  Spiking neural network (SNN), next generation of artificial neural network
(ANN) that more closely mimic natural neural networks offers promising
improvements in computational efficiency. However, current SNN training
methodologies predominantly employ a fixed timestep approach, overlooking the
potential of dynamic inference in SNN. In this paper, we strengthen the
marriage between SNN and event-driven processing with a proposal to consider
cutoff in SNN, which can terminate SNN anytime during the inference to achieve
efficient inference. Two novel optimisation techniques are presented to achieve
inference efficient SNN: a Top-K cutoff and a regularisation. The Top-K cutoff
technique optimises the inference of SNN, and the regularisation are proposed
to affect the training and construct SNN with optimised performance for cutoff.
We conduct an extensive set of experiments on multiple benchmark frame-based
datsets, such as Cifar10/100, Tiny-ImageNet and event-based datasets, including
CIFAR10-DVS, N-Caltech101 and DVS128 Gesture. The experimental results
demonstrate the effectiveness of our techniques in both ANN-to-SNN conversion
and direct training, affirming their compatibility and potential benefits in
enhancing accuracy and reducing inference timestep when integrated with
existing methods. Code available:
https://github.com/Dengyu-Wu/SNN-Regularisation-Cutoff
