built_with_flux »

sehugg wrote: post #41

How does this compare to Flux.ai? https://docs.flux.ai/tutorials/ai-for-hardware-design

flux.ai founder here

Agree with OP that the raw models aren't that useful for schematic/pcb design.

It's why we build flux from the ground up to provide the models with the right context. The models are great moderators but poor sources of great knowledge.

Here are some great use cases:

https://www.youtube.com/watch?v=XdH075ClrYk

https://www.youtube.com/watch?v=J0CHG_fPxzw&t=276s

https://www.youtube.com/watch?v=iGJOzVf0o7o&t=2s

and here a great example of levering AI to go from idea to full design https://x.com/BuildWithFlux/status/1804219703264706578

Re: Testing Generative AI for Circuit Board Design

#80

Post by ben_w »

refulgentis wrote: post #75
surfingdino wrote: post #45

Earlier quoted context omitted.

> This feels like an excellent demonstration of the limitation of zero-shot LLMs. It feels like the wrong way to approach this. There is one posted on HN every week. How many more do we need to accept the fact this tech is not what it is sold at and we are bored waiting for it get good? I am not say "get better", because it keeps getting better, but somehow doesn't get good.

There's this odd strain of thought that there's some general thing that will pop for hucksters and the unwashed masses, who are sheep led along by huckster wolves who won't admit LLMs aint ???, because they're profiting off it It's frustrating because it's infantalizing, it derails the potential of an interesting technical discussion (ex. Here, diffusion), and it misses the mark substantially. At the end of the day,…

> But is that really it? I'm all ears. I'm on a 5 hour flight. I'm genuinely unclear on whats going on that leads people to take this absolutist position that they're waiting for ??? to admit ??? about LLMs.

Irony: humans think in very black-and-white terms, one could even say boolean; conversely LLMs display subtly and nuance.

When I was a kid, repeats of Trek had Spock and Kirk defeating robots with the liar's paradox, yet today it seems like humans are the ones who are broken by it while the machines are just going "I understood that reference!"

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