Reference architecture
Edge AI Inspection Node
A local inference node that reports an inspection result together with device health and model identity.
This reference architecture does not demonstrate selected hardware, a packaged model or measured real-time performance.
The engineering question
How can inference remain observable and predictable when the network, camera or compute process is interrupted?
Proposed scope
- Local acquisition and bounded processing queue
- Versioned ONNX model and configuration
- Watchdog, health status and controller interface
- Local event buffering and deliberate fallback states
Reference decision path
01 →Image input
02 →Validate frame
03 →Local inference
04 →Check result deadline
05 →Result + health handshake
06 / OUTPUTBuffered event log
Validation plan
- Benchmark on the selected device under thermal load.
- Interrupt camera, network and inference services.
- Check behavior after reboot and model-version changes.
Start with the engineering problem
What does your process
need to do better?
Share the part, the machine, the constraint and the result you need. That is the starting point for a useful technical conversation.