  Spiking Neural Network (SNN) is acknowledged as the next generation of
Artificial Neural Network (ANN) and hold great promise in effectively
processing spatial-temporal information. However, the choice of timestep
becomes crucial as it significantly impacts the accuracy of the neural network
training. Specifically, a smaller timestep indicates better performance in
efficient computing, resulting in reduced latency and operations. While, using
a small timestep may lead to low accuracy due to insufficient information
presentation with few spikes. This observation motivates us to develop an SNN
that is more reliable for adaptive timestep by introducing a novel
regularisation technique, namely Spatial-Temporal Regulariser (STR). Our
approach regulates the ratio between the strength of spikes and membrane
potential at each timestep. This effectively balances spatial and temporal
performance during training, ultimately resulting in an Anytime Optimal
Inference (AOI) SNN. Through extensive experiments on frame-based and
event-based datasets, our method, in combination with cutoff based on softmax
output, achieves state-of-the-art performance in terms of both latency and
accuracy. Notably, with STR and cutoff, SNN achieves 2.14 to 2.89 faster in
inference compared to the pre-configured timestep with near-zero accuracy drop
of 0.50% to 0.64% over the event-based datasets. Code available:
https://github.com/Dengyu-Wu/AOI-SNN-Regularisation
