# A Data-Free Analytical Quantization Scheme for Deep Learning Models Source: [A Data-Free Analytical Quantization Scheme for Deep Learning Models]() Collection index: [llms.txt]() Data updated: 2025-10-27T07:01:39.544Z Authors: Ahmed Luqman, Khuzemah Qazi, Murray Patterson, Malik Jahan Khan, Imdadullah Khan Published: 2024-12-10T10:33:58.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()