Event-based vision is an emerging research field involving processing data generated by Dynamic Vision Sensors (neuromorphic cameras). One of the latest proposals in this area are Graph Convolutional Networks (GCNs), which allow to process events in its original sparse form while maintaining high detection and classification performance. In this paper, we present the hardware implementation of a~graph generation process from an event camera data stream, taking into account both the advantages and limitations of FPGAs. We propose various ways to simplify the graph representation and use scaling and quantisation of values. We consider both undirected and directed graphs that enable the use of PointNet convolution. The results obtained show that by appropriately modifying the graph representation, it is possible to create a~hardware module for graph generation. Moreover, the proposed modifications have no significant impact on object detection performance, only 0.08% mAP less for the base model and the N-Caltech data set.Finally, we describe the proposed hardware architecture of the graph generation module.

翻译：基于事件的视觉是一个新兴研究领域，涉及处理由动态视觉传感器（神经形态相机）生成的数据。该领域的最新进展之一是图卷积网络（GCN），它能够在保持高检测和分类性能的同时，以原始稀疏形式处理事件。本文提出了从事件相机数据流生成图的硬件实现方案，同时考虑了FPGA的优势和局限性。我们提出了多种简化图表示的方法，并采用值缩放与量化技术。我们同时考虑了无向图和可启用PointNet卷积的有向图。结果表明，通过适当修改图表示，可以创建用于图生成的硬件模块。此外，所提出的修改对目标检测性能无显著影响：在基础模型和N-Caltech数据集上，平均精度均值（mAP）仅下降0.08%。最后，我们描述了所提出的图生成模块的硬件架构。