SAGE: Surrogate-gradient Adaptation via Attention-Guided Entropy for Spiking Transformers
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...
| Authors | Kiran Nair, Rodrigue Rizk, KC Santosh |
|---|---|
| Primary category | cs.LG |
| Published | 13 August 2026 |
| Last updated at source | 13 August 2026 |
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