  The event streams generated by dynamic vision sensors (DVS) are sparse and
non-uniform in the spatial domain, while still dense and redundant in the
temporal domain. Although spiking neural network (SNN), the event-driven
neuromorphic model, has the potential to extract spatio-temporal features from
the event streams, it is not effective and efficient. Based on the above, we
propose an events sparsification spiking framework dubbed as Razor SNN, pruning
pointless event frames progressively. Concretely, we extend the dynamic
mechanism based on the global temporal embeddings, reconstruct the features,
and emphasize the events effect adaptively at the training stage. During the
inference stage, eliminate fruitless frames hierarchically according to a
binary mask generated by the trained temporal embeddings. Comprehensive
experiments demonstrate that our Razor SNN achieves competitive performance
consistently on four events-based benchmarks: DVS 128 Gesture, N-Caltech 101,
CIFAR10-DVS and SHD.
