  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.
