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Duration 21 hours
Course Outline
Introduction to Edge AI and TinyML
- Introduction to edge-based AI concepts
- Advantages and hurdles of executing AI on devices
- Applications in robotics and automation
TinyML Essentials
- Machine learning tailored for resource-constrained systems
- Techniques for model quantization, pruning, and compression
- Overview of supported frameworks and hardware platforms
Model Creation and Transformation
- Training lightweight models with TensorFlow or PyTorch
- Translating models for TensorFlow Lite and PyTorch Mobile
- Verifying and validating model accuracy
Implementing On-Device Inference
- Deploying AI models on embedded boards (Arduino, Raspberry Pi, Jetson Nano)
- Fusing inference processes with robotic perception and control
- Executing real-time predictions and tracking performance
Enhancing Edge Performance
- Minimizing latency and power consumption
- Leveraging hardware acceleration via NPUs and GPUs
- Conducting benchmarking and profiling for embedded inference
Edge AI Frameworks and Utilities
- Utilizing TensorFlow Lite and Edge Impulse
- Investigating PyTorch Mobile deployment strategies
- Debugging and refining embedded ML workflows
Real-World Integration and Case Studies
- Architecting edge AI perception systems for robots
- Merging TinyML with ROS-based robotics frameworks
- Case studies: autonomous navigation, object detection, and predictive maintenance
Recap and Future Directions
Requirements
- Foundational knowledge of embedded systems
- Proficiency in Python or C++ programming
- Basic familiarity with machine learning concepts
Intended 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.