Cursusaanbod

Introduction to TinyML

  • What is TinyML?
  • Why run AI on microcontrollers?
  • Challenges and benefits of TinyML

Setting Up the TinyML Development Environment

  • Overview of TinyML toolchains
  • Installing TensorFlow Lite for Microcontrollers
  • Working with Arduino IDE and Edge Impulse

Building and Deploying TinyML Models

  • Training AI models for TinyML
  • Converting and compressing AI models for microcontrollers
  • Deploying models on low-power hardware

Optimizing TinyML for Energy Efficiency

  • Quantization techniques for model compression
  • Latency and power consumption considerations
  • Balancing performance and energy efficiency

Real-Time Inference on Microcontrollers

  • Processing sensor data with TinyML
  • Running AI models on Arduino, STM32, and Raspberry Pi Pico
  • Optimizing inference for real-time applications

Integrating TinyML with IoT and Edge Applications

  • Connecting TinyML with IoT devices
  • Wireless communication and data transmission
  • Deploying AI-powered IoT solutions

Real-World Applications and Future Trends

  • Use cases in healthcare, agriculture, and industrial monitoring
  • The future of ultra-low-power AI
  • Next steps in TinyML research and deployment

Summary and Next Steps

Vereisten

  • An understanding of embedded systems and microcontrollers
  • Experience with AI or machine learning fundamentals
  • Basic knowledge of C, C++, or Python programming

Audience

  • Embedded engineers
  • IoT developers
  • AI researchers
 21 Uren

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