Speaker
Description
The ATLAS Level-0 Global Trigger is a mission critical system opting to take advantage of the full calorimeter granularity during Run-4 and beyond. Level-0 Global will be executing a cascade of trigger algorithms combined both the calorimeter information and the muons. Within the Next Generation Trigger project at CERN there is a dedicated work package (WP2.1) exploring large deployment of Machine Learning based algorithms to further enhance selections within the Global system. Given the tight latency and throughput conditions that Global is operating at, any solution developed at WP2.1 has to be deployed in custom hardware solution using FPGAs. The cutting edge FPGA technologies include within the same package dedicated co-processing chiplets optimised for Machine Learning applications. Such a device is the Adaptive Interface Engines provided in the Versal Premium packages. In this talk we will present the work performed within the scope of the NGT WP2.1 aiming to characterise the performance of those devices, provide some generic implementations for specific ML models and explore the feasibility of deploying them in the harsh environment of a mission critical system (L0-Global).