New Machine Learning Architectures For Fast Data Selection

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Particle Physics experiments can have huge limitations to their Physics reach due to the sheer amount of data that is produced, and consequently how little they can store. In addition huge amounts of the data produced at a facility such as the LHC are intrinsically not as scientifically interesting as rare processes. In order to solve these challenges "Trigger" systems were developed to quickly scan data and identify potentially interesting signatures. These systems have to maintain a high efficiency for the processes of interest while also staying in the technical confines of the experiment such as latency - the time it takes for the algorithm to make its decision. Several technologies have been used in this endeavour such as FPGAs, CPUs, or GPUs at various stages of selection, with each of the solutions better suited to different tasks. However each of the solutions have their own limitations. The new Xilinx Versal platform aims to combine the benefits of CPUs, GPUs and FPGAs into a flexible platform for parallel processing. This architecture is particularly well suited to machine learning (ML) based algorithms and we propose to exploit our expertise in this area to evaluate these new platforms and gain a head-start in developing new ML architectures for future particle physics experiments