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Course Outline
Introduction to Edge AI and TinyML
- Overview of edge AI applications
- Benefits and challenges of deploying AI on devices
- Key use cases in robotics and automation
Fundamentals of TinyML
- Machine learning tailored for resource-constrained systems
- Techniques such as model quantization, pruning, and compression
- Supported frameworks and compatible hardware platforms
Model Development and Conversion
- Training lightweight models using TensorFlow or PyTorch
- Converting models to TensorFlow Lite and PyTorch Mobile formats
- Testing and validating model accuracy
Implementing On-Device Inference
- Deploying AI models to embedded boards (e.g., Arduino, Raspberry Pi, Jetson Nano)
- Integrating inference capabilities with robotic perception and control systems
- Executing real-time predictions and monitoring system performance
Optimizing for Edge Performance
- Strategies to reduce latency and energy consumption
- Leveraging hardware acceleration via NPUs and GPUs
- Benchmarking and profiling embedded inference performance
Edge AI Frameworks and Tools
- Utilizing TensorFlow Lite and Edge Impulse
- Exploring deployment options with PyTorch Mobile
- Debugging and tuning embedded machine learning 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
- Knowledge of embedded systems
- Proficiency in Python or C++ programming
- Familiarity with fundamental machine learning concepts
Target Audience
- Embedded developers
- Robotics engineers
- System integrators specializing in 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.
