Unleashing AI Transformer Models on FPGAs for Accelerating LHC and Particle Physics
Description
This project centers on the deployment of Transformer models for Field Programmable Gate Arrays (FPGA), in order to seamlessly integrate AI capabilities into particle physics experiments, specifically focusing on the L1 triggering schemes and real-ti
Detailed example
This AI system has two fold use cases, represnetation learning for LHC Trigger and multi-modal magnet quench detection algorithms.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
This effort focuses on Transformer models for representation learning on Field Programmable Gate Arrays (FPGA), in order to seamlessly integrate AI capabilities into particle physics experiments, specifically focusing on the CMS level-1 (L1) trigger at the High-Luminosity LHC (HL-LHC) and real-time magnet quench detection. While conventional methods for event identification have limitations, modern AI and machine learning techniques offer superior alternatives.
Audit / financial statement impact
The use case does not have an effect on civil rights/liberties/privacy, access to education/housing/insurance/credit/employment, access to critical government resources/services, human health/safety, critical infrastructure/public safety
Controls / human review
ATO: No; PIA: Not published
Data needed
research datasets from scientific experiments