aiqbench analysis
This article focuses on GyRot, a novel algorithm-hardware co-design framework that successfully integrates rotation and fine-grained group quantization to optimize low-bit LLM inference. By resolving the inherent mismatch between global rotational operations and localized scaling factors, the method achieves state-of-the-art 4-bit accuracy with significant speedups and energy efficiency gains across major models like LLaMA. The work represents a critical advancement in making large language model deployment more scalable through precise hardware-aware quantization techniques.
GyRot: Leveraging Hidden Synergy between Rotation and Fine-grained Group Quantization for Low-bit LLM Inference
arXiv:2607.27694v1 Announce Type: cross Abstract: Low-bit quantization is essential for efficient LLM inference, and both rotation and fine-grained group quantization have shown individual promise. However, their combination often leads to accuracy degradation or hardware overhead due to a mismatch between the global nature of rotation and the localized behavior of group scaling. We propose GyRot, a quantization framework and hardware accelerator that bridges this gap through algorithm-hardware co-design. GyRot introduces Coarse Rotation, Fine Grouping (CoRFiG) and Harmonic-Aligned Permutation (HAP) to enable cooperative integration of rotation and group quantization, enhancing quantizability while relaxing scaling factor precision. To further reduce hardware cost, we reformulate asymmetric quantization and introduce a zero-point rounding strategy that enables fully integer dequantization. Implemented on an INT4-based tensor PE architecture, GyRot achieves state-of-the-art 4-bit accuracy across LLaMA-family models, while delivering up to 3.4x speedup and 3.6x energy efficiency over baseline LLM accelerators. These results validate GyRot's practical effectiveness for scalable and energy-efficient LLM deployment.
Abstract:Low-bit quantization is essential for efficient LLM inference, and both rotation and fine-grained group quantization have shown individual promise. However, their combination often leads to accuracy degradation or hardware overhead due to a mismatch between the global nature of rotation and the localized behavior of group scaling. We propose GyRot, a quantization framework and hardware accelerator that bridges this gap through algorithm-hardware co-design. GyRot introduces Coarse Rotation, Fine Grouping (CoRFiG) and Harmonic-Aligned Permutation (HAP) to enable cooperative integration of rotation and group quantization, enhancing quantizability while relaxing scaling factor precision. To further reduce hardware cost, we reformulate asymmetric quantization and introduce a zero-point rounding strategy that enables fully integer dequantization. Implemented on an INT4-based tensor PE architecture, GyRot achieves state-of-the-art 4-bit accuracy across LLaMA-family models, while delivering up to 3.4x speedup and 3.6x energy efficiency over baseline LLM accelerators. These results validate GyRot's practical effectiveness for scalable and energy-efficient LLM deployment.
15 pages, 12 figures. Published in 2026 IEEE International Symposium on High-Performance Computer Architecture (HPCA), Sydney, Australia, pp. 1-15, DOI: https://doi.org/10.1109/HPCA68181.2026.11408453
Hardware Architecture (cs.AR); Machine Learning (cs.LG)
arXiv:2607.27694 [cs.AR]
(or arXiv:2607.27694v1 [cs.AR] for this version)
https://doi.org/10.48550/arXiv.2607.27694 arXiv-issued DOI via DataCite (pending registration)
Proc. 2026 IEEE Int. Symp. High-Performance Computer Architecture (HPCA), 2026, pp. 1-15
https://doi.org/10.1109/HPCA68181.2026.11408453 DOI(s) linking to related resources
Full text under the publisher's open licence · 211 words
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