16 ms·
AI learns the “dark art” of RFIC design
- johnnyApplePRNG 4mo agoHopefully one day AI will design away the need for popups and other-things-that-prevent-you-from-reading-the-damn-article.
- inquirerGeneral 4mo ago[dead]
- esikich 4mo agoYep, can't wait until everything is free and costs nothing to generate content. Free hosting and electricity will be super sweet too. Won't need admins or even the Internet. Everything I want will just be free for me because I don't think anything has value.
- pseudohadamard 4mo agoIt's not really that magical. As TFA points out, RFIC design, way beyond normal RF engineering, is close to black magic that relies a lot on the knowledge and experience of the designer, assisted by what would have been supercomputer-level-a-few-decades-ago modelling and design tools. What AI can do is a breadth-first exploration of all possible outcomes and then pick the best-performing one rather than the human-level "this seems like a good path to go down, let's explore it further".
- deleted 4mo ago[deleted]
- fred_is_fred 4mo agoDoes it need to be magical to be interesting or useful?
- flossEveryday 4mo agothe biggest question for me is how robust are these designs. in the journal articles they did show measurements of real devices which agreed fine with predictions, but i didn't find them addressing it explicitly in the text. also, some systems they presented contained subblocks that were conventionally designed that could be carrying some of the weight. or maybe i'm just sour that they're coming for my job? or maybe that's what they want us to think? i think what wins in practice is simple ideas that can work in spite of all manufacturing and environment variations, and model limitations -- think stuff like feedback and symmetry. and what they show here is the opposite of that. i've done blind optimization of circuit parameters some times only to end up realizing some pretty simple such ideas that i'd missed (like "you need symmetry here" or "you just need more bandwidth here") and made complete sense when you thought about them. so i wonder if we can't tweak a few pixels in their structures and reveal something simpler. also, obligatory mention: "genetic antennas"
- iwhalen 4mo agoI came to mention genetic antennae as well! Since you beat me to it, I'll add something that relates relates you were saying on "realizing some pretty simple... ideas". I think a big plus of computer aided design like this is "innovization"[1]. Somewhat awkward term. But, a system like this leading one to deeper understanding of a particular process is the general idea. It's a fun feeling in practice. [1]: https://dl.acm.org/doi/10.1145/1143997.1144266 https://dl.acm.org/doi/10.1145/1143997.1144266
- adrian_b 4mo ago> but i didn't find them addressing it explicitly in the text Yes, this is exactly what bothers me about this article and about a few similar articles published in the past, that they do not contain any evidence that their claims about the usefulness of AI in design are true. In TFA it says that the role of AI is replacing the electromagnetic simulator in the optimization process, by guessing the behavior of the structure, which is many orders of magnitude faster than a simulation. This sounds plausible, but in order to believe this I would want to see the differences between AI guesses and real measurements, in the case of structures with geometries that are very different from those used in the training of the AI. Also I would want to see exactly with which simulators they have compared the speed of the AI model. There are various simulation approaches for electromagnetic fields and electronic circuits, that can trade-off accuracy for speed, so I am not convinced that AI inference takes necessarily much less time than some faster low-accuracy methods of simulation, which would still be more accurate and more reliable than AI guesses.
- dist-epoch 4mo agoI am confused, every day I read on HN that AI's can just interpolate the data they have seen in training, and that they are structurally incapable of coming up with something new, creative and not in the training distribution.
- LogicFailsMe 4mo agoIn my experience, if you tell them to research the web to see if their idea has been pursued before, you can get them to keep proposing new things until something is sufficiently new, even if it's a new interpolation between existing concepts, that it's effectively an original idea.
- deleted 4mo ago[deleted]
- dgellow 4mo agoHave you read the article? The creative element came from the researchers: > In our new approach, the architecture begins essentially from nothing and is progressively assembled through successive iterations. The system explores the design space by generating myriad candidate circuit combinations and mapping the resulting performance trade-offs as it navigates this landscape. Because the process is not biased by prior human design choices, it can produce completely novel circuit topologies that look markedly different from those created by human designers.
- deleted 4mo ago[deleted]
- zdragnar 4mo agoThis is analogies to finding a new prime number by brute force using existing maths, rather than inventing new maths to get there. The AI in this case didn't create a novel technology- it merely used the existing technology without basing the new design on a previous one. The whole "human couldn't come up with it" is because the possible design space is so large, there's no reason a human would start where the AI did. The thing the AI did better than humans was brute forcing a solution faster. Still a very handy thing to have, but it isn't "creating" in the sense that it invented new materials or fabrication processes or anything novel.
- robviren 4mo agoReminds me of good ol genetic algorithm search. Guess and check can be quite powerful, especially if you can toss in agent in the loop guidance. https://en.wikipedia.org/wiki/Evolved_antenna https://en.wikipedia.org/wiki/Evolved_antenna
- ben_w 4mo agoWas going to say much the same. I recall one story about a genetic algorithm to make an oscillator with the fewest possible components, and it successfully did so by surprising the humans with a single wire, i.e. an antenna picking up nearby stray RF.
- robviren 4mo agoThat is my favorite part of GA. Gradient free optimization but it turns out making a good fitness function is hard and like 70% of the time it just exploits some assumptions or gap you have in your theories. Really reveals the problem in different ways that traditional ML.
- deleted 4mo ago[deleted]
- nz 4mo agoAs someone who does a lot of genetic programming (like, old-school, without AI/LLMs, etc), I can confirm that the fitness function is very difficult to get right, especially if you are trying to evolve programs that have "adversarial fitness" -- you'd need to maintain a hall-of-fame, and that just makes the runs take _much_ longer, because, chances are, your fitness function is the bottle-neck. So, it is very hands-off, but also very expensive, and it is never clear if optimizing the fitness function is worth it, because the fitness function itself may be insufficiently or incorrectly specified. However, I do think that people should try, even with just a whiteboard or a notebook, to design a fitness-function, for their problem, as if they were going to try to evolve it, because (1) it forces them to explicate their correctness constraints, and (2) they may discover that the program that they are trying to write _is equivalent_ to the fitness function. I'll give you an example for point 2. Many years ago, I had to parse a gnarly language, and I chose to do it via Chomsky Grammars (that automatically build a tree based on the grammar-spec). Chomsky Grammars are cool, in that they are basically just a state-machine, but they are incredibly difficult to debug: when they work, they might work incorrectly (malformed tree), and when they fail, they give no reason for failure (because even with a trace, you are trying to figure out which backtrack should not have happened). So, out of desperation, I started to consider using genetic programming to just evolve a correct Chomsky Grammar. It became clear that there are only 2 possible fitness functions (1) a function that tests a hand-picked input against a hand-crafted tree-output (which is vulnerable to over-fitting), and (2) a function that is not (well, is much less) vulnerable to over-fitting, but is effectively a pre-existing, correct grammar that can produce those trees. If you are in situation 2, then the genetic programming is not necessary, unless you are trying to create an optimized (or obfuscated) parser, and even then the optimization may be overfit to the test-inputs (even if they are generated test-inputs from the grammar itself). If you are in situation 1, then you are better off re-evaluating your approach (I abandoned the Chomsky Grammar notation, and invented one that is much easier to understand and debug, without losing any of the expressiveness -- it also happens to be slower, but fast-and-broken is worthless compared to not-so-fast-and-works-fine). One place where genetic programming has been consistently awesome, is in parameter-search style problems (e.g. your genome is a long list of floats, representing weights and/or anti-weights, and you need to find out which weights give you more fitness (or less error)). I hear good things about variable-neighborhood-search, but have yet to try it.
- pshirshov 4mo agoChips? I've tried to task Opus, Gemini and Codex with a simple PCB. All of them placed holes correctly but can't understand that the traces should not cross physically.
- dempedempe 4mo agoRead the article.
- SamBam 4mo agoThe AI in the article isn't an LLM.
- deadbabe 4mo agoWe have always known the old trick of genetic algorithms to produce better radio chips. The problem isn’t the design: its manufacturing restraints. This is nothing new or impressive.
- autoexec 4mo ago"Humans couldn't even imagine" seems like overselling it, but I'm sure that machine learning algorithms can brute force their way to chip designs no one has tried before and that some of those might be useful to us. That seems like a pretty reasonable thing for a computer to do.
- ece 4mo agoMachine learning layer cake with some brute force crumbs.
- fooqux 4mo agoIt's marketing bullshit. For one, it's like proving a negative; you can't prove to me that humans couldn't have imagined it. Second, humans have already imagined quite a lot of crazy stuff...
- throwawayffffas 4mo agoIt really just means, irregular, unconventional, not in line with traditional designs.
- Izkata 4mo agoHere's a story about a genetic algorithm evolving a circuit that works with logic gates that aren't even connected, seemingly by using magnetic flux: https://www.damninteresting.com/on-the-origin-of-circuits/ https://www.damninteresting.com/on-the-origin-of-circuits/
- mallomarmeasle 4mo agoThanks, I was trying to recall that article. Fascinating stuff to this non-expert.
- jcims 4mo agoReminds me of this old article - https://www.damninteresting.com/on-the-origin-of-circuits/ https://www.damninteresting.com/on-the-origin-of-circuits/ One of my favorite little morsels of internet goodness.
- zephen 4mo agoYes. An example of a species so specialized and optimized that it can no longer adapt. Also, an example of POSIWID.
- gooseyard 4mo agoI was going to post this as well, its delightful to see that other people enjoy it since it was really mind-blowing when I read it. It's interesting since I saw another comment near yours that raised the question of robustness of the lab-grown design, which I thought was kind of the most fascinating part of the damninteresting article was the revelation that the evolved programs were inseparable from the single physical FPGA used in the training. Since this RFIC training model employs a simulator, do you suppose that the quirks of the physical hardware on which the simulator runs are sufficiently isolated from the training such that a pair of designs would behave similarly when the simulator was run on distinct hardware? And I guess the even more obvious question is whether a design evolved on a simulator would have any hope of behaving as expected in physical hardware? My hunch about the latter is no, although it still seems like an interesting study, and I often find myself thinking that really understanding what was going on with the FPGAs might be a prerequisite for really understanding how to master reinforcement learning. Anyway I'm glad you posted this and if you have any other favorites related to this domain send them my way!
- jcims 4mo agoGreat comment! >I thought was kind of the most fascinating part of the damninteresting article was the revelation that the evolved programs were inseparable from the single physical FPGA used in the training. 100% agree with this!!! It gave me this weird feeling the first time i read it, like the onset of some alien intelligence. xD I definitely think a simulator is the way to go, but I'm guessing tools like that could find problems and edge cases in the simulator that nobody thought to test for. I'm just glad they still have the article up. I bet I've shared it 50 times over the past 20 years lol.
- phendrenad2 4mo agoIn case anyone feels déjà vu, Popular Mechanics wrote about this professor's lab in Jan 2025, with almost the same title: "AI Designed Computer Chips That the Human Mind Can't Understand". I feel a bit of unease when I read this title, not because of the threat of AI, but because the prevailing aphorism that "RF is black magic" is a slap in the face to the millions of physicists and RF engineers who DO understand every bit of this. It's a fun harmless anti-intellectual saw that I don't believe is harmless at all. We need more RF engineers and telling people it's all "black magic" and "wizardry" (and worst of all, saying "even RF engineers don't understand RF") makes it seem like it's not worth studying.
- p_j_w 4mo ago> telling people it's all "black magic" and "wizardry" (and worst of all, saying "even RF engineers don't understand RF") makes it seem like it's not worth studying. I think the opposite is true. It being advertised as difficult to understand is one of the reasons I personally decided to study RF Engineering. The prospect of learning something so challenging pulled me in. The Smith Chart helped.
- deleted 4mo ago[deleted]
- fred_is_fred 4mo agoThe comments here are trending towards "There's nothing new here, I could design 5g radio chips with a cheap linux box running FTP".
- yashthakker 4mo ago[flagged]
- t-writescode 4mo agoI’m a bit frustrated. AI can do a looot of things; but I think as we continue to muddy the waters between LLMs and more traditional machine learning like Monte Carlo, Genetic Algoriths, Expert Systems and other Statistics magic tricks, we’re too aggressively conflating established and morally neutral activities in ML with the concerns that people have about LLMs and Stable Diffusion. Though I also imagine that that is the point.
- georgeecollins 4mo agoIt is a problem because people will talk about what AI can do implying that an LLM can do that thing, making it seem like a pure LLM can do almost anything. On the other hand people will say AI will never be able to do X because an LLM can’t do that thing well natively. AI has become too vague of a term to be useful.
- pixl97 4mo agoWe're relearning that intelligence is spikey, and that different things that we consider 'intelligent' can have vastly different capabilities.
- bigstrat2003 4mo agoWe're learning that people are way too lax with where they apply the term "intelligent". LLMs aren't remotely intelligent, but people are trying to ride the hype train and call them intelligence.
- deleted 4mo ago[deleted]
- subygan 4mo agothis is just false. by any meaningful measure of intelligence. the latest models are much smarter than the bulk of the population. how would you define intelligence?
- Brian_K_White 4mo agoIf you don't know how it works, then you don't know that it works.
- xp84 4mo agoHow does your consciousness work?
- Henchman21 4mo agoPretty well, between all the hallucinogens
- grantcas 4mo ago[dead]
- Brian_K_White 4mo agoUtterly haphazardly and inconsistently of course, same as yours. You thought that was some sort of argument? It slots right in and contradicts nothing.
- xp84 4mo agoI mean you can't explain how your consciousness works, so by your logic you don't "know that it works." But you do know that it works, because here you are.
- Brian_K_White 4mo agoIn fact I do not know that it works because we don't even have a definition for works for that. All we can say, maybe, is that it exists. How well it works is up for grabs but my own strongest suspicion is "with a great many flaws", which I don't consider "works" in most other contexts. The comment was intented to be pithy, which means OBVIOUSLY, it has to generalize and be more poem than encyclopedia. A few words that express the essense of an idea. That is both useful and true even if such an essesnse OBVIOUSLY leaves contrived edge case exceptions unadressed. Congradulations on spotting that the universe is actually infinite.
- skywhopper 4mo agoI don’t know. I can imagine quite a bit.
- Henchman21 4mo agoUnexpected Star Wars! A surprise to be sure, but a welcome one :)
- scoopdeddywoop 4mo agoBut is this AGI?
- activexray 4mo agoI did my PhD on inverse design of electromagnetic structures. I really hate that we're calling this AI when there isn't any training, really.
- deleted 4mo ago[deleted]
- fuzzfactor 4mo ago>To arrive at the “sweet spot” where all these different parameters are balanced into optimal harmony, designers will typically lay out several different versions of the circuit, using intuitions and methods they have picked up in their years of training. I kind of thought the real success is when the designer comes up with key things that are well beyond their training or any training that could have been done up until that time. Based on their years of experience living in an environment where training is table stakes but that's not the thing that's relied upon the most in the end. With LLMs it seems like odds are that a concept which is statistically insignificant in the training set may surface in place of a truly novel solution, effectively displacing the real breakthroughs that actually go beyond trainable performance. In a way that decision-makers can not tell the difference, and that could be the worst part.
- taffydavid 4mo ago> That’s not even to speak of all the movie plots that would have been ruined. I clicked on all the links. Pretty much all of those movies could still work with wired technology. Even the one called cellular, in which a woman is trapped in an attic with a broken landline phone and manages to connect wires and dial a random number. Yes I'm nitpicking. I guess I'm glad we have Wi-Fi and all, but don't try to sell me on it as a crucial plot device
- pfdietz 4mo agoAnd what about all the movie plots ruined by the existence of cell phones?
- taffydavid 4mo agoPeople seem to (in)conveniently have no service in movies a lot, exactly when they need it
- georgeburdell 4mo agoI work in a related field and “inverse” design is what this is called. Such designs usually are not manufacturable. I’m not too worried about my iob. That said we’ve had some success internally having Claude do parameter sweeps
- pfdietz 4mo agoOne great application of AI design is patent poisoning. Use AI to churn out masses of variant designs, make them publicly visible on a web site, and if future patents come out use any collisions to invalidate them or at least restrict their scope (generalization of a patent is limited by prior art.)
- alwa 4mo agoI’m reminded of lawyer Damien Riehl’s (performative) reaction to the Sam Smith infringement decision, back in 2019/2020. He and programmer Noah Rubin algorithmically generated every possible melody (within a certain combinatorial space, in MIDI format as I recall), and purported to release them under CC-0 license [0]. He went on to attract some attention and explain his argument at a regional TEDx event [1]. I seem to recall legal commentators reacting with an eyeroll—apparently judges split much finer hairs than these for a living—but it was a cute stunt. [0] https://allthemusic.info/ https://allthemusic.info/ [1] https://m.youtube.com/watch?v=sJtm0MoOgiU https://m.youtube.com/watch?v=sJtm0MoOgiU and https://www.the-independent.com/tech/music-copyright-algorithm-lawsuit-damien-riehl-a9364536.html https://www.the-independent.com/tech/music-copyright-algorit...
- pfdietz 4mo agoCopyrights are a bit different from patents.
- alwa 4mo agoFor sure, but I suspect the law might look similarly dimly on the argument that “machines systematically generated all possibilities in the problem space” === “somebody already had this idea.” I’d imagine maybe by reading specific human intention into “prior art” and “existing work” and those sorts of terms. Which is not to say let’s not do it anyway and see!
- pfdietz 4mo agoOr they might conclude "even an AI could come up with this, it's obvious". Obviousness is a defense in patent law, not in copyright law.
- Schlagbohrer 4mo agoThe methods outlined in this article aren't new. Scientists were using "genetic algorithms" to design antennas that weren't understood by anyone, but worked well, decades ago.
- bsaul 4mo agoI wonder if our common expectation that true theories somehow had to be beautiful and elegant is going to survive the coming century. What if "real" nature phenomenon were actually best described by horrible mess of impossible equations, that only machines could actually manipulate and reason about ? That would be really sad..
- akomtu 4mo agoDoes the nature look like a horrible mess to you?
- dyauspitr 4mo agoIf you’re not being facetious, then the answer is very much a yes.
- iamflimflam1 4mo agoI often think this about medicine and the human body. We want to believe that our bodies are some miraculous well oiled machine. But it often seems that it’s a barely held together bag of mess.
- HPsquared 4mo agoI think politics and economics work along similar lines.
- idiotsecant 4mo agoBiology is incredibly robust!! I'd say barely held together bag of mess describes something like an internal combustion engine. A primate, on the other hand, is a self-replicating machine capable of self-repair and just about universal fuel sourcing. It has a robust defense network capable of identifying and eradicating a staggering number of foreign replicators. It has holographic design storage, with each cell containing the plan for the whole organisim. It has general cognition based on a world model, and does all this on almost no energy. Biology is incredibly well oiled!
- supertroop 4mo agoAi can’t even place and route a two layer board with a microcontroller and a few peripherals.
- anonu 4mo agoOne takeaway from the article is that they had to get rid of the tried and tested fundamental building blocks of chip design to generate this advancement. I wonder if the same applies for mundane coding. Are the incredible innovations in AI coding actually hampered by rust and python? Should we let AI tools just code in the lowest level possible?
- bryancoxwell 4mo agoI’m intrigued by the question but I do think it has some worrying implications for portability.
- rebolek 4mo agoNo. I'm really not fan of Python but you're not going to parse JSON at assembler level. You need to choose right level of indirection for each task. And LLMs are good at more than Python and Rust. I'm using LLMs to write programs in my own language that is compiled by a very fringe language and it happily does everything from scripting on ESP32 to audio plugins and 3D gfx on desktop.
- wowczarek 4mo agoNow let's get them to come up with a valid design including a valid QR code. Maybe one containing Maxwell's equations.
- matheusmoreira 4mo ago> But freed from the constraints of human-designed templates and the need for humans to even understand the rationale of electromagnetic structures, ... ICs ... can take on truly wild-looking yet efficient designs. I feel like technology is going to become alien at some point. We're all going to be using magical runes instead of chips.
- blobbers 4mo agoGreat, and here I thought my job was safe.
- dmix 4mo agoWhat a well written article
- vahid_r 4mo agoVery interesting. I wonder how hardware craft would look like after adapting AI in a massive scale.
- AaronFriel 4mo agoIt may come as a surprise, but this phenomenon of "uninterpretable" circuits designed by algorithms is 30 years old now. Adrian Thompson's research in the 90s evolved FPGAs that did signal analysis with bizarre features: - A tiny number of cells (far fewer than expected) - No clock, despite performing signal analysis - FPGA cells that were logically disconnected, but when removed caused the device to stop working Even then their approach was taking advantage of the physics in the FPGA. One can only imagine how effective this could be when applied to circuit design with the compute budget of a frontier lab. https://cacm.acm.org/research/analysis-of-unconventional-evolved-electronics/ https://cacm.acm.org/research/analysis-of-unconventional-evo...
- pfj1 4mo agoThose "evolved" FPGAs weren't much of a mystery. They just used undefined parameters (inductive coupling, power supply rail glitching, etc) to achieved the trained outputs. They didn't work when the ambient temperature changed, or when moved to another example of the same FPGA.
- jingpostmedia 4mo ago[flagged]
- avian 4mo ago> Freed from intelligibility and aesthetics, AI designs faster I like this headline. In other words, AI will suck out every last bit that makes engineering fun. I know, I know. The job is to make money for your employer not have fun. AI makes money faster so shut up and do your job. But fuck, I took this career because I found joy in understanding things and making things that look and work well.
- DoctorOetker 4mo agoCan oscilloscopes go the way of the calculator? When can one reasonably expect $10 10GHz oscilloscope on a chip, with some pins for video out and user input in? At some point the economy will realize theres more LLM inference than access to scientific & technologic measurements, its an economic waste not to connect as much scientific instruments as possible to inference which already exists. If the "dark arts" (which never really were that dark, analog designers for higher frequencies used the same Maxwell equations as the analog designers for lower frequencies, even if the implications change with frequency) end up automated by AI, the high wages will disappear, and oscilloscope mfrs won't be able to charge as much.