AQ4SViT: An Automated Quantization Framework with Search Gating Policy for Compressing Spiking Vision Transformers
arXiv:2606.15523v1 Announce Type: cross Abstract: 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 au
The proliferation of AI models, particularly advanced vision transformers, is creating an urgent need for efficient deployment on resource-constrained edge devices, driving innovation in quantization techniques.
Efficient compression techniques like AQ4SViT are critical for enabling widespread adoption of sophisticated AI in edge computing, reducing computational burden and energy consumption, which directly impacts scalability and accessibility.
The development of automated quantization frameworks will significantly reduce the manual effort and expertise required to deploy complex AI models on embedded systems, making advanced AI more pervasive and less hardware-intensive.
- · Edge AI device manufacturers
- · Embedded AI developers
- · AI hardware accelerators
- · Energy-efficient computing initiatives
- · Manual quantization specialists
- · General-purpose, high-power compute platforms
Automated quantization drastically lowers the barrier to entry for deploying complex AI on low-power devices.
This efficiency gain accelerates the integration of advanced AI into consumer electronics, IoT, and industrial automation where power and size are critical constraints.
Widespread edge AI deployment shifts data processing away from centralized cloud infrastructure, potentially altering data governance and privacy landscapes.
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Read at arXiv cs.AI