  Spike-based neuromorphic hardware has demonstrated substantial potential in
low energy consumption and efficient inference. However, the direct training of
deep spiking neural networks is challenging, and conversion-based methods still
require substantial time delay owing to unresolved conversion errors. We
determine that the primary source of the conversion errors stems from the
inconsistency between the mapping relationship of traditional activation
functions and the input-output dynamics of spike neurons. To counter this, we
introduce the Consistent ANN-SNN Conversion (CASC) framework. It includes the
Consistent IF (CIF) neuron model, specifically contrived to minimize the
influence of the stable point's upper bound, and the wake-sleep conversion
(WSC) method, synergistically ensuring the uniformity of neuron behavior. This
method theoretically achieves a loss-free conversion, markedly diminishing time
delays and improving inference performance in extensive classification and
object detection tasks. Our approach offers a viable pathway toward more
efficient and effective neuromorphic systems.
