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Duration 21 hours
Course Outline
Introduction to Edge AI and TinyML
- Overview of AI applications at the edge
- Advantages and challenges of executing AI on local devices
- Key use cases in robotics and automation
Core Concepts of TinyML
- Machine learning strategies for resource-constrained systems
- Techniques for model quantization, pruning, and compression
- Overview of supported frameworks and hardware platforms
Model Development and Transformation
- Training lightweight models with TensorFlow or PyTorch
- Converting models for TensorFlow Lite and PyTorch Mobile
- Testing and validating model accuracy
Implementing On-Device Inference
- Deploying AI models on embedded boards such as Arduino, Raspberry Pi, and Jetson Nano
- Integrating inference capabilities with robotic perception and control
- Executing real-time predictions and monitoring system performance
Optimizing for Edge Performance
- Strategies to reduce latency and lower energy consumption
- Leveraging hardware acceleration with NPUs and GPUs
- Benchmarking and profiling embedded inference processes
Edge AI Frameworks and Tooling
- Utilizing TensorFlow Lite and Edge Impulse effectively
- Exploring deployment options with PyTorch Mobile
- Debugging and refining embedded ML workflows
Practical Integration and Case Studies
- Designing edge AI perception systems for robotic applications
- Integrating TinyML solutions with ROS-based robotics architectures
- Case study analyses: autonomous navigation, object detection, and predictive maintenance
Summary and Future Directions
Requirements
- Solid understanding of embedded systems
- Proficiency in Python or C++ programming
- Knowledge of fundamental machine learning concepts
Target Audience
- Embedded software developers
- Robotics engineers
- System integrators focused on intelligent device development
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.