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Duration 21 hours
Course Outline
Introduction to TinyML
- Exploring the limitations and potential of TinyML
- Overview of prevalent microcontroller ecosystems
- Evaluating Raspberry Pi, Arduino, and alternative boards
Hardware Preparation and Setup
- Configuring Raspberry Pi OS
- Setting up Arduino boards
- Linking sensors and peripheral devices
Methods for Data Acquisition
- Recording sensor inputs
- Processing audio, movement, and environmental data
- Building annotated datasets
Developing Models for Edge Hardware
- Choosing appropriate model structures
- Training TinyML models using TensorFlow Lite
- Assessing suitability for embedded applications
Refining and Converting Models
- Applying quantization methods
- Adapting models for microcontroller implementation
- Optimizing memory usage and computational load
Implementation on Raspberry Pi
- Executing TensorFlow Lite inference
- Incorporating model results into software applications
- Resolving performance-related challenges
Implementation on Arduino
- Leveraging the Arduino TensorFlow Lite Micro library
- Transferring models to microcontrollers
- Confirming accuracy and operational behavior
Creating Complete TinyML Systems
- Architecting cohesive embedded AI workflows
- Building interactive, real-world prototypes
- Validating and improving project capabilities
Conclusions and Future Directions
Requirements
- A foundational grasp of basic programming principles
- Hands-on experience with microcontroller operations
- Proficiency in Python or C/C++
Target Audience
- Hardware creators
- Enthusiasts
- Embedded AI engineers
