Not All Timesteps Matter Equally: Selective Alignment Knowledge Distillation for Spiking Neural Networks

2026-05-13

AI Summary

This work addresses a key limitation in existing knowledge distillation methods for spiking neural networks (SNNs), which uniformly align teacher and student outputs across all time steps, disregarding variations in prediction quality over time. To overcome this, the authors propose Selective Alignment Knowledge Distillation (SeAl-KD), which introduces, for the first time, an error-aware and confidence-driven mechanism to selectively align only those time steps exhibiting low confidence or high prediction error. During these critical intervals, competitive logits are dynamically corrected, while cross-time-step similarity-based reweighting preserves informative temporal dynamics. By departing from the conventional assumption of uniform distillation, SeAl-KD achieves substantial performance gains over prior approaches on both static image and neuromorphic event datasets, effectively narrowing the accuracy gap between SNNs and artificial neural networks (ANNs).

Abstract

Spiking neural networks (SNNs), which are brain-inspired and spike-driven, achieve high energy efficiency. However, a performance gap between SNNs and artificial neural networks (ANNs) still remains. Knowledge distillation (KD) is commonly adopted to improve SNN performance, but existing methods typically enforce uniform alignment across all timesteps, either from a teacher network or through inter-temporal self-distillation, implicitly assuming that per-timestep predictions should be treated equally. In practice, SNN predictions vary and evolve over time, and intermediate timesteps need not all be individually correct even when the final aggregated output is correct. Under such conditions, effective distillation should not force every timestep toward the same supervision target, but instead provide corrective guidance to erroneous timesteps while preserving useful temporal dynamics. To address this issue, we propose Selective Alignment Knowledge Distillation (SeAl-KD), which selectively aligns class-level and temporal knowledge by equalizing competing logits at erroneous timesteps and reweighting temporal alignment based on confidence and inter-timestep similarity. Extensive experiments on static image and neuromorphic event-based datasets demonstrate consistent improvements over existing distillation methods. The code is available at https://github.com/KaiSUN1/SeAl

Problem

• Spiking Neural Networks
• Knowledge Distillation
• Temporal Alignment
• Selective Alignment
• Timestep-wise Prediction

Innovation

• Selective Alignment
• Knowledge Distillation
• Spiking Neural Networks
• Temporal Dynamics
• Confidence-based Reweighting