Sensitivity-Aware Post-Training Quantization for Deep Neural Networks

• URL: http://arxiv.org/abs/2509.05576v1
• Date: Sat, 06 Sep 2025 03:26:57 GMT
• Title: Sensitivity-Aware Post-Training Quantization for Deep Neural Networks
• Authors: Zekang Zheng, Haokun Li, Yaofo Chen, Mingkui Tan, Qing Du,
• Abstract summary: Existing post-training quantization methods employ iterative parameter updates to preserve accuracy under high compression ratios.<n>This paper proposes an efficient PTQ method guided by parameter sensitivity analysis.<n> Experimental results on ResNet-50 and YOLOv5s demonstrate a 20-200-fold quantization speedup over the Optimal Brain Quantization baseline.
• Score: 33.64653796994035
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Model quantization reduces neural network parameter precision to achieve compression, but often compromises accuracy. Existing post-training quantization (PTQ) methods employ iterative parameter updates to preserve accuracy under high compression ratios, incurring significant computational complexity and resource overhead, which limits applicability in resource-constrained edge computing and real-time inference scenarios. This paper proposes an efficient PTQ method guided by parameter sensitivity analysis. The approach prioritizes quantization of high-sensitivity parameters, leveraging unquantized low-sensitivity parameters to compensate for quantization errors, thereby mitigating accuracy degradation. Furthermore, by exploiting column-wise clustering of parameter sensitivity, the method introduces a row-parallel quantization framework with a globally shared inverse Hessian matrix update mechanism, reducing computational complexity by an order of magnitude. Experimental results on ResNet-50 and YOLOv5s demonstrate a 20-200-fold quantization speedup over the Optimal Brain Quantization baseline, with mean accuracy loss below 0.3%, confirming the method's efficacy in balancing efficiency and accuracy.

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