From Track to Edge: Shipping Real-Time AI on Embedded Linux

Deploying AI at the edge on embedded Linux goes far beyond running inference on a local device. It is a full systems engineering challenge that involves hardware constraints, runtime compatibility, thermal management, and long-term maintainability. When real-time feedback is critical and cloud connectivity cannot be guaranteed, the entire pipeline (from model optimization to production deployment) must be designed for the edge from the start.

In this webinar, João Victor Martins, Field Application Engineer, Toradex, and Henrique Garcia, Associate Manager, Torizon OS, present a practical, end-to-end workflow for building and shipping a real-time edge AI system on embedded Linux. Using a motorsport telemetry use case that demands low-latency analysis without connectivity, they cover model export and quantization, CPU vs. NPU trade-offs with real benchmark data, containerized deployment with Torizon OS, and the production challenges that determine whether an edge AI project actually ships.

Key Takeaways:

• How to export, quantize, and deploy AI models to embedded System-on-Modules using ONNX and TFLite.
• CPU vs. NPU inference trade-offs: latency, memory, and thermal behavior under sustained load.
• Containerized edge deployment with Torizon OS: reproducible builds, pipeline separation, and OTA model updates.
• What breaks in production and what to design early when shipping real-time AI on constrained hardware.

We will also have a dedicated live Q&A towards the end of the webinar.