mit-han-lab/tinyengine
[NeurIPS 2020] MCUNet: Tiny Deep Learning on IoT Devices; [NeurIPS 2021] MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning; [NeurIPS 2022] MCUNetV3: On-Device Training Under 256KB Memory
This tool helps embed sophisticated machine learning capabilities directly onto tiny, low-power microcontroller devices, enabling them to process data and make decisions independently. It takes a trained neural network model and optimizes it to run efficiently on hardware with very limited memory. The primary users are engineers and product developers creating smart IoT devices like wearable sensors, smart appliances, or industrial monitors.
928 stars. No commits in the last 6 months.
Use this if you need to run deep learning models on resource-constrained microcontrollers for applications like object detection, voice recognition, or predictive maintenance, without needing a constant cloud connection.
Not ideal if your application runs on devices with ample memory and processing power, such as smartphones, PCs, or cloud servers, where larger models and more conventional frameworks are suitable.
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C
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MIT
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Last pushed
Nov 27, 2024
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