SAGE: Surrogate-gradient Adaptation via Attention-Guided Entropy for Spiking Transformers

• URL: http://arxiv.org/abs/2608.13702v1
• Date: Thu, 13 Aug 2026 18:51:04 GMT
• Title: SAGE: Surrogate-gradient Adaptation via Attention-Guided Entropy for Spiking Transformers
• Abstract summary: Spiking neural networks (SNNs) offer an energy-efficient alternative to conventional deep neural networks by exploiting sparse event-driven computation.<n>We introduce SAGE, an uncertainty-modulated surrogate-gradient mechanism for Transformer-based SNNs.
• Score: 2.7946918847372277
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Spiking neural networks (SNNs) offer an energy-efficient alternative to conventional deep neural networks by exploiting sparse event-driven computation, but their training remains challenging because the non-differentiable spike function requires surrogate gradients whose fixed shape may be suboptimal across layers and training stages. In this work, we introduce SAGE, an uncertainty-modulated surrogate-gradient mechanism for Transformer-based SNNs. SAGE estimates block-level uncertainty from normalized self-attention entropy and uses this signal to adapt the surrogate-gradient slope during training while leaving the inference model unchanged. By modulating only the training-time surrogate parameter, the proposed method preserves the original architecture and deployment cost while improving optimization flexibility. Experiments on CIFAR-10/100 demonstrate that SAGE achieves improved accuracy over fixed-surrogate baselines, with results up to 1-2\% consistent gains across multiple simulation time steps. These results highlight the potential of attention-derived uncertainty as a lightweight training signal for adaptive surrogate-gradient learning in transformer-based SNNs.

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