Get in Touch
 Duration 21 hours

Course Outline

Introduction to Edge AI and TinyML

  • An overview of AI at the edge.
  • Exploring the advantages and challenges of on-device AI.
  • Real-world use cases in robotics and automation.

Fundamentals of TinyML

  • Machine learning techniques for resource-constrained environments.
  • 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 for TensorFlow Lite and PyTorch Mobile.
  • Testing and validating model accuracy.

On-Device Inference Implementation

  • Deploying AI models to embedded boards such as Arduino, Raspberry Pi, and Jetson Nano.
  • Linking inference processes with robotic perception and control.
  • Executing real-time predictions and monitoring performance metrics.

Optimization for Edge Performance

  • Strategies to minimize latency and energy consumption.
  • Leveraging hardware acceleration through NPUs and GPUs.
  • Benchmarking and profiling embedded inference workflows.

Edge AI Frameworks and Tools

  • Working with TensorFlow Lite and Edge Impulse.
  • Investigating deployment options with PyTorch Mobile.
  • Debugging and refining embedded ML workflows.

Practical Integration and Case Studies

  • Architecting edge AI perception systems for robots.
  • Merging TinyML with ROS-based robotics architectures.
  • Case studies covering 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 focused on intelligent devices.

Number of participants


Price per participant

Testimonials (2)

Upcoming Courses

Related Categories