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Extropic is building thermodynamic computing hardware
- quantumHazer 1y agothere is also Normal Computing[0] that are trying different approaches to chips like that. Anyway these are very difficult problems and Extropic already abandoned some of their initial claims about superconductors to pivot to more classical CMOS circuits[1] [0]: https://www.normalcomputing.com https://www.normalcomputing.com [1]: https://www.zach.be/p/making-unconventional-computing-practical https://www.zach.be/p/making-unconventional-computing-practi...
- nfw2 1y agoI don't really understand the purpose of hyping up a launch announcement and then not making any effort whatsoever to make the progress comprehensible to anyone without advanced expertise in the field.
- ipsum2 1y agoThat's the intention. Fill it up with enough jargon and gobbledegook that it looks impressive to investors, while hiding the fact that there's no real technology underneath.
- frozenseven 1y ago>jargon and gobbledegook >no real technology underneath They're literally shipping real hardware. They also put out a paper + posted their code too. Flippant insults will not cut it.
- ipsum2 1y agoNice try. It's smoke and mirrors. Tell me one thing it does better than a 20 year old CPU.
- frozenseven 1y agoMore insults and a blanket refusal to engage with the material. Ok.
- ipsum2 1y agoIf you think comparing hardware performance is an insult, then you have some emotional issues or are a troll.
- frozenseven 1y agoAh, more insults. This will be my final reply to you. I'll say it again. The hardware exists. The paper and code are there. If someone wants to insist that it's fake or whatever, they need to come up with something better than permutations of "u r stoopid" (your response to their paper: https://news.ycombinator.com/item?id=45753471 https://news.ycombinator.com/item?id=45753471). Just engage with the actual material. If there's a solid criticism, I'd like to hear it too.
- maradan 1y agoThis hardware is an analog simulator for Gibbs sampling, which is an idealized physical process that describes random systems with large scale structure. The energy efficient gains come from the fact that it's analog. It may seem like jargon, but Gibbs sampling is an extremely well known concept with decades of work with connections to many areas of statistics, probability theory, and machine learning. The algorithmic problem they need to solve is how to harness Gibbs sampling for large scale ML tasks, but arguably this isn't really a huge leap, it's very similar to EBM learning/sampling but with the advantage of being able to sample larger systems for the same energy.
- rcxdude 1y agoThe fact that there's real hardware and a paper doesn't mean the product is actually worth anything. It's very possible to make something (especially some extremely simplified 'proof of concept' which is not actually useful at all) and massively oversell it. Looking at the paper, it looks like it may have some very niche applications but it's really not obvious that it would be enough to justify the investment needed to make it better than existing general purpose hardware, and the amount of effort that's been put into 'sizzle' aimed at investors makes it look disingenuous.
- frozenseven 1y ago>The fact that there's real hardware and a paper doesn't mean the product is actually worth anything. I said you can't dismiss someone's hardware + paper + code solely based on insults. That's what I said. That was my argument. Speaking of which: >disingenuous >sizzle >oversell >dubious niche value >window dressing >suspicious For the life of me I can't understand how any of this is an appropriate response when the other guy is showing you math and circuits.
- rcxdude 1y agoNo, they're not showing just math and circuits, they're also showing a very splashy and snazzy front page which makes all kinds of vague, exciting sounding claims that aren't really backed up by the very boring (though sometimes useful) application of that math and circuits (neat how the design of those circuits may be). If this was just the paper, I'd say 'cool area of research, dunno if it'll find application though'. I'm criticizing the business case and the messaging around it, not the implementation. Two important questions I think illustrate my point: 1) The paper shows an FPGA implementation which has a 10x speedup compared to a CPU or GPU implementation. Extropic's first customer would have leapt up and started trying to use the FPGA version immediately. Has anyone done this? 2) The paper shows the projected real implementation being ~10x faster than the FPGA version. This is similar to the speedup going from an FPGA to an ASIC implementation of a digital circuit, which is a standard process which requires some notable up-front cost but much less than developing and debugging custom analog chips. Why not go this route, at least initially?
- maradan 1y ago"no really technology underneath" zzzzzzzzzzz
- fastball 1y agoYou not comprehending a technology does not automatically make it vaporware.
- lacy_tinpot 1y agoWhat's not comprehensible? It's just miniaturized lava lamps.
- nfw2 1y agoA lava lamps that just produces randomness, ie for cryptology purposes, is different than the benefit here, which is to produce specific randomness at low energy-cost
- hirako2000 11mo agoIt proves the point it mesmerizes the audience. One can create a true random generator algorithm by plugging a moving computer mouse to its input. Would be easy to put a dozen cages with mouse wheels on in them, real mammals in there, to generate a lot of random numbers, everyone would understand so only funny, they want mysterious!
- d_silin 1y agoIt is a hardware RNG they are building. The claim is that their solution is going to be more computationally efficient for a narrow class of problems (de-noising step for diffusion AI models) vs current state of the art. Maybe. This is what they are trying to create, more specifically: https://pubs.aip.org/aip/apl/article/119/15/150503/40486/Probabilistic-computing-with-p-bits https://pubs.aip.org/aip/apl/article/119/15/150503/40486/Pro...
- A_D_E_P_T 1y agoAn old concept indeed! I think about this Ed Fredkin story a lot... In his words: "Just a funny story about random numbers: in the early days of computers people wanted to have random numbers for Monte Carlo simulations and stuff like that and so a great big wonderful computer was being designed at MIT’s Lincoln laboratory. It was the largest fastest computer in the world called TX2 and was to have every bell and whistle possible: a display screen that was very fancy and stuff like that. And they decided they were going to solve the random number problem, so they included a register that always yielded a random number; this was really done carefully with radioactive material and Geiger counters, and so on. And so whenever you read this register you got a truly random number, and they thought: “This is a great advance in random numbers for computers!” But the experience was contrary to their expectations! Which was that it turned into a great disaster and everyone ended up hating it: no one writing a program could debug it, because it never ran the same way twice, so ... This was a bit of an exaggeration, but as a result everybody decided that the random number generators of the traditional kind, i.e., shift register sequence generated type and so on, were much better. So that idea got abandoned, and I don’t think it has ever reappeared." RIP Ed. https://en.wikipedia.org/wiki/Edward_Fredkin https://en.wikipedia.org/wiki/Edward_Fredkin
- rcxdude 1y agoIt's funny because that did actually reappear at some point with rdrand. But still it's only really used for cryptography, if you just need a random distribution almost everyone just uses a PRNG (a non-cryptographic one is a lot faster still, apart from being deterministic).
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- rvz 1y agoThis looks really amazing if not unbelieveable to the point where it is almost too good to be real. I have not seen benchmarks on Extropic's new computing hardware yet but need to know from experts who are in the field of AI infrastructure at the semiconductor level if this is legit. I'm 75% believing that this is real but have a 25% skepticisim and will reserve judgement until others have tried the hardware. So my only question for the remaining 25%: Is this a scam?
- arisAlexis 1y agoToo good to be true, incomprehensible jargon to go along...
- KaiserPro 1y agoI mean it sure looks like a scam. I really like the design of it though.
- unsupp0rted 1y agoI doubt it’s a scam. Beff might be on to something or completely delusional, but not actively scamming.
- vzcx 1y agoThe best conmen have an abundance confidence in themselves.
- delichon 1y agoThis one just released a prototype platform, "XTR-0", so if it's a fraud the jig will shortly be up. https://extropic.ai/writing/inside-x0-and-xtr-0 https://extropic.ai/writing/inside-x0-and-xtr-0
- natosaichek 1y agoI think it's more a concern that the hardware isn't useful in the real world, rather than that the hardware doesn't meet the description they provide of it.
- behnamoh 1y agoUsually there's a negative correlation between the fanciness of a startup webpage and the actual value/product they'll deliver. This gives "hype" vibes.
- noir_lord 1y agoI'm more impressed that my laptop fans came on when I loaded the page. It's the one attached to my TV that just runs movies/YT - I don't recall the last time I heard the fans.
- ecshafer 1y agoThey did say thermodynamic computing.
- imploded_sub 1y agoSame on a serious dev machine. That page just pegs a core at max, it's sort of impressive.
- noir_lord 1y agoI ran it through googles pagespeed insights. It scored 34 for mobile and completely timed out for desktop with a time limit exceeded warning. It’s hilariously bad.
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- wfurney 1y agoInteresting you say that, I had an instinctual reaction in that vein as well. I chalked it up to bias since I couldn’t think of any concrete examples. Something about the webpage being so nice made me think they’ve spent a lot of time on it (relative to their product?) Admittedly I’m nowhere close to even trying to understand their paper, but I’m interested in seeing what others think about it
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- vlovich123 1y agoI’ve been wondering how long it would take for someone to try probabilistic computing for AI workloads - the imprecision inherent in the workload makes it ideally suited for AI matrix math with a significant power reduction. My professor in university was researching this space and it seemed very interesting. I never thought it could supplant CPUs necessarily but certainly massive computer applications that don’t require precise math like 3D rendering (and now AI) always seemed like a natural fit.
- Imustaskforhelp 1y agoI don't think that it does AI matrix math with significant power reduction but rather it just seems to provide rng? I may be wrong but I don't think what you are saying is true in my limited knowledge, maybe someone can tell what is the reality of it, whether it can do Ai matrix math with significant power reduction or not or if its even their goal right now as to me currently it feels like a lava lamp equivalent* thing as some other commenter said
- rcxdude 1y agoThe paper talks about some quite old-school AI techniques (the kind of thing I learned about in university a decade ago when it was already on its way out). It's not anything to do with matrix multiplications (well, anything do with computing them faster directly) but instead being able to sample from a complex distribution more efficiently by have dedicated circuits to simulate elements of that distribution in hardware. So it won't make your neural nets any faster.
- 6510 1y agoI'm still waiting for my memristors.
- docandrew 1y agoHype aside, if you can get an answer to a computing problem with error bars in significantly less time, where precision just isn’t that important (such as LLMs) this could be a game changer.
- alyxya 1y agoPrecision actually matters a decent amount in LLMs. Quantization is used strategically in places that’ll minimize performance degradation, and models are smart enough so some loss in performance still gives a good model. I’m skeptical how well this would turn out, but it’s probably always possible to remedy precision loss with a sufficiently larger model though.
- fastball 1y agoLLMs are inherently probabilistic. Things like ReLU throw out a ton of data deliberately.
- alyxya 1y agoNo that isn’t throwing out data. Activation functions perform a nonlinear transformation to increase the expressivity of a function. If you did two matrix multiplications without ReLU in between, your function contains less information than with a ReLU in between.
- fastball 1y agoHow are you calculating "less information"?
- shwaj 1y agoI think what they meant was: Two linear transformations compose into a single linear transformation. If you have y = W2(W1*x) = (W2*W1)*x = W*x where W = W2*W1, you've just done one matrix multiply instead of two. The composition of linear functions is linear. The ReLU breaks this because it's nonlinear: ReLU(W1*x) can't be rewritten as some W*x, so W2(ReLU(W1*x)) can't collapse either. Without nonlinearities like ReLU, many layers of a neural network could be collapsed into a single matrix multiplication. This inherently limits the function approximation that it can do, because linear functions are not very good at approximating nonlinear functions. And there are many nonlinearities involved in modeling speech, video, etc.
- trevor_extropic 1y agoIf you want to understand exactly what we are building, read our blogs and then our paper https://extropic.ai/writing https://extropic.ai/writing https://arxiv.org/abs/2510.23972 https://arxiv.org/abs/2510.23972
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- throwaway_7274 1y agoI was hoping the preprint would explain the mysterious ancient runes on the device chassis :(
- ipsum2 1y agoThe answer is that they're cosplaying sci-fi movies, in attempt to woo investors.
- simonerlic 1y agoWhat, is a bit of whimsy illegal?
- dmos62 1y agoWhy are you replying under every other comment here in this low effort, negative manner?
- moralestapia 1y agoNice! This is "quantum" computing, btw.
- trevormccrt 1y agoActually it's not. Here's some stuff to read to get a clearer picture! https://extropic.ai/writing https://extropic.ai/writing
- moralestapia 1y agoIt strictly is not, as no quantum phenomena is being measured (hence why I used the quotes); but if all goes well w/ extropic you'll most likely end up doing quantum again.
- fidotron 1y agoIs this the new term for analog VLSI? Or if we call it analog is it too obvious what the problems are going to be?
- alyxya 1y agoThis seems to be the page that describes the low level details of what the hardware aims to do. https://extropic.ai/writing/tsu-101-an-entirely-new-type-of-computing-hardware https://extropic.ai/writing/tsu-101-an-entirely-new-type-of-... To me, the biggest limitation is that you’d need an entirely new stack to support a new paradigm. It doesn’t seem compatible with using existing pretrained models. There’s plenty of ways to have much more efficient paradigms of computation, but it’ll be a long while before any are mature enough to show substantial value.
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- sashank_1509 1y agoThe cool thing about Silicon Valley is serious people try stuff that may seem wild and unlikely and in the off chance it works, entire humanity benefits. This looks like Atomic Semi, Joby Aviation, maybe even OpenAI in its early days. The bad thing about Silicon Valley is charlatans abuse this openness and friendly spirit, and swindle investors of millions with pipe dreams and worthless technology. I think the second is inevitable as Silicon Valley becomes more famous, more high status without a strong gatekeeping mechanism which is also anathema to its open ethos. Unfortunately this company is firmly in the second category. A performative startup, “changing the world” to satisfy the neurosis of its founders who desperately want to be seen as someone taking risks to change the world. In reality it will change nothing, and go die into the dustbins of history. I hope he enjoys his 15 minutes of fame.
- nfw2 1y agoWhat makes you so sure that extropic is the second and not the first?
- sashank_1509 1y agoFundamentally, gut feels by following the founder on Twitter. But if I had to explain, I don’t understand the point of speeding up or getting true RnG, even for diffusion models this is not a big bottleneck, so it sounds more like a buzzword than actual practical technology.
- jazzyjackson 1y agoHaving a TRNG is easy, you just reverse bias a zener diode or any number of other strategies that rely on physics for noise. Hype is a strategy they're clearly taking, but people in this thread are so dismissive (and I get why, extropic has been vague posting for years and makes it sound like vaporware) but what does everything think they're actually doing with the money? It's not a better dice roller...
- nfw2 1y ago
- Void_ 1y agoThis gives me Devs vibe (2020 TV Series) - https://www.indiewire.com/awards/industry/devs-cinematography-rob-hardy-alex-garland-1234583396/ https://www.indiewire.com/awards/industry/devs-cinematograph...
- tcdent 1y agoSuch an underrated TV show.
- Void_ 1y agoYes, the billionaire driving a Subaru Forester was my favorite part
- vortegne 1y agoThat's what they're trying to do, yeah. To give off a cool vibe I mean. To raise more money. There is nothing even remotely as cool in their real (or not) product. I was very excited when they started specifically because of their cool branding, but the vibe quickly wears off.
- OBELISK_ASI 1y ago[dead]
- arjie 1y agoHas anyone received the dev board? What did you do with it? Curious what this can do.
- frozenseven 1y agoIt's finally here! Extropic has been working on this since 2022. I'm really excited to see how this performs in the real world.
- lordofgibbons 1y agoHow should we think about how much effective compute is being done with these devices compared to classical (GPU) computing? Obviously FLOPs doesn't make sense, so what does?
- hereme888 1y agoLooks like an artifact from Assassin's Creed or Halo.
- motohagiography 1y agoi've followed them for a while and as just a general technologist and not a scientist, i have a probably wrong idea of what they do, but perhaps correcting it will let others write about it more accurately. my handwavy analogy interpretation was they were in-effect building an analog computer for AI model training, using some ideas that originated in quantum computing. their insight is that since model training is itself probabilistic, you don't need discrete binary computation to do it, you just need something that implements the sigmoid function for training a NN. they had some physics to show they could cause a bunch of atoms to polarize (conceptually) instantaneously using the thermodynamic properties of a material, and the result would be mostly deterministic over large samples. the result is what they are calling a "probabilistic bit" or pbit, which is an inferred state over a probability distribution, and where the inference is incorrect, they just "get it in post," because the speed of the training data through a network of these pbits is so much more efficient that it's faster to just augment and correct the result in the model afterwards than to use classical clock cycles to directly compute it.
- jabedude 1y agoQuestion for the experts in the field: why does this need to be a CPU and not a dongle you plug into a server and query?
- antics 1y agoI like this but based on what I am seeing here and the THRML readme, I would describe this as "an ML stack that is fully prepared for the Bayesian revolution of 2003-2015." A kind of AI equivalent of, like, post-9/11 airport security. I mean this in a value-neutral way, as personally I think that era of models was very beautiful. The core idea of THRML, as I understand it, is to present a nice programming interface to hardware where coin-flipping is vanishingly cheap. This is moderately useful to deep learning, but the artisanally hand-crafted models of the mid-2000s did essentially nothing at all except flip coins, and it would have been enormously helpful to have something like this in the wild at that time. The core "trick" of the era was to make certain very useful but intractable distributions built on something called "infinitely exchangeable sequences" merely almost intractable. The trick, roughly, was that conditioning on some measure space makes those sequences plain-old iid, which (via a small amount of graduate-level math) implies that a collection of "outcomes" can be thought of as a random sample of the underlying distribution. And that, in turn, meant that the model training regimens of the time did a lot of sampling, or coin-flipping, as we have said here. Peruse the THRML README[1] and you'll see the who's who of techniques and modeling prodedures of the time. "Gibbs sampling", "probabilistic graphical models", and "energy-based models", and so on. All of these are weaponized coin flipping. I imagine the terminus of this school of thought is basically a natively-probabilistic programming environment. Garden variety deterministic computing is essentially probabilistic computing where every statement returns a value with probability 1. So in that sense, probabilistic computing is a ful generalization of deterministic computing, since an `if` might return a value with some probability other than 1. There was an entire genre of languages like this, e.g., Church. And now, 22 years later, we have our own hardware for it. (Incidentally this line of inquiry is also how we know that conditional joint distributions are Turing complete.) Tragically, I think, this may have arrived too late. This is not nearly as helpful in the world of deep learning, with its large, ugly, and relatively sample-free models. Everyone hates to hear that you're cheering from the sidelines, but this time I really am. I think it's a great idea, just too late. [1]: https://github.com/extropic-ai/thrml/blob/7f40e5cbc460a4e2e913557a6c2a21d9155c4db6/README.md https://github.com/extropic-ai/thrml/blob/7f40e5cbc460a4e2e9...
- wasabi991011 1y agoReally informative insight, thanks. I'm not too familiar with those models, is there any chance that this hardware could lead to a renaissance of sample-based methods? Given efficient hardware, would they scale to LLM size, and/or would they allow ML to answer some types of currently unanswerable questions?
- est 1y agoI listened to the Hinton podcast few days ago, he mentioned (IIRC) that "analog" AIs are bad because the models can not be transfered/duplicated in a lossless way, like in .gguf format, every analog system is built differently you have to re-learn/re-train again somehow Does TSUs have to same issue?
- OBELISK_ASI 1y ago[dead]
- hl_maker 1y agoIn non deterministic computation, verificaition will be key challenge. Curious to see how companies address this.
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- WorldPeas 1y agoI really don't get why they decided to choose usb-b-3.0 and usb-micro-b-2.0 female for this? Usb-c is so much cheaper and more common at this point. Why bother with using such old plugs, especially so when one plug could do the job of 2?
- mikewarot 1y agoEven if this works as intended, the hardware is needs at least another order of magnitude precision to be able to use gradient descent methods. Without that you're stuck with genetic or other less effective strategies to train models. I also have concerns that this is quite unlikely to be able to have sufficiently uncorrelated randomness in all channels to be useful in practical applications.
- Jaauthor 1y agoIn this house we obey the laws of Thermodynamics!
- OBELISK_ASI 1y ago[dead]
- moktonar 1y agoI think you at Extropic should find a way of “entangling “ your pbits..
- OBELISK_ASI 1y ago[dead]