Spiking Neural Networks (SNNs) can do inference with low power consumption due to their spike sparsity. ANN-SNN conversion is an efficient way to achieve deep SNNs by converting well-trained Artificial Neural Networks (ANNs). However, the existing methods commonly use constant threshold for conversion, which prevents neurons from rapidly delivering spikes to deeper layers and causes high time delay. In addition, the same response for different inputs may result in information loss during the information transmission. Inspired by the biological model mechanism, we propose a multi-stage adaptive threshold (MSAT). Specifically, for each neuron, the dynamic threshold varies with firing history and input properties and is positively correlated with the average membrane potential and negatively correlated with the rate of depolarization. The self-adaptation to membrane potential and input allows a timely adjustment of the threshold to fire spike faster and transmit more information. Moreover, we analyze the Spikes of Inactivated Neurons error which is pervasive in early time steps and propose spike confidence accordingly as a measurement of confidence about the neurons that correctly deliver spikes. We use such spike confidence in early time steps to determine whether to elicit spike to alleviate this error. Combined with the proposed method, we examine the performance on non-trivial datasets CIFAR-10, CIFAR-100, and ImageNet. We also conduct sentiment classification and speech recognition experiments on the IDBM and Google speech commands datasets respectively. Experiments show near-lossless and lower latency ANN-SNN conversion. To the best of our knowledge, this is the first time to build a biologically inspired multi-stage adaptive threshold for converted SNN, with comparable performance to state-of-the-art methods while improving energy efficiency.

翻译：脉冲神经网络（SNN）因其脉冲稀疏性而具备低功耗推理能力。ANN-SNN转换通过将训练良好的人工神经网络（ANN）转换，是实现深度SNN的有效途径。然而现有方法通常采用恒定阈值进行转换，这阻碍了神经元向深层快速传递脉冲，导致高延时。此外，对不同输入产生相同响应可能导致信息传输过程中的信息损失。受生物模型机制启发，我们提出了多阶段自适应阈值（MSAT）。具体而言，对于每个神经元，动态阈值随脉冲发放历史及输入特性变化，且与平均膜电位正相关、与去极化速率负相关。对膜电位和输入的自适应特性使阈值能及时调整以加速脉冲发放并传递更多信息。此外，我们分析了早期时间步中普遍存在的神经元失活脉冲误差，并据此提出脉冲置信度作为衡量正确传递脉冲的神经元置信度的指标。我们在早期时间步利用该脉冲置信度决定是否激发脉冲以减轻该误差。结合所提方法，我们在CIFAR-10、CIFAR-100和ImageNet等非平凡数据集上验证了性能，并分别在IDBM和Google语音指令数据集上进行了情感分类与语音识别实验。实验结果表明实现了近无损且低延时的ANN-SNN转换。据我们所知，这是首次为转换型SNN构建受生物启发的多阶段自适应阈值，其在提升能效的同时性能与最先进方法相当。