When tuning the architecture and hyperparameters of large machine learning models for on-device deployment, it is desirable to understand the optimal trade-offs between on-device latency and model accuracy. In this work, we leverage recent methodological advances in Bayesian optimization over high-dimensional search spaces and multi-objective Bayesian optimization to efficiently explore these trade-offs for a production-scale on-device natural language understanding model at Facebook.
@article{arxiv.2106.11890,
title = {Latency-Aware Neural Architecture Search with Multi-Objective Bayesian Optimization},
author = {David Eriksson and Pierce I-Jen Chuang and Samuel Daulton and Peng Xia and Akshat Shrivastava and Arun Babu and Shicong Zhao and Ahmed Aly and Ganesh Venkatesh and Maximilian Balandat},
journal= {arXiv preprint arXiv:2106.11890},
year = {2021}
}
Comments
To Appear at the 8th ICML Workshop on Automated Machine Learning, ICML 2021