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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 on-device AI execution
  • Applications in robotics and automation

TinyML Foundations

  • Machine learning techniques for resource-limited systems
  • Methods for model quantization, pruning, and compression
  • Supported software frameworks and hardware platforms

Model Development and Adaptation

  • Training lightweight models using TensorFlow or PyTorch
  • Translating models to TensorFlow Lite and PyTorch Mobile formats
  • Testing and validating model accuracy

Implementing On-Device Inference

  • Deploying AI models on embedded boards such as Arduino, Raspberry Pi, and Jetson Nano
  • Combining inference with robotic perception and control systems
  • Executing real-time predictions and monitoring system performance

Optimizing Edge Performance

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

Edge AI Frameworks and Toolsets

  • Utilizing TensorFlow Lite and Edge Impulse
  • Reviewing deployment options with PyTorch Mobile
  • Debugging and tuning embedded machine learning pipelines

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 Future Directions

Requirements

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

Target Audience

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

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