CERN Burns Tiny AI Models Into Silicon for Real-Time LHC Data Filtering — 40,000 Exabytes/Year, Microsecond Decisions

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CERN is using the open-source HLS4ML tool to compile quantized, pruned ML models into synthesizable C++ that deploys directly onto FPGAs and custom ASICs for real-time particle collision filtering. The LHC generates ~40,000 exabytes/year at hundreds of terabytes/second, requiring microsecond-level decisions about which events to keep. The models are 'trained to be small from the get-go' — quantized to extreme bit widths, consuming far less power and silicon than GPU/TPU alternatives. 51 points, 39 comments on HN.