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Duration 21 hours
Course Outline
Introduction to Edge AI and TinyML
- Overview of AI at the edge
- Advantages and challenges of executing AI on devices
- Applications in robotics and automation
Fundamentals of TinyML
- Machine learning for resource-constrained environments
- Model quantization, pruning, and compression techniques
- Supported frameworks and hardware platforms
Model Development and Conversion
- Training lightweight models with TensorFlow or PyTorch
- Converting models to TensorFlow Lite and PyTorch Mobile
- Testing and verifying model accuracy
On-Device Inference Implementation
- Deploying AI models to embedded boards (Arduino, Raspberry Pi, Jetson Nano)
- Combining inference with robotic perception and control
- Executing real-time predictions and monitoring system performance
Optimization for Edge Performance
- Minimizing latency and energy usage
- Leveraging NPUs and GPUs for hardware acceleration
- Benchmarking and profiling embedded inference
Edge AI Frameworks and Tools
- Utilizing TensorFlow Lite and Edge Impulse
- Exploring deployment options with PyTorch Mobile
- Debugging and refining embedded ML workflows
Practical Integration and Case Studies
- Designing edge AI perception systems for robots
- Merging TinyML with ROS-based robotics architectures
- Case studies: autonomous navigation, object detection, and predictive maintenance
Summary and Next Steps
Requirements
- Knowledge of embedded systems
- Proficiency in Python or C++ programming
- Understanding of foundational machine learning concepts
Target Audience
- Embedded developers
- Robotics engineers
- System integrators focusing 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.