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Leveraging the Power of C++ for Efficient Machine Learning on Embedded Devices - Adrian Stanciu
Discover how C++ can be used to achieve efficient machine learning on embedded devices, leveraging the power of TensorFlow Lite and the Raspberry Pi for fast and accurate inference.
- C++ can be leveraged for efficient machine learning on embedded devices.
- Using C++ can provide significant improvements in memory consumption, compared to Python.
- The Raspberry Pi was used as a target device, and was connected to a power outlet and did not have battery life.
- TensorFlow Lite was used for inference on the embedded device.
- The model size has an impact on the running time, and increasing the complexity of the model can lead to slower performance.
- More diverse data can lead to better models.
- Running inference on an embedded device can be done efficiently and does not require a cloud-based connection.
- C++ provides both low-level access to hardware resources and higher-level abstraction, making it a suitable choice for embedded systems development.
- Machine learning code alone is not enough, and data also matters in achieving good accuracy.
- Efficient memory management is crucial in machine learning, especially on embedded devices.
- Optimizing for memory consumption can be done by reducing the precision of floating point numbers or using quantization techniques.
- A powerful embedded device like the Raspberry Pi can be used for machine learning applications with high accuracy.
- Python scripts can be used for training and testing, while C++ code can be used for actual deployment on the embedded device.