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MNN is a highly efficient and lightweight deep learning framework. It supports inference and training of deep learning models, and has industry leading performance for inference and training on-device. At present, MNN has been integrated in more than 20 apps of Alibaba Inc, such as Taobao, Tmall, Youku, Dingtalk, Xianyu and etc., covering more than 70 usage scenarios such as live broadcast, short video capture, search recommendation, product searching by image, interactive marketing, equity distribution, security risk control. In addition, MNN is also used on embedded devices, such as IoT. MNN Workbench could be downloaded from MNN's homepage, which provides pretrained models, visualized training tools, and one-click deployment of models to devices. Android platform, core so size is about 400KB, OpenCL so is about 400KB, Vulkan so is about 400KB. Supports hybrid computing on multiple devices. Currently supports CPU and GPU.
Features
- Implements core computing with lots of optimized assembly code to make full use of the ARM CPU
- For iOS, GPU acceleration (Metal) can be turned on, which is faster than Apple's native CoreML
- For Android, OpenCL, Vulkan, and OpenGL are available and deep tuned for mainstream GPUs (Adreno and Mali)
- Convolution and transposition convolution algorithms are efficient and stable. The Winograd convolution algorithm is widely used to better symmetric convolutions such as 3x3 -> 7x7
- Twice speed increase for the new architecture ARM v8.2 with FP16 half-precision calculation support
- Optimized for devices, no dependencies, can be easily deployed to mobile devices and a variety of embedded devices
Project Samples
