Quantization Techniques for Efficient Machine Learning Models

The recent advancements in quantization techniques for various machine learning models, particularly in the context of resource-constrained environments, have shown significant progress. Researchers are focusing on developing methods that not only reduce computational and storage costs but also maintain or even enhance model performance. Key innovations include the introduction of adaptive and mixed-precision quantization strategies, which tailor the bit-width allocation to specific model components or data characteristics, thereby mitigating the performance degradation often associated with low-bit quantization. Additionally, novel approaches like perturbation error mitigation and progressive fine-to-coarse reconstruction are being employed to address the challenges posed by dynamic data streams and the unique architecture of models such as Vision Transformers and Diffusion Models. These developments indicate a shift towards more robust and efficient quantization techniques that can adapt to the evolving needs of real-world applications.

Noteworthy papers include: 1) TTAQ for its innovative approach to stable quantization in dynamic test domains, and 2) ResQ for its advanced mixed-precision quantization method for large language models.

Sources

TTAQ: Towards Stable Post-training Quantization in Continuous Domain Adaptation

MPQ-DM: Mixed Precision Quantization for Extremely Low Bit Diffusion Models

ResQ: Mixed-Precision Quantization of Large Language Models with Low-Rank Residuals

MixLLM: LLM Quantization with Global Mixed-precision between Output-features and Highly-efficient System Design

Qua$^2$SeDiMo: Quantifiable Quantization Sensitivity of Diffusion Models

Progressive Fine-to-Coarse Reconstruction for Accurate Low-Bit Post-Training Quantization in Vision Transformers

Preventing Local Pitfalls in Vector Quantization via Optimal Transport

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