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[2505.11695] Qronos: Correcting the Past by Shaping the Future... in Post-Training Quantization
Summary
The paper introduces Qronos, a novel post-training quantization algorithm that enhances neural network performance by correcting quantization errors through an iterative optimization framework.
Why It Matters
As machine learning models grow in complexity, efficient quantization methods like Qronos are crucial for deploying these models on resource-constrained devices. This research advances the state-of-the-art in post-training quantization, potentially improving model efficiency and accuracy in real-world applications.
Key Takeaways
- Qronos corrects quantization errors in neural networks effectively.
- The algorithm utilizes a disciplined optimization framework for better performance.
- It outperforms existing state-of-the-art adaptive rounding methods.
- Qronos is compatible with various transformation techniques.
- Efficient implementation is achieved using Cholesky decomposition.
Computer Science > Machine Learning arXiv:2505.11695 (cs) [Submitted on 16 May 2025 (v1), last revised 17 Feb 2026 (this version, v3)] Title:Qronos: Correcting the Past by Shaping the Future... in Post-Training Quantization Authors:Shihao Zhang, Haoyu Zhang, Ian Colbert, Rayan Saab View a PDF of the paper titled Qronos: Correcting the Past by Shaping the Future... in Post-Training Quantization, by Shihao Zhang and 3 other authors View PDF HTML (experimental) Abstract:We introduce Qronos -- a new state-of-the-art post-training quantization algorithm that sequentially rounds and updates neural network weights. Qronos not only explicitly corrects errors due to both weight and activation quantization, but also errors resulting from quantizing previous layers. Our iterative algorithm is based on an interpretable and disciplined optimization framework that subsumes and surpasses existing data-driven approaches. At each step, Qronos alternates between error correction and diffusion via optimal update rules. Importantly, we prove that Qronos admits an efficient implementation that uses the Cholesky decomposition for solving least-squares problems. We also demonstrate that Qronos is compatible with existing transformation techniques such as Hadamard-based incoherence processing and weight-activation scaling equalization, among others. We evaluate Qronos using recent autoregressive language generation models in the Llama3 family; Qronos consistently outperforms previous state-of-the...
