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Duration 21 hours
Course Outline
Introduction to TinyML
- Exploring the constraints and potential of TinyML
- Overview of prevalent microcontroller platforms
- Comparative analysis of Raspberry Pi, Arduino, and alternative boards
Hardware Preparation and Setup
- Preparing the Raspberry Pi OS environment
- Configuring Arduino boards for operation
- Integrating sensors and external peripherals
Data Acquisition Methods
- Capturing raw sensor data
- Processing audio, motion, and environmental inputs
- Constructing annotated datasets
Model Design for Edge Devices
- Choosing appropriate model architectures
- Training TinyML models using TensorFlow Lite
- Assessing performance for embedded scenarios
Model Optimization and Conversion
- Applying quantization techniques
- Converting models for microcontroller implementation
- Optimizing memory usage and computational load
Implementation on Raspberry Pi
- Executing TensorFlow Lite inference
- Integrating model outputs into functional applications
- Diagnosing and resolving performance bottlenecks
Implementation on Arduino
- Leveraging the Arduino TensorFlow Lite Micro library
- Flashing models onto microcontrollers
- Validating accuracy and runtime behavior
Developing Complete TinyML Solutions
- Architecting comprehensive embedded AI workflows
- Building interactive, real-world prototypes
- Testing and iterating on project functionality
Conclusion and Future Directions
Requirements
- A foundational understanding of core programming concepts
- Practical experience with microcontroller applications
- Proficiency in Python or C/C++
Target Audience
- Makers
- Hobbyists
- Embedded AI developers
