“The new device is built from arrays of resistive random-access memory (RRAM) cells… The team was able to combine the speed of analog computation with the accuracy normally associated with digital processing. Crucially, the chip was manufactured using a commercial production process, meaning it could potentially be mass-produced.”
Article is based on this paper: https://www.nature.com/articles/s41928-025-01477-0
> See article preview image > AI crap CPU > Leaves immediatelyLook, It’s one of those articles again. The bi-monthly “China invents earth-shattering technology breakthrough that we never hear about again.”
“1000x faster?” Learn to lie better. Real technological improvements are almost always incremental, like “10-20% faster, bigger, stronger.” Not 1000 freaking times faster. You lie like a child. Or like Trump.
It can be 1000x faster because it analog. Analog things take very very little time to compute stuff. We don’t generally use them because they are very hard to get the same result twice and updating is also hard
And it’ll be on sale through Temu and Wish.com
sounds like bullshit.
read the paper
The paper doesn’t even claim they achieved it. They only say it could potentially reach it
The problem is with the clickbait headline (on livescience.com), not the paper itself.
(x) Doubt.
Same here. I wait to see real life calculations done by such circuits. They won’t be able to e.g. do a simple float addition without losing/mangling a bunch of digits.
But maybe the analog precision is sufficient for AI, which is an imprecise matter from the start.
Wouldn’t analog be a lot more precise?
Accurate, though, that’s a different story…
No, it wouldn’t. Because you cannot make it reproduceable on that scale.
Normal analog hardware, e.g. audio tops out at about 16 bits of precision. If you go individually tuned and high end and expensive (studio equipment) you get maybe 24 bits. That is eons from the 52 bits mantissa precision of a double float.
The maximum theoretical precision of an analog computer is limited by the charge of an electron, 10^-19 coulombs. A normal analog computer runs at a few milliamps, for a second max. So a max theoretical precision of 10^16, or 53 bits. This is the same as a double precision (64-bit) float. I believe 80-bit floats are standard in desktop computers.
In practice, just getting a good 24-bit ADC is expensive, and 12-bit or 16-bit ADCs are way more common. Analog computers aren’t solving anything that can’t be done faster by digitally simulating an analog computer.
What does this mean, in practice? In what application does that precision show its benefit? Crazy math?
Every operation your computer does. From displaying images on a screen to securely connecting to your bank.
It’s an interesting advancement and it will be neat if something comes of it down the line. The chances of it having a meaningful product in the next decade is close to zero.
They used to use analog computers to solve differential equations, back when every transistor was expensive (relays and tubes even more so) and clock rates were measured in kilohertz. There’s no practical purpose for them now.
In cases of number theory, and RSA cryptography, you need even more precision. They combine multiple integers together to get 4096-bit precision.
If you’re asking about the 24-bit ADC, I think that’s usually high-end audio recording.
Ahh yeah and we should 1. Believe this exists 2. Believe that china doesnt think technology of this caliber isnt a matter of national security
This already a thing, there’s a US lab doing this
Ya but they just deported all the employees, probably
For the love of Christ this thumbnail is triggering, lol
Why? It’s standard socket in SMOBO design (sandwich Motherboard).
The CPU is upside down you dork.
Let me guess, you think The Onion is a real newspaper, right?
Just push ever so slightly more when you hear the crunching sounds.
Then apply thermal paste generously
Pour a bucket of water over it for liquid cooling
1000x!
Is this like medical articles about major cancer discoveries?
yes, except the bullshit cancer discoveries are always in Israel, and the bullshit chip designs are in china.
1000x yes!
It uses 1% of the energy but is still 1000x faster than our current fastest cards? Yea, I’m calling bullshit. It’s either a one off, bullshit, or the next industrial revolution.
EDIT: Also, why do articles insist on using ##x less? You can just say it uses 1% of the energy. It’s so much easier to understand.
I would imagine there’s a kernel of truth to it. It’s probably correct, but for one rarely used operation, or something like that. It’s not a total revolution. It’s something that could be included to speed up a very particular task. Like GPUs are much better at matrix math than the CPU, so we often have that in addition to the CPU, which can handle all tasks, but isn’t as fast for those particular ones.
They’re real, but they aren’t general purpose and lack precision. It’s just analog.
I mean it‘s like the 10th time I‘m reading about THE breakthrough in Chinese chip production on Lemmy so lets just say I‘m not holding my breath LoL.
Yeah it’s like reading about North American battery science. Like yeah ok cool, see you in 30 years when you’re maybe production ready
coming from china, more like 1 -off bs, with nothing to backup on.
https://www.nature.com/articles/s41928-025-01477-0
Here’s the paper published in Nature.
However, it’s worth noting that Nature has had to retract studies before:
https://en.wikipedia.org/wiki/Nature_(journal)#Retractions
From 2000 to 2001, a series of five fraudulent papers by Jan Hendrik Schön was published in Nature. The papers, about semiconductors, were revealed to contain falsified data and other scientific fraud. In 2003, Nature retracted the papers. The Schön scandal was not limited to Nature; other prominent journals, such as Science and Physical Review, also retracted papers by Schön.
Not saying that we shouldn’t trust anything published in scientific journals, but yes, we should wait until more studies that replicate these results exist before jumping to conclusions.
But it only does 16x16 matrix inversion.
Oh noes, how could that -possibly- scale?
To a billion parameter matrix inverter? Probably not too hard, maybe not at those speeds.
To a GPU, or even just the functions used in GenAI? We don’t even know if those are possible with analog computers to begin with.
@TheBlackLounge @kalkulat LLM inference is definitely theoretically possible on analog chips. They just may not scale :v
It’s a weird damn lie if it is.
And the death of the American economy if it isn’t, fingers crossed.
As someone with a 401k I really hope it isn’t.
The economy crashing won’t hurt billionaires but will kill the middle class.
If anything the economy crashing will allow the 0.1% to buy up anything they haven’t gotten already.
Yeah this is literally what happened in 2008. Economic instability stopped banks from lending to would be individual home buyers, but corpos bought up everything they could eagerly with a 20% price cut.
Economic instability is generally better for the people who can weather the storm, i.e. those with resources to spare, because (as you say) they can buy assets on the cheap when the less fortunate run out of cash to survive on and have to liquidate.
It’s long periods of stability that seem to let the lower classes build up a little. Yet another reason why war and strife is of benefit to the rich.
And now you see why they want to crash the economy.
What middle class? 🤔
The one so worried about their 401Ks they won’t risk the ire of the rich.
This seems like promising technology, but the figures they are providing are almost certainly fiction.
This has all the hallmarks of a team of researchers looking to score an R&D budget.
This was bound to happen. Neural networks are inherently analog processes, simulating them digitally is massively expensive in terms of hardware and power.
Digital domain is good for exact computation, analog is better for approximate computation, as required by neural networks.
That and the way companies have been building AI they have been doing so little to optimize compute to instead try to get the research out faster because that’s what is expected in this bubble. I’m absolutely fully expecting to see future research finding plenty of ways to optimize these major models.
But also R&D has been entirely focused on digital chips I would not be at all surprised if there were performance and/or efficiency gains to be had in certain workloads by shifting to analog circuits
You might benefit from watching Hinton’s lecture; much of it details technical reasons why digital is much much better than analog for intelligent systems
BTW that is the opposite of what he set out to prove He says the facts forced him to change his mind
Thank you for the link, it was very interesting.
Even though analogue neural networks have the drawback that you can’t copy the neuron weights (currently, but tech may evolve to do it), they can still have use cases in lower powered edge devices.
I think we’ll probably end up with hybrid designs, using digital for most parts except the calculations.
much of it details technical reasons why digital is much much better than analog for intelligent systems
For current LLMs there would be a massive gain in energy efficiency if analogue computing was used. Much of the current energy costs come from stimulating what effectively analogue processing on digital hardware. There’s a lot lost in the conversation, or “emulation” of analogue.
I wish researchers like Hinton would stick to discussing the tech. Anytime he says anything about linguistics or human intelligence he sounds like a CS major smugly raising his hand in Phil 101 to a symphony of collective groans.
Hinton is a good computer scientist (with an infinitesimally narrow field of expertise). But the guy is philosophically illiterate.
That’s a good point. The model weights could be voltage levels instead of digital representations. Lots of audio tech uses analog for better fidelity.I also read that there’s a startup using particle beams for lithography. Exciting times.
what audio tech uses analog for better fidelity?
Vinyl records, analog tube amplifiers, a good pair of speakers 🤌
Honestly though digital compression now is so good it probably sounds the same.
speakers are analog devices by nature.
The other two are used for the distortions they introduce, so quite literally lower fidelity. Whether some people like those distortions is irrelevant.
You want high fidelity: lossless digital audio formats.
Yeah, I get very good sound out of class d amplifiers. They’re cheap; they’re energy efficient, and they usually pack in features for digital formats because it’s easy to do.
At least one Nobel Laureate has exactly the opposite opinion (see the Hinton lecture above)
This is not a new line of research in the sense that this is not the only place looking in the mixed analog/digital computers. been articles on it for at least a year I think and when digital was taking over there was a lot of discussion around it being inferior to analog so I bet its been being thrown around to combine the two likely since digital became a thing.
Who is China? Why is it so smart?
West Taiwan. Because The Great Ruling Party said so.
Edit: I removed a chatgtp generated summary because I thought it could have been useful.
Anyway just have a good day.The article is like 5 paragraphs, not even a single sheet of paper if printed (with the unneeded images and ads excluded of course). Why does it need a summary‽
The summary was for the paper the article was based on. And it was also put it in an easier to understand language.
This comment violates rule 8 of the community. Please get your AI generated garbage out of here.
In that case I’m editing it. I’m sorry for my mistake, I thought it would be useful to a point. That’s why I said it was AI.
I appreciate that you wanted to help people even if it didn’t land how you intended. :)
No one is reading that.
That’s fine. Just have a good day :)
It was a decent summary, I was replying when you pulled it. Analog has its strengths (the first computers were analog, but electronics was much cruder 70 years ago) and it is def. a better fit for neural nets. Bound to happen.
Nice thorough commentary. The LiveScience article did a better job of describing it for people with no background in this stuff.
The original computers were analog. They were fast, but electronics was -so crude- at the time, it had to evolve a lot … and has in the last half-century.
