EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices

Shaibal Saha; Lanyu Xu

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arXiv · 2506.11093

EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices

Shaibal Saha·Lanyu Xu

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Abstract

Hybrid models that combine convolutional and transformer blocks offer strong performance in computer vision (CV) tasks but are resource-intensive for edge deployment. Although post-training quantization (PTQ) can help reduce resource demand, its application to hybrid models remains limited. We propose EfficientQuant, a novel structure-aware PTQ approach that applies uniform quantization to convolutional blocks and $log_2$ quantization to transformer blocks. EfficientQuant achieves $2.5 \times - 8.7 \times$ latency reduction with minimal accuracy loss on the ImageNet-1K dataset. It further demonstrates low latency and memory efficiency on edge devices, making it practical for real-world deployment.

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BibTeX RIS

Shaibal Saha, Lanyu Xu. 2025-06-05. EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices. https://arxiv.org/abs/2506.11093

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