  Spiking neural networks (SNNs) have garnered interest due to their energy
efficiency and superior effectiveness on neuromorphic chips compared with
traditional artificial neural networks (ANNs). One of the mainstream approaches
to implementing deep SNNs is the ANN-SNN conversion, which integrates the
efficient training strategy of ANNs with the energy-saving potential and fast
inference capability of SNNs. However, under extreme low-latency conditions,
the existing conversion theory suggests that the problem of misrepresentation
of residual membrane potentials in SNNs, i.e., the inability of IF neurons with
a reset-by-subtraction mechanism to respond to residual membrane potentials
beyond the range from resting potential to threshold, leads to a performance
gap in the converted SNNs compared to the original ANNs. This severely limits
the possibility of practical application of SNNs on delay-sensitive edge
devices. Existing conversion methods addressing this problem usually involve
modifying the state of the conversion spiking neurons. However, these methods
do not consider their adaptability and compatibility with neuromorphic chips.
We propose a new approach based on explicit modeling of residual errors as
additive noise. The noise is incorporated into the activation function of the
source ANN, which effectively reduces the residual error. Our experiments on
the CIFAR10/100 dataset verify that our approach exceeds the prevailing ANN-SNN
conversion methods and directly trained SNNs concerning accuracy and the
required time steps. Overall, our method provides new ideas for improving SNN
performance under ultra-low-latency conditions and is expected to promote
practical neuromorphic hardware applications for further development.
