AutoTinyML for microcontrollers: Dealing with black-box deployability

作者：

Highlights：

• Constrained Bayesian Optimization includes deployment constraints for an HPO problem

• Deep Neural Networks deployable on MCUs is a massive untapped market for TinyML

• Finding out most accurate DNN taking into account deployment constraints.

摘要

•Constrained Bayesian Optimization includes deployment constraints for an HPO problem•Deep Neural Networks deployable on MCUs is a massive untapped market for TinyML•Finding out most accurate DNN taking into account deployment constraints.

论文关键词：Tiny Machine Learning,Deep Neural Networks,Automated Machine Learning,Neural Architecture Search,Hyperparameter optimization,Bayesian Optimization

论文评审过程：Received 28 January 2021, Revised 5 April 2022, Accepted 13 June 2022, Available online 22 June 2022, Version of Record 28 June 2022.

论文官网地址：https://doi.org/10.1016/j.eswa.2022.117876