They used a custom neural net with autoencoders, which contain convolutional layers. They trained it on previous experiment data.

https://arxiv.org/html/2411.19506v1

Why is it so hard to elaborate what AI algorithm / technique they integrate? Would have made this article much better

Might be related: https://www.youtube.com/watch?v=T8HT_XBGQUI (Big Data and AI at the CERN LHC by Dr. Thea Klaeboe Aarrestad)

https://www.youtube.com/watch?v=8IZwhbsjhvE (From Zettabytes to a Few Precious Events: Nanosecond AI at the Large Hadron Collider by Thea Aarrestad)

Page: https://www.scylladb.com/tech-talk/from-zettabytes-to-a-few-...

How are FPGAs "bruned into silicon"? Would be news to me that there are ASICs being taped out at CERN

CERN has been doing HEP experiments for decades. What did it use before the current incarnation of AI? The AI label seems to be more marketing and superficial than substantial. It’s a bit sad that a place like CERN feels the need to make it public that it is on the bandwagon.

Thanks for the thoughtful comments and links really appreciated the high-signal feedback. We've updated the article to better reflect the actual VAE-based AXOL1TL architecture (variational autoencoder for anomaly detection). Added the arXiv paper and Thea Aarrestad's talks to the Primary Sources.

A bit of hype in the AI wording here. This could be called a chip with hardcoded logic obtained with machine learning

Not on the same extreme level, but I know that some coffee machines use a tiny CNN based model locally/embedded. There is a small super cheap camera integrated in the coffee machine, and the model does three things: (1) classifies the container type in order to select type of coffee, (2) image segmentation - to determine where the cup/hole is placed, (3) regression - to determine the volume and regulate how much coffee to pour.

Very important! This is not a LLM like the ones so often called AI these days. Its a neural network in a FPGA.

Intuitively, I’ve always had an impression that using an analogue circuit would be feasible for neural networks (they just matrix multiplication!). These should provide instantaneous output.

Isn’t this kind of approach feasible for something so purpose-built?

Hey Siri, show me an example of an oxymoron!

> CERN is using extremely small, custom large language models physically burned into silicon chips to perform real-time filtering of the enormous data generated by the Large Hadron Collider (LHC).

I think chips having a single LLM directly on them will be very common once LLMs have matured/reached a ceiling.

Do they actually have ASICs or just FPGAs? The article seems a bit unclear.

That's what Groq did as well: burning the Transformer right onto a chip (I have to say I was impressed by the simplicity, but afterwards less so by their controversial Kushner/Saudi investment) .

the fact that 99% of LHC data is just gone forever is insane

cern has been using neural networks for decades

Does string theory finally make sense when we ad AI hallucinations?

When is the price of fabbing silicon coming down, so every SMB can do it?

Does anyone know why they are using language models instead of a more purpose-built statistical model? My intuition is that a language model would either be overfit, or its training data would have a lot of noise unrelated to the application and significantly drive up costs.