With its lightweight characteristics, MNN has three main application directions in the IoT field:
Typical Application Scenarios::
- smart home: running face detection on the camera side (5ms single frame processing), voice wakeup for smart speakers
- Industrial Inspection: Real-time defect recognition on production lines, supporting embedded chips such as Rockchip
- edge computing: Localized processing of sensor data to reduce cloud transfers
Performance Optimization Solutions::
- Memory optimization: using memory pooling technology, the same model reduces memory usage by 40% compared to PyTorch Mobile
- Compute Acceleration: Optimize ARM CPU performance with NEON instruction set, Vulkan backend to boost GPU utilization
- Power consumption control: Dynamically adjust the calculation precision (FP32→FP16) to extend the device endurance
real time data: Running ResNet18 on a Raspberry Pi 4B:
- FP32 accuracy: 38ms/frame
- Int8 quantized: 22ms/frame (42% performance gain)
The framework is specially optimized for embedded Linux, and the minimum runtime memory requirement can be controlled within 10MB.
This answer comes from the articleMNN: A Lightweight and Efficient Deep Learning Inference FrameworkThe
May not be reproduced without permission:AI productivity tools " What are the typical application scenarios of MNN on IoT devices? What is the performance?
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