Graph Neural Networks (GNNs) are a class of deep learning methods intended to analyze graph data. GNNs include two different phases: the Aggregation phase, in which each node gathers information about its neighbors, and the Combination phase, which usually acts as a Neural Network on the output of the first phase. While the Combination possesses many of the same characteristics as other kinds of NNs with regard to the dataflow and can be optimized accordingly, the Aggregation phase presents some distinctive properties that prevent efficient mapping on traditional NN processors, and requires novel dedicated hardware and software schemes. In this work, an Aggregation Engine is designed based on a 2-D square mesh Network-on-Chip of SIMD cores.