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 Duration 21 hours

Course Outline

Introduction to Edge AI and TinyML

  • Introduction to edge-based AI concepts
  • Advantages and hurdles of executing AI on devices
  • Applications in robotics and automation

TinyML Essentials

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

Model Creation and Transformation

  • Training lightweight models with TensorFlow or PyTorch
  • Translating models for TensorFlow Lite and PyTorch Mobile
  • Verifying and validating model accuracy

Implementing On-Device Inference

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

Enhancing Edge Performance

  • Minimizing latency and power consumption
  • Leveraging hardware acceleration via NPUs and GPUs
  • Conducting benchmarking and profiling for embedded inference

Edge AI Frameworks and Utilities

  • Utilizing TensorFlow Lite and Edge Impulse
  • Investigating PyTorch Mobile deployment strategies
  • Debugging and refining embedded ML workflows

Real-World Integration and Case Studies

  • Architecting edge AI perception systems for robots
  • Merging TinyML with ROS-based robotics frameworks
  • Case studies: autonomous navigation, object detection, and predictive maintenance

Recap and Future Directions

Requirements

  • Foundational knowledge of embedded systems
  • Proficiency in Python or C++ programming
  • Basic familiarity with machine learning concepts

Intended Audience

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

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