LM-HT SNN: Enhancing the Performance of SNN to ANN Counterpart through
  Learnable Multi-hierarchical Threshold Model
Zecheng Hao Xinyu Shi Zhiyu Pan Yujia Liu Zhaofei Yu Tiejun Huang
Compared to traditional Artificial Neural Network (ANN), Spiking Neural
Network (SNN) has garnered widespread academic interest for its intrinsic
ability to transmit information in a more biological-inspired and
energy-efficient manner. However, despite previous efforts to optimize the
learning gradients and model structure of SNNs through various methods, SNNs
still lag behind ANNs in terms of performance to some extent. The recently
proposed multi-threshold model provides more possibilities for further
enhancing the learning capability of SNNs. In this paper, we rigorously analyze
the relationship among the multi-threshold model, vanilla spiking model and
quantized ANNs from a mathematical perspective, then propose a novel LM-HT
model, which is an equidistant multi-hierarchical model that can dynamically
regulate the global input current and membrane potential leakage on the time
dimension. In addition, we note that the direct training algorithm based on the
LM-HT model can seamlessly integrate with the traditional ANN-SNN Conversion
framework. This novel hybrid learning framework can effectively improve the
relatively poor performance of converted SNNs under low time latency. Extensive
experimental results have demonstrated that our LM-HT model can significantly
outperform previous state-of-the-art works on various types of datasets, which
promote SNNs to achieve a brand-new level of performance comparable to
quantized ANNs.