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

• URL: http://arxiv.org/abs/2506.11093v1
• Date: Thu, 05 Jun 2025 21:46:41 GMT
• Title: EfficientQuant: An Efficient Post-Training Quantization for CNN-Transformer Hybrid Models on Edge Devices
• Authors: Shaibal Saha, Lanyu Xu,
• Abstract summary: Hybrid models that combine convolutional and transformer blocks offer strong performance in computer vision (CV) tasks but are resource-intensive for edge deployment.<n>We propose EfficientQuant, a novel structure-aware PTQ approach that applies uniform quantization to convolutional blocks and $log$ quantization to transformer blocks.<n> EfficientQuant achieves $2.5 times - 8.7 times latency reduction with minimal accuracy loss on the ImageNet-1K dataset.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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