StageCal: Shared Neuron Calibration for ANN-to-SNN Conversion with Few-Step Inference

Converting pretrained artificial neural networks (ANNs) into spiking neural networks (SNNs) aims to reuse learned representations while addressing severe clipping, quantization, and temporal mismatch errors at short simulation steps. To mitigate these errors, existing calibration methods commonly reconstruct ANN activations through layer-wise parameter adjustment. Consequently, these methods fail to preserve network-wide feature alignment across heterogeneous stages and varying inference budgets. In this paper, we find that isolated parameter fitting inherently overlooks the complex interactions of temporal conversion errors. Effective calibration cannot treat neuron operating parameters as independent variables. Instead, it must govern them as a globally coordinated system anchored by shared network statistics. In light of this, we propose STAGECAL, a novel shared neuron calibration framework for plug-and-play ANN-to-SNN conversion that keeps pretrained ANN backbone weights strictly fixed. Our framework brings two key technical contributions. Firstly, we introduce a shared calibration network. It utilizes channel-wise activation statistics, normalized stage depth, and target spike budgets to jointly predict threshold adjustments and initial membrane potentials. On top, we then introduce a structured residual correction function. This function dynamically predicts mixing coefficients that combine three fixed residual bases from an initial pilot conversion into bounded stage-wise current corrections. These corrections are theoretically linked through an exact membrane-potential balance identity. Extensive experiments on classification tasks demonstrate that STAGECAL even exceeds the original ANN by 5.71% with only 0.17x energy and few simulation time steps on ImageNet. It also reduces hidden-layer synaptic operations by 4.24% relative to percentile-based conversion. This enables highly efficient SNN inference without running the source ANN and calibration network.