STARS: Spike Tail-Aware Relational Synthesis for ANN-to-SNN Data-Free Knowledge Distillation

SNNs offer a route to efficient inference, but their performance still trails that of ANNs. ANN-to-SNN knowledge distillation helps narrow this gap, yet the original training data are often unavailable. Existing data-free knowledge distillation (DFKD) methods synthesize surrogate data using teacher-side priors, especially BN statistics, whose first two moments do not determine even a neuron's pre-reset threshold-crossing behavior. We propose Spike Tail-Aware Relational Synthesis (STARS), an add-on for BN-guided ANN-to-SNN DFKD that augments synthesis with Relational Consistency Alignment, matching cross-sample teacher–student relations, and Tail-Aware Regularization, matching soft exceedance probabilities over teacher-derived feature thresholds. The original teacher priors remain active during synthesis. Experiments on CIFAR-10, CIFAR-100, and Tiny-ImageNet across multiple ANN–SNN pairs show consistent gains over three DFKD baselines, reaching absolute accuracy gains of 4.6% on CIFAR-10 and 6.7% on CIFAR-100. Paired multi-seed and resource measurements further characterize reliability and training cost in the evaluated settings.