  Event-based data are commonly encountered in edge computing environments
where efficiency and low latency are critical. To interface with such data and
leverage their rich temporal features, we propose a causal spatiotemporal
convolutional network. This solution targets efficient implementation on
edge-appropriate hardware with limited resources in three ways: 1) deliberately
targets a simple architecture and set of operations (convolutions, ReLU
activations) 2) can be configured to perform online inference efficiently via
buffering of layer outputs 3) can achieve more than 90% activation sparsity
through regularization during training, enabling very significant efficiency
gains on event-based processors. In addition, we propose a general affine
augmentation strategy acting directly on the events, which alleviates the
problem of dataset scarcity for event-based systems. We apply our model on the
AIS 2024 event-based eye tracking challenge, reaching a score of 0.9916 p10
accuracy on the Kaggle private testset.
