A Data-Free Analytical Quantization Scheme for Deep Learning Models
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866911143345258496 |
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| author | Luqman, Ahmed Qazi, Khuzemah Patterson, Murray Khan, Malik Jahan Khan, Imdadullah |
| author_facet | Luqman, Ahmed Qazi, Khuzemah Patterson, Murray Khan, Malik Jahan Khan, Imdadullah |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_07391 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Data-Free Analytical Quantization Scheme for Deep Learning Models Luqman, Ahmed Qazi, Khuzemah Patterson, Murray Khan, Malik Jahan Khan, Imdadullah Computer Vision and Pattern Recognition Machine Learning 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. |
| title | A Data-Free Analytical Quantization Scheme for Deep Learning Models |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2412.07391 |