A Multi-Core FPGA-Based 2D-Clustering Implementation for Real-Time Image Processing

A multi-core FPGA-based 2D-clustering implementation for real-time image processing is presented in this paper. The clustering algorithm is using a moving window technique to reduce the time and data required for the cluster identification process. The implementation is fully generic, with an adjustable detection window size. A fundamental characteristic of the implementation is that multiple clustering cores can be instantiated. Each core can work on a different identification window that processes data of independent “images” in parallel, thus, increasing performance by exploiting more FPGA resources. The algorithm and implementation are developed for the Fast TracKer processor for the trigger upgrade of the ATLAS experiment but their generic design makes them easily adjustable to other demanding image processing applications that require real-time pixel clustering.