  State-of-the-art machine-learning methods for event cameras treat events as
dense representations and process them with conventional deep neural networks.
Thus, they fail to maintain the sparsity and asynchronous nature of event data,
thereby imposing significant computation and latency constraints on downstream
systems. A recent line of work tackles this issue by modeling events as
spatiotemporally evolving graphs that can be efficiently and asynchronously
processed using graph neural networks. These works showed impressive
computation reductions, yet their accuracy is still limited by the small scale
and shallow depth of their network, both of which are required to reduce
computation. In this work, we break this glass ceiling by introducing several
architecture choices which allow us to scale the depth and complexity of such
models while maintaining low computation. On object detection tasks, our
smallest model shows up to 3.7 times lower computation, while outperforming
state-of-the-art asynchronous methods by 7.4 mAP. Even when scaling to larger
model sizes, we are 13% more efficient than state-of-the-art while
outperforming it by 11.5 mAP. As a result, our method runs 3.7 times faster
than a dense graph neural network, taking only 8.4 ms per forward pass. This
opens the door to efficient, and accurate object detection in edge-case
scenarios.
