Towards Training-Free and Accurate ANN-to-SNN Conversion via Activation-Aware Redistribution

Conversion represents an effective approach for obtaining low-power models by transforming Artificial Neural Networks (ANNs) into event-driven Spiking Neural Networks (SNNs) without additional training. However, existing spiking neuron models for conversion introduce substantial conversion errors due to insufficient comparative analysis of ANN activation distributions and SNN spike rate ranges. Here, we first reveal that channel-wise activation distributions exhibit distinct offsets, while spike rates typically lack such offsets and are configured layer-wise, resulting in severe distributional mismatch. To address this limitation, we propose Adaptive Integrate-and-Fire (AIF) neurons with channel-specific characteristics that perceive channel-wise offsets of activation distributions and dynamically adjust spike rates, thereby minimizing conversion errors. Experimental results across multiple vision and natural language processing datasets demonstrate state-of-the-art performance, with a notable achievement of 85.52\% accuracy on ImageNet-1K. Furthermore, our approach requires negligible time complexity for the conversion process, offering substantial practical value for conversion applications.