  Spiking neural networks (SNNs), as a biology-inspired method mimicking the
spiking nature of brain neurons, is a promising energy-efficient alternative to
the traditional artificial neural networks (ANNs). The energy saving of SNNs is
mainly from multiplication free property brought by binarized intermediate
activations. In this paper, we proposed a Multiple Threshold (MT) approach to
alleviate the precision loss brought by the binarized activations, such that
SNNs can reach higher accuracy at fewer steps. We evaluate the approach on
CIFAR10, CIFAR100 and DVS-CIFAR10, and demonstrate that MT can promote SNNs
extensively, especially at early steps. For example, With MT,
Parametric-Leaky-Integrate-Fire(PLIF) based VGG net can even outperform the ANN
counterpart with 1 step.
