@inproceedings{li-etal-2025-improving-efficiency,
    title = "Improving Efficiency in Large Language Models via Extendable Block Floating Point Representation",
    author = "Li, Dongyang  and
      Li, Zeyang  and
      Liu, Bosheng  and
      Wu, Jigang",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.findings-acl.768/",
    doi = "10.18653/v1/2025.findings-acl.768",
    pages = "14861--14873",
    ISBN = "979-8-89176-256-5",
    abstract = "Large language models (LLMs) have revolutionized natural language processing (NLP) tasks, yet their increasing size poses substantial challenges in terms of computational and memory resources. Block floating-point (BFP) arithmetic offers an effective solution by leveraging the strengths of both floating-point and fixed-point representations, leading to reductions in both storage and computational overhead. However, current low-bit BFP quantization approaches often struggle to handle extreme outliers, leading to significant accuracy degradation. To overcome this limitation, we introduce Extendable Exponent Sharing (EES), a novel BFP representation that extends the exponent bit width to capture a wider dynamic range. EES achieves this by embedding extendable exponent bits into the least significant mantissa bits, thereby increasing the shared exponent{'}s bit width without incurring additional storage costs. To optimize the trade-off between accuracy and energy efficiency, EES employs a design space exploration strategy to optimize the configuration of extendable exponent bit widths. Experimental results show that EES outperforms representative baselines in both accuracy and computational efficiency."
}