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arXiv · 2412.07391

A Data-Free Analytical Quantization Scheme for Deep Learning Models

Ahmed Luqman·Khuzemah Qazi·Murray Patterson·Malik Jahan Khan·Imdadullah Khan
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Abstract

Despite the success of CNN models on a variety of Image classification and segmentation tasks, their extensive computational and storage demands pose considerable challenges for real-world deployment on resource-constrained devices. Quantization is one technique that aims to alleviate these large storage requirements and speed up the inference process by reducing the precision of model parameters to lower-bit representations. In this paper, we introduce a novel post-training quantization method for model weights. Our method finds optimal clipping thresholds and scaling factors along with mathematical guarantees that our method minimizes quantization noise. Empirical results on real-world datasets demonstrate that our quantization scheme significantly reduces model size and computational requirements while preserving model accuracy.

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Ahmed Luqman, Khuzemah Qazi, Murray Patterson, Malik Jahan Khan, Imdadullah Khan. 2024-12-10. A Data-Free Analytical Quantization Scheme for Deep Learning Models. https://arxiv.org/abs/2412.07391

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