GoKawiil[ GENEVA, SWITZERLAND — March 28, 2026 ] — CERN is using extremely small, custom artificial intelligence models physically burned into silicon chips to perform real-time filtering of the enormous data generated by the Large Hadron Collider (LHC).
LHC tunnel and detectors
OVERVIEW
Proton collision in LHC detector
The Large Hadron Collider (LHC) generates an extraordinary volume of raw data — approximately 40,000 exabytes per year, equivalent to roughly one quarter of the entire current internet. During peak operation, the data stream can reach hundreds of terabytes per second, far exceeding the capacity of any feasible storage or conventional computing system.
Because it is physically impossible to store or process the full dataset, CERN must make split-second decisions at the detector level: which collision events contain potentially groundbreaking scientific value, and which should be discarded forever. This real-time selection process is one of the most demanding computational challenges in modern science.
To meet these extreme requirements, CERN has deliberately moved away from conventional GPU or TPU-based artificial intelligence architectures. Instead, the laboratory develops highly optimized, ultra-compact AI models that are compiled and physically implemented directly into custom silicon — primarily field-programmable gate arrays (FPGAs) and application-specific integrated circuits (ASICs). These hardware-embedded models enable ultra-low-latency inference at the very edge of the detector system, where decisions must be made in microseconds or even nanoseconds.
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