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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 local devices
  • Key use cases in robotics and automation

Core Concepts of TinyML

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

Model Development and Transformation

  • Training lightweight models with 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 such as Arduino, Raspberry Pi, and Jetson Nano
  • Integrating inference capabilities with robotic perception and control
  • Executing real-time predictions and monitoring system performance

Optimizing for Edge Performance

  • Strategies to reduce latency and lower energy consumption
  • Leveraging hardware acceleration with NPUs and GPUs
  • Benchmarking and profiling embedded inference processes

Edge AI Frameworks and Tooling

  • Utilizing TensorFlow Lite and Edge Impulse effectively
  • Exploring deployment options with PyTorch Mobile
  • Debugging and refining embedded ML workflows

Practical Integration and Case Studies

  • Designing edge AI perception systems for robotic applications
  • Integrating TinyML solutions with ROS-based robotics architectures
  • Case study analyses: autonomous navigation, object detection, and predictive maintenance

Summary and Future Directions

Requirements

  • Solid understanding of embedded systems
  • Proficiency in Python or C++ programming
  • Knowledge of fundamental machine learning concepts

Target Audience

  • Embedded software developers
  • Robotics engineers
  • System integrators focused on intelligent device development

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