In recent years, artificial intelligence technology has developed rapidly, but its powerful computing capabilities often rely on cloud servers. This poses a significant challenge for resource-constrained embedded devices. However, the rise of TinyML (Tiny Machine Learning) technology brings new hope: enabling resource-limited microcontrollers to run deep learning models! This article will take you into the world of TinyML, exploring how to implement deep learning on Arduino and kickstart your AI embedded journey!
1. TinyML: Making AI Accessible
TinyML is not an esoteric technology; it simply refers to deploying machine learning models on micro devices (such as Arduino, microcontrollers, etc.). Compared to traditional AI solutions that rely on cloud servers, TinyML has the following significant advantages:
- Low Power Consumption: No need to connect to the network and cloud servers, significantly reducing power consumption and extending battery life, especially suitable for battery-powered IoT devices.
- Low Latency: Local data processing leads to faster response times and stronger real-time capabilities, making it suitable for applications with high timeliness requirements.
- Data Privacy Protection: Data does not need to be uploaded to the cloud, effectively ensuring user data security and privacy.
- Cost-Effective: Reduces dependence on servers and network bandwidth, thereby lowering overall costs.
These advantages have led to the application of TinyML in an increasing number of fields, such as:
- Smart Home: Smart speakers, smart lighting, smart locks, etc.
- Wearable Devices: Smartwatches, fitness trackers, etc.
- Industrial Automation: Fault prediction, quality monitoring, etc.
- Environmental Monitoring: Air quality monitoring, water quality monitoring, etc.
2. TinyML on Arduino: The DeepC Framework
DeepC is an open-source deep learning inference framework designed specifically for embedded devices, and it perfectly adapts to Arduino. DeepC allows you to run lightweight deep learning models on Arduino and has the following outstanding features:
- Compact and Efficient: The binary files generated by DeepC are small in size, occupy little memory, and have high performance. This means it can run on extremely resource-limited Arduinos.
- Low Latency Inference: DeepC optimizes the inference process, enabling low-latency model predictions to meet the needs of real-time applications.
- Easy to Use: Although it involves deep learning, DeepC’s API design is relatively user-friendly, allowing developers without a deep learning background to quickly get started.
3. Configuring the DeepC Environment on Arduino
To successfully run DeepC on Arduino, we need to make some necessary configurations. The following steps will guide you through the environment setup:
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Install Arduino IDE: First, you need to download and install the latest Arduino IDE.
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Install Necessary Libraries: DeepC depends on several library files, and you need to install these libraries according to the DeepC documentation.
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Configure Compiler Options: This is a critical step for DeepC to run on Arduino. You need to modify the
<span>platform.txt</span>file in the Arduino IDE installation directory, adding the<span>-fexceptions</span>compiler option and removing the<span>-fno-exceptions</span>option. This usually requires modifying the following two files:Please note that the file paths may vary depending on the Arduino IDE version and installation path. Be sure to back up the original files to avoid errors! After modification, restart the Arduino IDE.
<span>${ARDUINO_INSTALLATION}/.arduino15/packages/arduino/hardware/mbed/1.1.4/variants/ARDUINO_NANO33BLE/cxxflags.txt</span><span>${ARDUINO_INSTALLATION}/arduino-1.8.12/hardware/arduino/avr/platform.txt</span>
4. Example Applications and Advanced Learning
DeepC supports various types of deep learning models, and you can choose the appropriate model based on your application scenario. The DeepC GitHub repository provides many example codes to help you get started quickly. Additionally, you can learn about other TinyML frameworks like TensorFlow Lite Micro to further expand your skills.
5. Conclusion
TinyML brings deep learning capabilities to embedded devices like Arduino, empowering them with greater intelligence. With the DeepC framework, we can easily deploy deep learning models on Arduino, achieving various exciting applications. Although the configuration process may require some patience and skill, the results will be worth it. Join the ranks of TinyML now and start your embedded AI journey!
Project Address: https://github.com/ai-techsystems/arduino