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Course Outline
Introduction to Edge AI and TinyML
- An overview of edge-based AI
- The advantages and challenges of executing AI on local devices
- Application scenarios in robotics and automation
TinyML Fundamentals
- Machine learning techniques for resource-constrained systems
- Methods for model quantization, pruning, and compression
- Review of supported frameworks and hardware platforms
Model Development and Conversion
- Training lightweight models with TensorFlow or PyTorch
- Converting models to formats compatible with TensorFlow Lite and PyTorch Mobile
- Testing and validating model precision
Implementing On-Device Inference
- Deploying AI models on embedded boards such as Arduino, Raspberry Pi, and Jetson Nano
- Integrating inference processes with robotic perception and control loops
- Executing real-time predictions and monitoring system performance
Optimizing for Edge Performance
- Minimizing latency and energy expenditure
- Leveraging hardware acceleration via NPUs and GPUs
- Benchmarking and profiling embedded inference workflows
Edge AI Frameworks and Tools
- Utilizing TensorFlow Lite and Edge Impulse
- Examining deployment options with PyTorch Mobile
- Debugging and refining embedded ML processes
Practical Integration and Case Studies
- Architecting edge AI perception systems for robotic applications
- Combining TinyML with ROS-based robotics architectures
- Case study analyses: autonomous navigation, object detection, and predictive maintenance
Conclusion and Future Directions
Requirements
- A solid understanding of embedded systems
- Proficiency in Python or C++ programming
- Knowledge of 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.