  The Spiking Neural Networks (SNNs), renowned for their bio-inspired
operational mechanism and energy efficiency, mirror the human brain's neural
activity. Yet, SNNs face challenges in balancing energy efficiency with the
computational demands of advanced tasks. Our research introduces the RTFormer,
a novel architecture that embeds Re-parameterized Temporal Sliding Batch
Normalization (TSBN) within the Spiking Transformer framework. This innovation
optimizes energy usage during inference while ensuring robust computational
performance. The crux of RTFormer lies in its integration of reparameterized
convolutions and TSBN, achieving an equilibrium between computational prowess
and energy conservation.
