  Spiking neural networks (SNNs) are biology-inspired artificial neural
networks (ANNs) that comprise of spiking neurons to process asynchronous
discrete signals. While more efficient in power consumption and inference speed
on the neuromorphic hardware, SNNs are usually difficult to train directly from
scratch with spikes due to the discreteness. As an alternative, many efforts
have been devoted to converting conventional ANNs into SNNs by copying the
weights from ANNs and adjusting the spiking threshold potential of neurons in
SNNs. Researchers have designed new SNN architectures and conversion algorithms
to diminish the conversion error. However, an effective conversion should
address the difference between the SNN and ANN architectures with an efficient
approximation \DSK{of} the loss function, which is missing in the field. In
this work, we analyze the conversion error by recursive reduction to layer-wise
summation and propose a novel strategic pipeline that transfers the weights to
the target SNN by combining threshold balance and soft-reset mechanisms. This
pipeline enables almost no accuracy loss between the converted SNNs and
conventional ANNs with only $\sim1/10$ of the typical SNN simulation time. Our
method is promising to get implanted onto embedded platforms with better
support of SNNs with limited energy and memory.
