A Dynamically Pipelined Dataflow Architecture for Graph Convolutions in Real-Time Event Interpretation

In high-performance data acquisition systems, realtime interpretation of captured signals is crucial due to limited offline storage and processing capabilities. Large scientific applications, such as particle detectors or massive radar testbeds, often capture inherently sparse data for which Point Cloud Networks (PCNs) have shown promising performance in online event processing tasks. In this work, we propose a novel approach for deploying point-based network layers on FPGAs in high-throughput, low-latency environments. We provide a programming model accompanied by an FPGA overlay which exploits a user-defined degree of spatial parallelism. By leveraging our dynamic dataflow architecture, we are able to guarantee time-variant real-time requirements for known point cloud sizes. In two case studies, we evaluate the inference latency of state-of-theart PCN layers. Based on the input point cloud size, our results demonstrate between 5.17x and 26.39x average latency improvements respectively, compared to our baseline implementations without spatial parallelism. Therefore, our proposed concept enables dynamic event interpretation for future scientific data acquisition systems.