TINYML AND EMBEDDED AI WITH ESP32
Pre-requisites
- Basic knowledge of C/C++ programming (Arduino IDE familiarity preferred).
- Understanding of microcontrollers (GPIO, ADC, serial communication).
- Fundamental knowledge of machine learning concepts (what training and inference mean).
- Laptop with Arduino IDE installed (Windows/Linux/macOS) and USB cable for ESP32.
- ESP32 development board (ESP32/ESP32-S3 recommended), basic electronic components (LED, button, potentiometer, light sensor).
Tools & Platforms
- Hardware:
- ESP32 board
- USB cable
- LED
- Push button
- Potentiometer/light sensor
- Breadboard
- Jumper wires
- Laptop/PC
- Software:
- Arduino IDE with ESP32 support
- TensorFlow Lite for Microcontrollers (TFLM) library
- Edge Impulse Studio / TensorFlow Lite
- Arduino Serial Monitor/Plotter
- Python (optional)
Outcomes:
- Deploy TensorFlow Lite Micro models on ESP32 boards.
- Perform basic signal processing (filtering, feature extraction) for sensor data.
- Implement real-time ML applications (gesture, anomaly, keyword, and chatbot demos).
- Evaluate and optimize TinyML models for inference speed and memory footprint.
- Prototype IoT + AI solutions for domains like predictive maintenance, smart monitoring, and human–device interaction.
- Confidently design and demonstrate a working TinyML project using ESP32.
