@inproceedings{el-kurdi-etal-2022-zero,
    title = "Zero-Shot Dynamic Quantization for Transformer Inference",
    author = "El-kurdi, Yousef  and
      Quinn, Jerry  and
      Sil, Avi",
    editor = "Li, Yunyao  and
      Lazaridou, Angeliki",
    booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: Industry Track",
    month = dec,
    year = "2022",
    address = "Abu Dhabi, UAE",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2022.emnlp-industry.45/",
    doi = "10.18653/v1/2022.emnlp-industry.45",
    pages = "451--457",
    abstract = "We introduce a novel run-time method for significantly reducing the accuracy loss associated with quantizing BERT-like models to 8-bit integers. Existing methods for quantizing models either modify the training procedure, or they require an additional calibration step to adjust parameters that also requires a selected held-out dataset. Our method permits taking advantage of quantization without the need for these adjustments. We present results on several NLP tasks demonstrating the usefulness of this technique."
}