APEIRON is an INFN Scientific Committee 5 funded project aimed at designing and developing a framework to study, prototype and deploy AI-based real-time processing apparatuses boosting particle identification capabilities in trigger systems or performing efficient online data reduction for triggerless ones.
It involves the definition of the general architecture of a heterogeneous distributed execution platform along with its software stack and a set of relevant use cases to validate it.
NA62 at CERN is a fixed target experiment on ultra-rare kaon decays and represents the main use case for APEIRON.
High particle rates and prompt online data selections are the pillars of its experimental strategy given that the physics signal of interest is ten orders of magnitude less frequent than the background.
To upgrade the trigger and data acquisition system of NA62 we are building shallow neural networks that extract high level features from a Ring Imaging Cherenkov detector (RICH) and we are testing them for real-time inference on FPGA using HLS technique.
Fully connected and convolutional architectures have been explored getting different performances in terms of classification accuracy, computational latency and digital resources utilization on the target FPGA device.
In this short talk we briefly introduce the models, present the status of the project and sketch the perspectives of our work.