  Spiking Neural Networks (SNNs) that operate in an event-driven manner and
employ binary spike representation have recently emerged as promising
candidates for energy-efficient computing. However, a cost bottleneck arises in
obtaining high-performance SNNs: training a SNN model requires a large number
of time steps in addition to the usual learning iterations, hence this limits
their energy efficiency. This paper proposes a general training framework that
enhances feature learning and activation efficiency within a limited time step,
providing a new solution for more energy-efficient SNNs. Our framework allows
SNN neurons to learn robust spike feature from different receptive fields and
update neuron states by utilizing both current stimuli and recurrence
information transmitted from other neurons. This setting continuously
complements information within a single time step. Additionally, we propose a
projection function to merge these two stimuli to smoothly optimize neuron
weights (spike firing threshold and activation). We evaluate the proposal for
both convolution and recurrent models. Our experimental results indicate
state-of-the-art visual classification tasks, including CIFAR10, CIFAR100, and
TinyImageNet.Our framework achieves 72.41% and 72.31% top-1 accuracy with only
1 time step on CIFAR100 for CNNs and RNNs, respectively. Our method reduces 10x
and 3x joule energy than a standard ANN and SNN, respectively, on CIFAR10,
without additional time steps.
