@inproceedings{lin-etal-2023-mobilenmt,
    title = "{M}obile{NMT}: Enabling Translation in 15{MB} and 30ms",
    author = "Lin, Ye  and
      Wang, Xiaohui  and
      Zhang, Zhexi  and
      Wang, Mingxuan  and
      Xiao, Tong  and
      Zhu, Jingbo",
    editor = "Sitaram, Sunayana  and
      Beigman Klebanov, Beata  and
      Williams, Jason D",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-industry.36",
    doi = "10.18653/v1/2023.acl-industry.36",
    pages = "368--378",
    abstract = "Deploying NMT models on mobile devices is essential for privacy, low latency, and offline scenarios. For high model capacity, NMT models are rather large. Running these models on devices is challenging with limited storage, memory, computation, and power consumption. Existing work either only focuses on a single metric such as FLOPs or general engine which is not good at auto-regressive decoding. In this paper, we present MobileNMT, a system that can translate in 15MB and 30ms on devices. We propose a series of principles for model compression when combined with quantization. Further, we implement an engine that is friendly to INT8 and decoding. With the co-design of model and engine, compared with the existing system, we speed up 47.0x and save 99.5{\%} of memory with only 11.6{\%} loss of BLEU. Our code will be publicly available after the anonymity period.",
}

Deploying NMT models on mobile devices is essential for privacy, low latency, and offline scenarios. For high model capacity, NMT models are rather large. Running these models on devices is challenging with limited storage, memory, computation, and power consumption. Existing work either only focuses on a single metric such as FLOPs or general engine which is not good at auto-regressive decoding. In this paper, we present MobileNMT, a system that can translate in 15MB and 30ms on devices. We propose a series of principles for model compression when combined with quantization. Further, we implement an engine that is friendly to INT8 and decoding. With the co-design of model and engine, compared with the existing system, we speed up 47.0x and save 99.5{\%} of memory with only 11.6{\%} loss of BLEU. Our code will be publicly available after the anonymity period.
Lin, Ye Wang, Xiaohui Zhang, Zhexi Wang, Mingxuan Xiao, Tong Zhu, Jingbo
MobileNMT: Enabling Translation in 15MB and 30ms