AutoML Mobile: Automated ML Model Design for Every Mobile Device

Designing accurate and efficient CNNs for mobile devices is challenging due to the large design space and expensive computational methods. Although many mobile CNNs are available for developers to train and deploy to mobile devices, existing CNN architecture may not be able to achieve the best results for some tasks on mobile devices. Last year, Google introduced an automated mobile neural architecture search (MNAS) approach, and proposed MnasNet based on reinforcement learning to automatically design mobile models. Facebook then proposed FBNet, a differentiable neural architecture search (DNAS) framework to optimize CNN architecture based on a gradient method. Both FBNet and MnasNet introduced automated solutions to change the way deep learning models are designed for mobile.