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Duration 21 hours
Course Outline
Introduction to Edge AI and TinyML
- Overview of AI capabilities at the edge
- Advantages and challenges of on-device AI execution
- Applications in robotics and automation
TinyML Foundations
- Machine learning techniques for resource-limited systems
- Methods for model quantization, pruning, and compression
- Supported software frameworks and hardware platforms
Model Development and Adaptation
- Training lightweight models using TensorFlow or PyTorch
- Translating 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
- Combining inference with robotic perception and control systems
- Executing real-time predictions and monitoring system performance
Optimizing Edge Performance
- Minimizing latency and energy consumption
- Leveraging hardware acceleration via NPUs and GPUs
- Benchmarking and profiling embedded inference workflows
Edge AI Frameworks and Toolsets
- Utilizing TensorFlow Lite and Edge Impulse
- Reviewing deployment options with PyTorch Mobile
- Debugging and tuning embedded machine learning pipelines
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 Future Directions
Requirements
- Foundational knowledge of embedded systems
- Practical experience with Python or C++ programming
- Basic familiarity with machine learning concepts
Target Audience
- Embedded software 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.