Elastic Prediction from the First Time-Step: Spiking Neural Networks with Temporal Membrane Biasing

Spiking neural networks (SNNs) naturally support elastic inference, where salient inputs trigger early predictions, and additional time-steps progressively refine the output, enabling fast responses and improved accuracy without repeated inference or model retraining. However, existing SNN methods are not well-suited for elastic inference: ANN-to-SNN conversion methods suffer from poor early-time-step accuracy, whereas directly trained SNNs perform well at specific time-steps but often degrade when inference continues, limiting the accuracy refinement. In this work, we identify the root cause of the early-stage performance degradation as a *membrane-threshold mismatch* between membrane potentials and firing thresholds. To mitigate this mismatch, we propose a temporal membrane biasing (TMB) method that applies sustained calibration across time-steps while preserving Quantized Artificial Neural Network (QANN) equivalence. In addition, we develop a bias-only fine-tuning strategy that enables efficient optimization with minimal training cost. Across multiple tasks, our work markedly improves low time-step accuracy (*e.g.,* 70.15% top-1 accuracy improvement at the first time-step on ImageNet). Combining elastic inference and adaptive methods, TMB achieves 24.3% operation and 24.8% time-step reduction with $<0.1$% accuracy degradation compared to the same SNN without TMB.