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Course Outline
Introduction to Edge AI and TinyML
- Overview of AI at the edge
- Advantages and challenges of running AI on devices
- Applications in robotics and automation
Fundamentals of TinyML
- Machine learning for resource-constrained systems
- Model quantization, pruning, and compression
- Supported frameworks and hardware platforms
Model Development and Conversion
- Training lightweight models using TensorFlow or PyTorch
- Converting models to TensorFlow Lite and PyTorch Mobile
- Testing and validating model accuracy
Implementing On-Device Inference
- Deploying AI models to embedded boards (Arduino, Raspberry Pi, Jetson Nano)
- Integrating inference with robotic perception and control
- Executing real-time predictions and monitoring performance
Optimizing for Edge Performance
- Reducing latency and energy consumption
- Leveraging hardware acceleration via NPUs and GPUs
- Benchmarking and profiling embedded inference
Edge AI Frameworks and Tools
- Utilizing TensorFlow Lite and Edge Impulse
- Exploring deployment options with PyTorch Mobile
- Debugging and tuning embedded ML workflows
Practical Integration and Case Studies
- Designing edge AI perception systems for robots
- Integrating TinyML with ROS-based robotics architectures
- Case studies: autonomous navigation, object detection, predictive maintenance
Summary and Next Steps
Requirements
- Knowledge of embedded systems
- Programming experience in Python or C++
- Familiarity with fundamental machine learning concepts
Target Audience
- Embedded developers
- Robotics engineers
- System integrators developing intelligent devices
21 Hours
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.
