SpikingVLA: Asynchronous Spiking Vision-Language-Action Models
ANN-to-SNN conversion offers a practical route toward energy-efficient spiking Vision-Language-Action (VLA) models by bypassing the substantial cost of training large-scale SNNs from scratch. However, existing methods often require many timesteps to maintain competitive performance, resulting in substantial inference latency for real-time VLA deployment. To address this challenge, we introduce SpikingVLA, an ANN-to-SNN conversion framework that enables accurate and low-latency spiking VLA inference. Specifically, we propose a Dendritic Integrate-and-Fire (DIF) neuron that alleviates channel-wise activation outliers through dendritic mixing and adaptive somatic firing, enabling accurate AN...
| Authors | Jingya Wang, Dehao Zhang, Shuai Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 7 October 2026 |
| Last updated at source | 7 October 2026 |
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