Alapfogalmak
GenQ introduces a novel approach using advanced Generative AI models to generate high-quality synthetic data for quantization, setting new benchmarks in low data regimes.
Kivonat
The content discusses the challenges of low-bit quantization in deep neural network deployment and introduces GenQ as a solution. It explains the methodology, experiments, and results of using synthetic data for quantization.
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Introduction:
- Model compression techniques like quantization, pruning, and knowledge distillation are discussed.
- Challenges of accessing original training data due to privacy concerns are highlighted.
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Data-Free Quantization:
- Various methods like ZeroQ, KW, MixMix, and Genie are compared with GenQ on CNNs.
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Data-Free QAT:
- Comparison of GenQ with GDFQ, ZAQ, Qimera, IntraQ, ARC+AIT, DSG, AdaDFQ, and TexQ on CNNs.
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Data-Scarce GenQ:
- Evaluation of GenQ performance under limited training data scenarios.
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Experiments:
- Visualization of synthetic data quality and comparison with existing methods.
- Latency comparison for data generation speed.
- Accuracy comparisons for PTQ and QAT scenarios on various architectures.
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Conclusion:
- Summary of the contributions and achievements of GenQ in synthetic data generation for quantization.
Statisztikák
Zero-shot netowrk quantization framework (TexQ) achieves 10 minutes latency.
Stable Diffusion v1-5 used in pipeline for generating high-quality synthetic data.
Idézetek
"GenQ establishes new benchmarks in data-free and data-scarce quantization."
"Our method excels in data-free quantization scenarios."