Speaker
Description
The High-Luminosity LHC (HL-LHC) era will confront particle physics experiments with unprecedented challenges in data volume, computational complexity, and real-time decision making. Preparing for this paradigm shift requires innovation across the full computing and triggering stack. Within this context, CERN openlab plays a central role in exploring and validating emerging technologies in close collaboration with industry partners.
This talk presents recent CERN openlab activities aimed at integrating AI techniques into both high-performance computing (HPC) workflows and low-latency trigger systems. On the offline side, we discuss the deployment of machine learning for large-scale simulation, reconstruction, and analysis, including studies of heterogeneous architectures, accelerators, and novel computing models to efficiently process exabyte-scale datasets. On the online side, we highlight R&D efforts to bring AI into real-time environments, enabling inference within strict latency and determinism constraints imposed by the trigger requirements at the HL-LHC.
We review prototyping results, performance studies, and lessons learned from deploying AI across diverse hardware platforms, from GPUs and CPUs to FPGAs and emerging accelerators. Finally, we outline how CERN openlab’s collaborative approach is helping to bridge the gap between cutting-edge research and production-ready solutions, laying the groundwork for scalable, intelligent computing and triggering systems for the HL-LHC and beyond.