Speaker
Description
The LHCb Upgrade II will operate at a data rate of 200 Tb/s, requiring efficient real-time data reduction. A major challenge of this pipeline is the transfer of full timing information from the frontend Electromagnetic Calorimeter (ECAL) to the backend for processing, which is critical for resolving pile-up, background suppression, and enhancing energy resolution. Due to the data rate, full timing information cannot be transmitted, requiring compression of data to reduce bandwidth. To address this, we develop a machine-learning-based compression algorithm, capable of learning detector-specific correlations in the data, outperforming generic compression schemes. Central to this effort is the extension of the hls4ml framework to fully support Microchip architectures, enabling the deployment of optimised autoencoder networks on the PolarFire FPGAs. These networks compress high-granularity timing data with minimal latency, achieving O(25 ns) inference times within stringent resource constraints. This development is key to reducing bandwidth while preserving physics performance and represents an essential step toward maintaining the physics reach of LHCb Upgrade II in the high-luminosity era.