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Duration 21 hours
Course Outline
Introduction to TinyML
- Examining the limitations and potential of TinyML
- An overview of prevalent microcontroller ecosystems
- Evaluating Raspberry Pi against Arduino and alternative boards
Hardware Preparation and Setup
- Setting up the Raspberry Pi operating system
- Initial configuration of Arduino boards
- Linking sensors and auxiliary peripherals
Data Acquisition Methods
- Recording sensor readings
- Processing audio, motion, and environmental information
- Generating annotated data sets
Model Creation for Edge Computing
- Choosing appropriate model structures
- Training TinyML models utilizing TensorFlow Lite
- Assessing performance metrics for embedded contexts
Model Refinement and Conversion
- Applying quantization techniques
- Transforming models for microcontroller compatibility
- Optimizing memory usage and computational load
Implementation on Raspberry Pi
- Executing TensorFlow Lite inference
- Incorporating model outputs into broader applications
- Diagnosing and resolving performance challenges
Implementation on Arduino
- Leveraging the Arduino TensorFlow Lite Micro library
- Transferring models to microcontroller memory
- Confirming precision and execution stability
Developing Full-Scale TinyML Solutions
- Architecting integrated embedded AI processes
- Building interactive, real-world prototypes
- Testing and polishing project capabilities
Conclusion and Future Directions
Requirements
- A foundational grasp of core programming principles
- Practical experience in utilizing microcontrollers
- Proficiency in either Python or C/C++
Target Audience
- Makers
- Hobbyists
- Embedded AI developers
