Versal ACAP processing for ATLAS-TileCal signal reconstruction

Particle detectors at accelerators generate a large amount of data, requiring analysis to derive insights. Collisions lead to signal pile-up, where multiple particles produce signals in the same detector sensors, complicating individual signal identification. This contribution describes the implementation of a deep learning algorithm on a Versal Adaptive Compute Acceleration Platform (ACAP) device for improved processing via parallelization and concurrency. The system will emulate the real conditions in the ATLAS Tile Calorimeter signal reconstruction module. Connected to a host computer via Peripheral Component Interconnect express (PCIe), this system aims for enhanced speed and energy efficiency over Central Processing Units (CPUs) and Graphics Processing Units (GPUs). In the contribution, we will describe in detail the data processing and the hardware, firmware and software components of the system, as well as the implementation of the deep learning algorithm on Versal ACAP device, and the system for transferring data in an efficient way.