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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 strategies for resource-constrained systems.
- Techniques for 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.
On-Device Inference Implementation
- Deploying AI models to embedded boards (Arduino, Raspberry Pi, Jetson Nano).
- Integrating inference with robotic perception and control systems.
- Executing real-time predictions and monitoring performance metrics.
Optimization for Edge Performance
- Strategies to reduce 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, and predictive maintenance.
Summary and Next Steps
Requirements
- A solid understanding of embedded systems.
- Proficiency in Python or C++ programming.
- Basic familiarity with machine learning concepts.
Target Audience
- Embedded developers.
- Robotics engineers.
- System integrators specializing in 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.