  The utilisation of event cameras represents an important and swiftly evolving
trend aimed at addressing the constraints of traditional video systems.
Particularly within the automotive domain, these cameras find significant
relevance for their integration into embedded real-time systems due to lower
latency and energy consumption. One effective approach to ensure the necessary
throughput and latency for event processing systems is through the utilisation
of graph convolutional networks (GCNs). In this study, we introduce a series of
hardware-aware optimisations tailored for PointNet++, a GCN architecture
designed for point cloud processing. The proposed techniques result in more
than a 100-fold reduction in model size compared to Asynchronous Event-based
GNN (AEGNN), one of the most recent works in the field, with a relatively small
decrease in accuracy (2.3% for N-Caltech101 classification, 1.7% for N-Cars
classification), thus following the TinyML trend. Based on software research,
we designed a custom EFGCN (Event-Based FPGA-accelerated Graph Convolutional
Network) and we implemented it on ZCU104 SoC FPGA platform, achieving a
throughput of 13.3 million events per second (MEPS) and real-time partially
asynchronous processing with a latency of 4.47 ms. We also address the
scalability of the proposed hardware model to improve the obtained accuracy
score. To the best of our knowledge, this study marks the first endeavour in
accelerating PointNet++ networks on SoC FPGAs, as well as the first hardware
architecture exploration of graph convolutional networks implementation for
real-time continuous event data processing. We publish both software and
hardware source code in an open repository: https://github.com/vision-agh/***
(will be published upon acceptance).
