PSViT: A Methodology for Structurally Pruning Spiking Vision Transformers
Spiking Vision Transformer (SViT) models are promising low-power ViT models for solving vision-based tasks with state-of-the-art performance. However, their large sizes limit their deployments for resource-constrained embedded platforms, underscoring the needs of model compression. One of prominent compression techniques is pruning, and the state-of-the-art works employ unstructured pruning techniques to compress SViT models. Such techniques require specialized hardware architectures tailored for the sparsity patterns to maximize their efficiency benefits, making this approach not scalable. To address this, we propose PSViT, a novel methodology to perform structured pruning on SViT models...
| Authors | Rachmad Vidya Wicaksana Putra, Achyuta Muthuvelan, Alberto Marchisio et al. |
|---|---|
| Primary category | cs.NE |
| Published | 2 June 2026 |
| Last updated at source | 2 June 2026 |
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