  Due to the binary spike signals making converting the traditional high-power
multiply-accumulation (MAC) into a low-power accumulation (AC) available, the
brain-inspired Spiking Neural Networks (SNNs) are gaining more and more
attention. However, the binary spike propagation of the Full-Spike Neural
Networks (FSNN) with limited time steps is prone to significant information
loss. To improve performance, several state-of-the-art SNN models trained from
scratch inevitably bring many non-spike operations. The non-spike operations
cause additional computational consumption and may not be deployed on some
neuromorphic hardware where only spike operation is allowed. To train a
large-scale FSNN with high performance, this paper proposes a novel Dual-Stream
Training (DST) method which adds a detachable Auxiliary Accumulation Pathway
(AAP) to the full spiking residual networks. The accumulation in AAP could
compensate for the information loss during the forward and backward of full
spike propagation, and facilitate the training of the FSNN. In the test phase,
the AAP could be removed and only the FSNN remained. This not only keeps the
lower energy consumption but also makes our model easy to deploy. Moreover, for
some cases where the non-spike operations are available, the APP could also be
retained in test inference and improve feature discrimination by introducing a
little non-spike consumption. Extensive experiments on ImageNet, DVS Gesture,
and CIFAR10-DVS datasets demonstrate the effectiveness of DST.
