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:
this https URL

本文介绍了移动神经网络（Mobile Neural Network，MNN），它是一种适用于移动应用的普适且高效的推理引擎，解决了在移动设备上进行深度学习模型推理的模型兼容性、设备多样性和资源限制等挑战，采用了称为预推理的机制进行运行时优化，针对操作符进行了彻底的内核优化以实现最佳计算性能，并引入了后端抽象模块，实现了混合调度并保持引擎轻量级。广泛的基准实验表明，MNN 在性能上与其他流行的轻量级深度学习框架相当。