A multi-core FPGA-based 2D-clustering algorithm for real-time image processing is presented. The algorithm uses a moving window technique adjustable to the cluster size in order to minimize the FPGA resources required for cluster identification. The window size is generic and application dependent (size/shape of clusters in the input images). A key element of this algorithm is the possibility to instantiate multiple clustering cores working on different windows that can be used in parallel to increase performance exploiting more resources on the FPGA device. In addition to the offered parallelism, the algorithm is executed in a pipeline, thus allowing the cluster readout to be performed in parallel with the cluster identification and the data pre-processing. The algorithm is developed for the Fast Tracker processor for the trigger upgrade of the ATLAS experiment but is easily adjustable to other image processing applications which require real-time pixel clustering.
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