Edge AI refers to running artificial intelligence workloads directly on devices at the “edge” of the network instead of in the cloud. In industrial and commercial projects, this usually means that the embedded system, HMI panel, or single board computer (SBC) has enough local compute power to analyze sensor data, camera streams, or user behavior in real time. For applications such as smart factories, building automation, medical devices, and smart home control panels, Edge AI reduces latency, improves reliability when the network is unstable, and helps protect sensitive data by keeping it on the device.
Under this Edge AI tag, you will find practical articles about ARM-based SBCs with integrated NPUs, Android and Linux platforms for AI inference, and display solutions that combine responsive touch HMIs with on-device intelligence. The focus is on real engineering topics: how to choose hardware, how to architect systems, and how to turn AI algorithms into robust products that can run 24/7 in demanding environments.
As more SoCs add dedicated NPUs and GPU acceleration, Edge AI is rapidly moving from experimental projects into mainstream embedded design. Instead of treating the display as a “dumb” screen and pushing all analytics to a remote server, modern control panels can host computer vision, anomaly detection, or user recognition models directly on the same board that drives the TFT display. This changes how engineers think about architecture: the edge device becomes both a user interface and an intelligent node that can act autonomously when needed.
Typical Edge AI use cases in embedded systems include:
Hardware platforms that combine ARM CPUs, integrated NPUs, and high-quality TFT/IPS displays are at the center of this evolution. They allow OEMs to keep software stacks flexible—running Linux or Android, using familiar AI frameworks—while still meeting industrial requirements such as long-term availability, extended temperature ranges, and robust mechanical design. The articles under this tag aim to bridge theory and practice, helping you design Edge AI systems that are not only impressive in demos, but also manufacturable, maintainable, and ready for real installations.
