[2005.03842] GOBO: Quantizing Attention-Based NLP Models for Low Latency and Energy Efficient Inference

arxiv.org · 763 words · saved by 1 readers

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. arXiv Operational Status Get status notifications via email or slack

[2005.03842] GOBO: Quantizing Attention-Based NLP Models for Low Latency and Energy Efficient Inference Skip to main content arXiv is now an independent nonprofit! Learn more × Search arXiv Press Enter to search · Advanced search --> Computer Science > Machine Learning arXiv:2005.03842 (cs) [Submitted on 8 May 2020 ( v1 ), last revised 27 Sep 2020 (this version, v2)] Title: GOBO: Quantizing Attention-Based NLP Models for Low Latency and Energy Efficient Inference Authors: Ali Hadi Zadeh , Isak Edo , Omar Mohamed Awad , Andreas Moshovos View a PDF of the paper titled GOBO: Quantizi

[2005.03842] GOBO: Quantizing Attention-Based NLP Models for Low Latency and Energy Efficient Inference

related reading