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 Duration 21 hours

Course Outline

Introduction to Edge AI and TinyML

  • Overview of AI applications at the edge
  • Advantages and challenges of executing AI on devices
  • Practical use cases in robotics and automation

Core Concepts of TinyML

  • Machine learning for resource-limited systems
  • Techniques for model quantization, pruning, and compression
  • Compatible frameworks and hardware platforms

Model Development and Conversion

  • Developing lightweight models using TensorFlow or PyTorch
  • Converting models for TensorFlow Lite and PyTorch Mobile
  • Testing and validating model accuracy

Implementing On-Device Inference

  • Deploying AI models on embedded boards (Arduino, Raspberry Pi, Jetson Nano)
  • Integrating inference capabilities with robotic perception and control
  • Executing real-time predictions and monitoring performance

Optimizing for Edge Performance

  • Minimizing latency and energy consumption
  • Leveraging hardware acceleration via NPUs and GPUs
  • Benchmarking and profiling embedded inference

Edge AI Frameworks and Tools

  • Working with TensorFlow Lite and Edge Impulse
  • Examining PyTorch Mobile deployment options
  • Debugging and refining embedded ML workflows

Practical Integration and Case Studies

  • Designing edge AI perception systems for robots
  • Integrating TinyML with ROS-based robotics architectures
  • Case studies covering autonomous navigation, object detection, and predictive maintenance

Summary and Next Steps

Requirements

  • A solid understanding of embedded systems
  • Proficiency in Python or C++ programming
  • Familiarity with fundamental machine learning concepts

Target Audience

  • Embedded developers
  • Robotics engineers
  • System integrators focused on intelligent devices

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