Spiking Neural Networks (SNNs) have attracted great attention due to their distinctive characteristics of low power consumption and temporal information processing. ANN-SNN conversion, as the most commonly used training method for applying SNNs, can ensure that converted SNNs achieve comparable performance to ANNs on large-scale datasets. However, the performance degrades severely under low quantities of time-steps, which hampers the practical applications of SNNs to neuromorphic chips. In this paper, instead of evaluating different conversion errors and then eliminating these errors, we define an offset spike to measure the degree of deviation between actual and desired SNN firing rates. We perform a detailed analysis of offset spike and note that the firing of one additional (or one less) spike is the main cause of conversion errors. Based on this, we propose an optimization strategy based on shifting the initial membrane potential and we theoretically prove the corresponding optimal shifting distance for calibrating the spike. In addition, we also note that our method has a unique iterative property that enables further reduction of conversion errors. The experimental results show that our proposed method achieves state-of-the-art performance on CIFAR-10, CIFAR-100, and ImageNet datasets. For example, we reach a top-1 accuracy of 67.12% on ImageNet when using 6 time-steps. To the best of our knowledge, this is the first time an ANN-SNN conversion has been shown to simultaneously achieve high accuracy and ultralow latency on complex datasets. Code is available at https://github.com/hzc1208/ANN2SNN_COS.

翻译：脉冲神经网络（SNN）因其低功耗和时序信息处理的独特特性而备受关注。ANN-SNN转换作为SNN最常用的训练方法，能够确保转换后的SNN在大规模数据集上达到与ANN相当的性能。然而，在低时间步长下性能严重下降，这阻碍了SNN在神经形态芯片上的实际应用。本文中，我们并未评估不同的转换误差并消除这些误差，而是定义了一种偏移脉冲来度量实际SNN发放率与期望SNN发放率之间的偏离程度。我们对偏移脉冲进行了详细分析，并指出额外发放一个脉冲（或少发放一个脉冲）是导致转换误差的主要原因。基于此，我们提出了一种基于初始膜电位偏移的优化策略，并从理论上证明了用于校准脉冲的相应最优偏移距离。此外，我们还注意到我们的方法具有独特的迭代特性，能够进一步减少转换误差。实验结果表明，我们提出的方法在CIFAR-10、CIFAR-100和ImageNet数据集上均达到了最先进的性能。例如，在使用6个时间步长时，我们在ImageNet上达到了67.12%的Top-1准确率。据我们所知，这是首次证明ANN-SNN转换能够在复杂数据集上同时实现高精度和超低延迟。代码可在https://github.com/hzc1208/ANN2SNN_COS获取。