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NP-Hard is NP-hard... "heuristics" in this case is just a euphemism for a fantasy cost-function to scam shareholders. It is a named problem, and can't be misa

NP-Hard is NP-hard... "heuristics" in this case is just a euphemism for a fantasy cost-function to scam shareholders. It is a named problem, and can't be misappropriated like the $9Tn worth of FOSS code "AI" scraped and compacted for isomorphic plagiarism tokens. =3

monocasa 1mo ago
So, then how are today's pcb layout techs solving these Np-hard problems in your mind, armed only with Altium?
- Joel_Mckay 1mo ago
  Only decades of standards compliance, workmanship training, and experience. Human beings are very good at spotting patterns in noisy jungles, and making rational design tradeoffs. Auto-routers have been around far longer than even smartphones. Somewhat functional in the trivial problem domains, and always useless where design choices mattered. Best of luck solving a named problem. =3
  - monocasa 1mo ago
    > Only decades of standards compliance, workmanship training, and experience If that could solve NP-hard problems, computer science would be a very different field.
  - KingMachiavelli 1mo ago
    Computationally what’s the difference between a human and an LLM besides scale and speed? Trained humans are still using heuristics and shortcuts. Just because these are subconscious and only possessed by talented, trained professionals doesn’t make the skill computationally special.
    - Joel_Mckay 1mo ago
      LLM do not think because they are not real "AI", but it does copy the linear patterns people exhibit if statistically salient within the granularity of the higher dimensional vector search space proximity. Watermarking does skew the compaction slightly, but not far from resulting output patterns. =3 https://en.wikipedia.org/wiki/The_Subservient_Chicken https://en.wikipedia.org/wiki/The_Subservient_Chicken
      - adrianvi 1mo ago
        I do think that slightly smarter LLMs and a good (although not perfect) autorouting algorithm could solve 99% of hobbyist's projects and simple industry boards, due to the repetition of those patterns in real life. The LLM would learn the heiristics (example: data lines first, power lines later, etc.) and would request the autorouter to do that routing, then take the image output and request a different part (depth-first). If later no routing is posible with that configuration, after some retries it could try another way. It wouldn't solve complex boards, and engineers are always needed for short comings (and even if not, for research), but I wouldn't say this is something different than poetry, music or pixel art, LLMs can imitate although most of the times in a uncanny way.
        Joel_Mckay 1mo ago
        Every trace on a PCB is a traveling salesman problem with ballooning complexity. https://en.wikipedia.org/wiki/Travelling_salesman_problem https://en.wikipedia.org/wiki/Travelling_salesman_problem While physics informed models do exist, they are still going to burn a lot of compute to generate failure modes people didn't know were possible. =3 https://www.youtube.com/watch?v=T4Upf_B9RLQ https://www.youtube.com/watch?v=T4Upf_B9RLQ
        adrianvi 1mo ago
        Yes, the optimal solution won't be achievable, it will burn lots of compute and we need better simulations so that less errors are made (and will be made). Still, if LLMs are capable of writing working code, art and solving math problems, they qre definitely capable of doing suboptimal routing (with some algorithmic aid)
  - murderfs 1mo ago
    > Somewhat functional in the trivial problem domains, and always useless where design choices mattered. This is laughable when you consider that semiconductor design is the least "trivial" problem domain, and it happens entirely in RTL. There's basically zero human intervention when it comes to layout: even floorplanning is being automated nowadays.
    - Joel_Mckay 1mo ago
      Fab standard cell libraries are already the practical optimization of their current capabilities, and even FPGA try to minimize routing proximity with various levels of success. However, there are again named problems that constrain what naive people can get away with.... https://en.wikipedia.org/wiki/Clock_domain_crossing https://en.wikipedia.org/wiki/Clock_domain_crossing > floorplanning is being automated nowadays. Procedural generation or parametric design is not the same, as the behavior and constraints were rationally engineered by people. LLM would simply look at the outputs during training, abstract it as looking similar to a potato-chip factory, and generate a nonsense answer some fool assumes is appropriate. Humans are wired to see meaningful patterns in chaotic systems even when they aren't really there. Neuromorphic computing may create real "AI" someday, but it almost certainly won't be from LLM cults. =3 https://en.wikipedia.org/wiki/Pareidolia https://en.wikipedia.org/wiki/Pareidolia
- unrented7977 1mo ago
  Simple: we aren't. We can find local optima, but there's no real way to prove a given design is the best of all possible designs. We can only find designs which work and meet the given criteria. One can prove that a design is correct, but it requires the same class of compute as an LLM to properly simulate. Circuit boards are hellishly complicated once you start simulating EM and RF responses. As for how a good engineer does this, it's the same way pilots can fly helicopters: intuition. It's a skill built up from lots of reading the rules and theory, and lots of practice. One gets a feel for how the various fields and energies are moving. Which, when you think about it, is just about how neural nets learn, isn't that neat? I think the real answer here is that machine learning is absolutely capable of producing satisfactorily correct circuit boards. In principle. But I think the problem space is far too large for today's ML techniques and verification/iteration is just way too expensive. Maybe in another few years, I just don't think we have enough compute for it yet.
bluGill 1mo ago
The best solution might be NP-hard. However we don't need the best, we need one that is good enough, and that is more doable.
- deleted 1mo ago
  [deleted]
Aurornis 1mo ago
> NP-Hard is NP-hard... "heuristics" in this case is just a euphemism for a fantasy cost-function to scam shareholders. I don’t know why you’re trying to make this so hard. The problem is NP hard whether I do it or a machine does it. I can still do a good job despite being NP hard. I believe a machine with enough compute will be able to, too.
- Joel_Mckay 1mo ago
  Copying millions of other peoples work for a similar looking result excludes the design compromises every engineer must make during the process. LLM don't think because it is not real "AI", and simply conflates every isomorphic vector search proximity output. Auto-routers have existed for decades, and they do work in the trivial design cases... but are almost always a liability where design choices matter. Best of luck =3
  - Aurornis 1mo ago
    LLMs are not the same as auto routers that have existed for decades. They function completely differently and are capable of taking more context into account, all the way to fetching and reading data sheets and considering design compromises.
    - Joel_Mckay 1mo ago
      Sure, one is a conflated chaotic nonsense generator, and the other is a chaotic random walk local "fit" evaluator set by an now arbitrary cost function. NP-hard is still NP-hard... and named problems are rarely solvable with naive approaches. Best of luck =3

monocasa 1mo ago

So, then how are today's pcb layout techs solving these Np-hard problems in your mind, armed only with Altium?

Joel_Mckay 1mo ago
Only decades of standards compliance, workmanship training, and experience. Human beings are very good at spotting patterns in noisy jungles, and making rational design tradeoffs. Auto-routers have been around far longer than even smartphones. Somewhat functional in the trivial problem domains, and always useless where design choices mattered. Best of luck solving a named problem. =3
- monocasa 1mo ago
  > Only decades of standards compliance, workmanship training, and experience If that could solve NP-hard problems, computer science would be a very different field.
- KingMachiavelli 1mo ago
  Computationally what’s the difference between a human and an LLM besides scale and speed? Trained humans are still using heuristics and shortcuts. Just because these are subconscious and only possessed by talented, trained professionals doesn’t make the skill computationally special.
  - Joel_Mckay 1mo ago
    LLM do not think because they are not real "AI", but it does copy the linear patterns people exhibit if statistically salient within the granularity of the higher dimensional vector search space proximity. Watermarking does skew the compaction slightly, but not far from resulting output patterns. =3 https://en.wikipedia.org/wiki/The_Subservient_Chicken https://en.wikipedia.org/wiki/The_Subservient_Chicken
    - adrianvi 1mo ago
      I do think that slightly smarter LLMs and a good (although not perfect) autorouting algorithm could solve 99% of hobbyist's projects and simple industry boards, due to the repetition of those patterns in real life. The LLM would learn the heiristics (example: data lines first, power lines later, etc.) and would request the autorouter to do that routing, then take the image output and request a different part (depth-first). If later no routing is posible with that configuration, after some retries it could try another way. It wouldn't solve complex boards, and engineers are always needed for short comings (and even if not, for research), but I wouldn't say this is something different than poetry, music or pixel art, LLMs can imitate although most of the times in a uncanny way.
      - Joel_Mckay 1mo ago
        Every trace on a PCB is a traveling salesman problem with ballooning complexity. https://en.wikipedia.org/wiki/Travelling_salesman_problem https://en.wikipedia.org/wiki/Travelling_salesman_problem While physics informed models do exist, they are still going to burn a lot of compute to generate failure modes people didn't know were possible. =3 https://www.youtube.com/watch?v=T4Upf_B9RLQ https://www.youtube.com/watch?v=T4Upf_B9RLQ
        adrianvi 1mo ago
        Yes, the optimal solution won't be achievable, it will burn lots of compute and we need better simulations so that less errors are made (and will be made). Still, if LLMs are capable of writing working code, art and solving math problems, they qre definitely capable of doing suboptimal routing (with some algorithmic aid)
- murderfs 1mo ago
  > Somewhat functional in the trivial problem domains, and always useless where design choices mattered. This is laughable when you consider that semiconductor design is the least "trivial" problem domain, and it happens entirely in RTL. There's basically zero human intervention when it comes to layout: even floorplanning is being automated nowadays.
  - Joel_Mckay 1mo ago
    Fab standard cell libraries are already the practical optimization of their current capabilities, and even FPGA try to minimize routing proximity with various levels of success. However, there are again named problems that constrain what naive people can get away with.... https://en.wikipedia.org/wiki/Clock_domain_crossing https://en.wikipedia.org/wiki/Clock_domain_crossing > floorplanning is being automated nowadays. Procedural generation or parametric design is not the same, as the behavior and constraints were rationally engineered by people. LLM would simply look at the outputs during training, abstract it as looking similar to a potato-chip factory, and generate a nonsense answer some fool assumes is appropriate. Humans are wired to see meaningful patterns in chaotic systems even when they aren't really there. Neuromorphic computing may create real "AI" someday, but it almost certainly won't be from LLM cults. =3 https://en.wikipedia.org/wiki/Pareidolia https://en.wikipedia.org/wiki/Pareidolia
unrented7977 1mo ago
Simple: we aren't. We can find local optima, but there's no real way to prove a given design is the best of all possible designs. We can only find designs which work and meet the given criteria. One can prove that a design is correct, but it requires the same class of compute as an LLM to properly simulate. Circuit boards are hellishly complicated once you start simulating EM and RF responses. As for how a good engineer does this, it's the same way pilots can fly helicopters: intuition. It's a skill built up from lots of reading the rules and theory, and lots of practice. One gets a feel for how the various fields and energies are moving. Which, when you think about it, is just about how neural nets learn, isn't that neat? I think the real answer here is that machine learning is absolutely capable of producing satisfactorily correct circuit boards. In principle. But I think the problem space is far too large for today's ML techniques and verification/iteration is just way too expensive. Maybe in another few years, I just don't think we have enough compute for it yet.

bluGill 1mo ago

The best solution might be NP-hard. However we don't need the best, we need one that is good enough, and that is more doable.

deleted 1mo ago
[deleted]

Aurornis 1mo ago

> NP-Hard is NP-hard... "heuristics" in this case is just a euphemism for a fantasy cost-function to scam shareholders. I don’t know why you’re trying to make this so hard. The problem is NP hard whether I do it or a machine does it. I can still do a good job despite being NP hard. I believe a machine with enough compute will be able to, too.

Joel_Mckay 1mo ago
Copying millions of other peoples work for a similar looking result excludes the design compromises every engineer must make during the process. LLM don't think because it is not real "AI", and simply conflates every isomorphic vector search proximity output. Auto-routers have existed for decades, and they do work in the trivial design cases... but are almost always a liability where design choices matter. Best of luck =3
- Aurornis 1mo ago
  LLMs are not the same as auto routers that have existed for decades. They function completely differently and are capable of taking more context into account, all the way to fetching and reading data sheets and considering design compromises.
  - Joel_Mckay 1mo ago
    Sure, one is a conflated chaotic nonsense generator, and the other is a chaotic random walk local "fit" evaluator set by an now arbitrary cost function. NP-hard is still NP-hard... and named problems are rarely solvable with naive approaches. Best of luck =3