MNN: A Universal and Efficient Inference Engine
This addresses the problem of efficient deep learning inference for mobile developers, though it is incremental as it builds on existing lightweight frameworks.
The authors tackled the challenge of deploying deep learning models on mobile devices by proposing MNN, a universal and efficient inference engine, which outperforms other lightweight frameworks in benchmark experiments.
Deploying deep learning models on mobile devices draws more and more attention recently. However, designing an efficient inference engine on devices is under the great challenges of model compatibility, device diversity, and resource limitation. To deal with these challenges, we propose Mobile Neural Network (MNN), a universal and efficient inference engine tailored to mobile applications. In this paper, the contributions of MNN include: (1) presenting a mechanism called pre-inference that manages to conduct runtime optimization; (2)deliveringthorough kernel optimization on operators to achieve optimal computation performance; (3) introducing backend abstraction module which enables hybrid scheduling and keeps the engine lightweight. Extensive benchmark experiments demonstrate that MNN performs favorably against other popular lightweight deep learning frameworks. MNN is available to public at: https://github.com/alibaba/MNN.