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Course Outline
Introduction to TinyML
- Defining TinyML.
- The importance of machine learning on microcontrollers.
- Comparing traditional AI with TinyML.
- Overview of necessary hardware and software requirements.
Setting Up the TinyML Environment
- Installing Arduino IDE and configuring the development environment.
- Introduction to TensorFlow Lite and Edge Impulse.
- Flashing and configuring microcontrollers for TinyML applications.
Building and Deploying TinyML Models
- Understanding the TinyML workflow.
- Training a simple machine learning model tailored for microcontrollers.
- Converting AI models to TensorFlow Lite format.
- Deploying models onto hardware devices.
Optimizing TinyML for Edge Devices
- Reducing memory usage and computational footprint.
- Techniques for quantization and model compression.
- Benchmarking TinyML model performance.
TinyML Applications and Use Cases
- Gesture recognition using accelerometer data.
- Audio classification and keyword spotting.
- Anomaly detection for predictive maintenance.
TinyML Challenges and Future Trends
- Hardware limitations and optimization strategies.
- Security and privacy concerns in TinyML.
- Future advancements and research in TinyML.
Summary and Next Steps
Requirements
- Basic programming proficiency (in Python or C/C++)
- Familiarity with machine learning concepts (recommended but not mandatory)
- Understanding of embedded systems (optional but beneficial)
Target Audience
- Engineers
- Data scientists
- AI enthusiasts
14 Hours
