arXiv:2510.04547v5 Announce Type: replace Abstract: Large pretrained vision encoders are central to multimodal intelligence, powering applications from on-device vision processing to vision-language models. Since these applications often demand real-time processing of massive visual data, reducing the inference cost of vision encoders is critical. Quantization offers a practical path, but it remains challenging even at 8-bit precision due to so-called outliers. In this work, we propose $\textit{RegCache}$, a training-free algorithm that mitigates outliers in large-scale pretrained vision encod
The proliferation of large vision models and the demand for their real-time, on-device deployment necessitate continuous advancements in computational efficiency.
This development addresses a key bottleneck in deploying powerful AI models, making advanced vision capabilities more accessible and energy-efficient for a wider range of applications.
The proposed 'RegCache' algorithm offers a training-free path to mitigate quantization outliers, potentially simplifying and accelerating the deployment of vision encoders with higher precision.
- · AI hardware manufacturers
- · On-device AI application developers
- · Cloud providers with AI inference services
- · Companies deploying autonomous systems
- · Developers reliant on high-precision, unquantized models
- · Traditional high-power inference solutions
More efficient and widespread deployment of large vision models across various devices and platforms.
Increased adoption of AI in edge computing and real-time vision applications due to reduced computational overhead.
Potential for new multimodal AI applications that were previously too computationally expensive for practical deployment.
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Read at arXiv cs.LG