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コース概要
Introduction to Edge AI and TinyML
- Overview of AI at the edge
- Benefits and challenges of running AI on devices
- Use cases in robotics and automation
Fundamentals of TinyML
- Machine learning for resource-constrained systems
- 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
- Running real-time predictions and monitoring performance
Optimization for Edge Performance
- Reducing latency and energy consumption
- Hardware acceleration using NPUs and GPUs
- Benchmarking and profiling embedded inference
Edge AI Frameworks and Tools
- Working with TensorFlow Lite and Edge Impulse
- Exploring PyTorch Mobile deployment options
- 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
要求
- An understanding of embedded systems
- Experience with Python or C++ programming
- Familiarity with basic machine learning concepts
Audience
- Embedded developers
- Robotics engineers
- System integrators working on intelligent devices
21 時間
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its knowledge and utilization of AI for Robotics in the Future.
