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 Duration 21 hours

Course Outline

Introduction to Edge AI and TinyML

  • Overview of AI capabilities at the edge.
  • Advantages and challenges of executing AI on local devices.
  • Real-world applications in robotics and automation.

Fundamentals of TinyML

  • Machine learning techniques for resource-constrained systems.
  • Techniques for model quantization, pruning, and compression.
  • Review of supported frameworks and hardware platforms.

Model Development and Conversion

  • Training lightweight models using TensorFlow or PyTorch.
  • Converting models for TensorFlow Lite and PyTorch Mobile.
  • Methods for testing and validating model accuracy.

On-Device Inference Implementation

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

Optimization for Edge Performance

  • Strategies for reducing latency and energy consumption.
  • Leveraging hardware acceleration via NPUs and GPUs.
  • Benchmarking and profiling embedded inference workflows.

Edge AI Frameworks and Tools

  • Utilizing TensorFlow Lite and Edge Impulse.
  • Exploring deployment options with PyTorch Mobile.
  • Debugging and fine-tuning embedded ML workflows.

Practical Integration and Case Studies

  • Designing edge AI perception systems for robots.
  • Integrating TinyML with ROS-based robotics architectures.
  • Case studies: 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 foundational machine learning concepts.

Target Audience

  • Embedded developers.
  • Robotics engineers.
  • System integrators specializing in intelligent devices.

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