Edge computing brings computation and data storage closer to the sources of data. This improves response times and saves bandwidth. When integrated with lightweight Edge AI models, devices can perform real-time inferences without continuous cloud connectivity, critical in autonomous vehicles, smart farming, and hospital telemedicine equipment.

In this article, we evaluate tensor quantization methods and resource-constrained inference engines suited for industrial microcontroller platforms tested within our campus robotics and automation lab.