CERN uses ultra-compact AI models on FPGAs for real-time LHC data filtering
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Re: CERN uses ultra-compact AI models on FPGAs for real-time LHC data filtering
#2[dead]
Re: CERN uses ultra-compact AI models on FPGAs for real-time LHC data filtering
#3Hey 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).
Re: CERN uses ultra-compact AI models on FPGAs for real-time LHC data filtering
#4Hey 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).
There's no mention of SLMs or LLMs, though.
> This work represents a compelling real-world demonstration of “tiny AI” — highly specialised, minimal-footprint neural networks
FPGAs for Neural Networks have been s thing since before the LLM era.
Re: CERN uses ultra-compact AI models on FPGAs for real-time LHC data filtering
#5Does 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.
Re: CERN uses ultra-compact AI models on FPGAs for real-time LHC data filtering
#6Hey 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).
There's no mention of SLMs or LLMs, though. > This work represents a compelling real-world demonstration of “tiny AI” — highly specialised, minimal-footprint neural networks FPGAs for Neural Networks have been s thing since before the LLM era.
Huh? The first paragraph literally says they are using LLMs
> [ GENEVA, SWITZERLAND — March 28, 2026 ] — 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).
Re: CERN uses ultra-compact AI models on FPGAs for real-time LHC data filtering
#7Hey 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).
There's no mention of SLMs or LLMs, though. > This work represents a compelling real-world demonstration of “tiny AI” — highly specialised, minimal-footprint neural networks FPGAs for Neural Networks have been s thing since before the LLM era.
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Re: CERN uses ultra-compact AI models on FPGAs for real-time LHC data filtering
#8Hey 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).
Are they some ancient small-scale integration VLSI design? Do they broadcast on a low-frequency VHF band? Face it: Oxymorons like those are part of the technical world. "VLSI" was a current term back when whole CPUs were made out of fewer transistors than we use for register files now, and "VHF" is low frequency even by commercial broadcasting standards.
Re: CERN uses ultra-compact AI models on FPGAs for real-time LHC data filtering
#9Does 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.
This might be some journalistic confusion. If you go to the CERN documentation at https://twiki.cern.ch/twiki/bin/view/CMSPublic/AXOL1TL2025 it states
> The AXOL1TL V5 architecture comprises a VICReg-trained feature extractor stacked on top of a VAE.
Re: CERN uses ultra-compact AI models on FPGAs for real-time LHC data filtering
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