AQ4SViT: An Automated Quantization Framework with Search Gating Policy for Compressing Spiking Vision Transformers
Spiking Vision Transformers (SViTs) have emerged as alternative low-power ViT models, but their large sizes hinder their deployments on resource-constrained embedded AI systems. To address this, state-of-the-art works proposed quantization techniques to compress SViT models, but their manual, human-guided approach needs a huge design time and power/energy consumption to find the appropriate quantization setting for each given network, making this approach not scalable for quantizing multiple networks. Toward this, we propose AQ4SViT, a novel automated quantization framework for SViTs that can provide quick quantization settings with good trade-offs between accuracy and memory. To achieve ...
| Authors | Rachmad Vidya Wicaksana Putra, Saad Iftikhar, Muhammad Shafique |
|---|---|
| Primary category | cs.NE |
| Published | 14 June 2026 |
| Last updated at source | 14 June 2026 |
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