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SpikingMoE: SDPrompt-Guided Dynamic Expert Fusion in Spiking Neural Networks
Yukai Yang, · 2026-05-25 · via cs updates on arXiv.org

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Abstract:Spiking Neural Networks (SNNs) provide an energy-efficient paradigm for visual recognition. We present SpikingMoE, which integrates a spike-driven Transformer with a Mixture-of-Experts (MoE) framework for dynamic computation. Inspired by the lateral geniculate nucleus (LGN), a spike-driven prompt (SDprompt) enables input-dependent expert routing in a biologically plausible manner. By replacing standard MLPs with spike-compatible expert modules and enforcing binary spike communication, SpikingMoE is designed for neuromorphic hardware. Experiments on CIFAR-10 and CIFAR-100 achieve 94.09% and 74.54% top-1 accuracy, showing that modular expert routing can be incorporated while retaining reasonable performance. To our knowledge, SpikingMoE is the first open-source SNN framework that integrates MoE into a spike-driven Transformer with LGN-inspired routing.
Subjects: Neural and Evolutionary Computing (cs.NE)
Cite as: arXiv:2605.23188 [cs.NE]
  (or arXiv:2605.23188v1 [cs.NE] for this version)
  https://doi.org/10.48550/arXiv.2605.23188

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Yukai Yang [view email]
[v1] Fri, 22 May 2026 03:14:15 UTC (1,198 KB)