Spike-SFT: Selective Parameter Enhancement and Fusion for Efficient Spiking Neural Networks

Spiking Neural Networks (SNNs) promise energy-efficient inference, but adapting large pre-trained SNNs to new tasks is expensive because BPTT memory and runtime scale with the number of simulation steps $T$. We observe that weights in pre-trained SNNs are strongly concentrated near zero, and under thresholded dynamics many such synapses are functionally silent. Based on this, we propose \textbf{Spike-SFT}, a two-stage adaptation framework. Stage 1 (\textbf{Selective Parameter Enhancement}, SPE) fine-tunes only a small-magnitude subset via masked updates with low-rank regularization, progressive re-selection, and sparse-gradient storage, reducing memory while maintaining competitive accuracy. Stage 2 (\textbf{PickIt}) fuses multiple SPE-adapted models by alignment and interference-aware delta merging with spike-statistics calibration, yielding a single merged model with zero inference-time overhead. Across several SNN backbones and benchmarks, Spike-SFT offers a favorable accuracy--cost trade-off, improving fine-tuning efficiency (time/memory) while retaining strong accuracy, and enabling zero-overhead weight-space fusion via PickIt.