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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 devices
- Practical use cases in robotics and automation
Core Concepts of TinyML
- Machine learning for resource-limited systems
- Techniques for model quantization, pruning, and compression
- Compatible frameworks and hardware platforms
Model Development and Conversion
- Developing lightweight models using 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 (Arduino, Raspberry Pi, Jetson Nano)
- Integrating inference capabilities with robotic perception and control
- Executing real-time predictions and monitoring performance
Optimizing for Edge Performance
- Minimizing latency and energy consumption
- Leveraging hardware acceleration via NPUs and GPUs
- Benchmarking and profiling embedded inference
Edge AI Frameworks and Tools
- Working with TensorFlow Lite and Edge Impulse
- Examining PyTorch Mobile deployment options
- Debugging and refining embedded ML workflows
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 Next Steps
Requirements
- A solid understanding of embedded systems
- Proficiency in Python or C++ programming
- Familiarity with fundamental machine learning concepts
Target Audience
- Embedded developers
- Robotics engineers
- System integrators focused on intelligent devices
Testimonials (2)
Supply of the materials (virtual machine) to get straight into the excersises, and the explanation of the Ros2 core. Why things work a certain way.
Arjan Bakema
Course - Autonomous Navigation & SLAM with ROS 2
its knowledge and utilization of AI for Robotics in the Future.