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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 for resource-constrained systems.
- Model quantization, pruning, and compression techniques.
- Supported frameworks and 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.
On-Device Inference Implementation
- 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.
Optimization for Edge Performance
- Reducing latency and energy consumption.
- Leveraging hardware acceleration with NPUs and GPUs.
- Benchmarking and profiling embedded inference.
Edge AI Frameworks and Tools
- Working with TensorFlow Lite and Edge Impulse.
- Exploring deployment options for 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 working on 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.
