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 Duration 21 hours

Course Outline

Introduction to Edge AI and TinyML

  • An overview of AI deployment at the edge.
  • The advantages and challenges of executing AI on physical devices.
  • Relevant use cases in robotics and automation.

Core Concepts of TinyML

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

Model Development and Conversion

  • Training lightweight models using TensorFlow or PyTorch.
  • Converting 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.
  • Integrating inference processes with robotic perception and control systems.
  • Executing real-time predictions and monitoring system performance.

Optimizing for Edge Performance

  • Strategies to minimize latency and energy consumption.
  • Leveraging hardware acceleration via NPUs and GPUs.
  • Benchmarking and profiling embedded inference workloads.

Edge AI Frameworks and Tools

  • Utilizing TensorFlow Lite and Edge Impulse.
  • Exploring deployment options with PyTorch Mobile.
  • Debugging and tuning embedded machine learning workflows.

Practical Integration and Case Studies

  • Designing edge AI perception systems specifically for robots.
  • Integrating TinyML into ROS-based robotics architectures.
  • Case study analyses: autonomous navigation, object detection, and predictive maintenance.

Summary and Future Directions

Requirements

  • A solid foundation in embedded systems.
  • Proficiency in Python or C++ programming.
  • A working knowledge of fundamental machine learning concepts.

Target Audience

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

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