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.

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.

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.

It's not an LLM, it is a purpose built model. https://arxiv.org/html/2411.19506v1

5 years ago we would've called it a Machine Learning algorithm. 5 years before that, a Big Data algorithm.

… they’re not? Who said they are? The article even explicitly says they’re not?

Is a LLM logic in weights derived from machine learning?

AI is not a new thing, and machine learned logic definitely counts as AI.

I'm half expecting to see "AI model" appearing as stand-in for "linear regression" at this point in the cycle.

> That's what Groq did as well: burning the Transformer right onto a chip

Are you perhaps confusing Groq with the Etched approach? IIUC Etched is the company that "burned the transformer onto a chip". Groq uses LPUs that are more generalist (they can run many transformers and some other architectures) and their speed comes from using SRAM.