Optimising Graph Representation for Hardware Implementation of Graph
  Convolutional Networks for Event-based Vision
Kamil Jeziorek Piotr Wzorek Krzysztof Blachut Andrea Pinna Tomasz Kryjak
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.