  Brain-inspired spiking neural networks (SNNs) have recently drawn more and
more attention due to their event-driven and energy-efficient characteristics.
The integration of storage and computation paradigm on neuromorphic hardwares
makes SNNs much different from Deep Neural Networks (DNNs). In this paper, we
argue that SNNs may not benefit from the weight-sharing mechanism, which can
effectively reduce parameters and improve inference efficiency in DNNs, in some
hardwares, and assume that an SNN with unshared convolution kernels could
perform better. Motivated by this assumption, a training-inference decoupling
method for SNNs named as Real Spike is proposed, which not only enjoys both
unshared convolution kernels and binary spikes in inference-time but also
maintains both shared convolution kernels and Real-valued Spikes during
training. This decoupling mechanism of SNN is realized by a re-parameterization
technique. Furthermore, based on the training-inference-decoupled idea, a
series of different forms for implementing Real Spike on different levels are
presented, which also enjoy shared convolutions in the inference and are
friendly to both neuromorphic and non-neuromorphic hardware platforms. A
theoretical proof is given to clarify that the Real Spike-based SNN network is
superior to its vanilla counterpart. Experimental results show that all
different Real Spike versions can consistently improve the SNN performance.
Moreover, the proposed method outperforms the state-of-the-art models on both
non-spiking static and neuromorphic datasets.
