Real-Time Considerations: Determinism Next to AI

Edge AI promises intelligence near the machine, but not all workloads can tolerate latency. When AI inference runs next to real-time PLC control, timing precision becomes a safety and performance issue.

Why Determinism Matters

Traditional AI frameworks operate in best-effort mode, meaning they respond “as soon as possible.” Real-time industrial systems, however, must meet deterministic deadlines — often under 10 ms cycle times. The challenge lies in integrating both worlds.

Balancing AI and Control

• Core isolation: Dedicate CPU cores for PLC or motion control tasks.
• Priority scheduling: Use real-time kernels like PREEMPT-RT or Xenomai.
• Edge partitioning: Run inference on GPU, while PLC logic executes on a separate thread or container.

Common Integration Architectures

• AI module feeding diagnostic data to PLC via OPC UA or shared memory.
• Separate edge compute node connected to control network via TSN.
• Dual-OS setups: Linux for AI, real-time RTOS for I/O tasks.

Case Example

An automotive supplier integrated an NVIDIA Jetson next to a Beckhoff controller. By isolating CPU cores and using TSN for synchronization, they achieved sub-5 ms end-to-end latency without jitter.

Related Articles

• Jetson Orin vs Intel iGPU vs AMD: A 2025 Buyer’s Guide
• Thermals, Enclosures, and Dust: Designing Rugged Edge Nodes
• GPU Sharing at the Edge: Containers and Scheduling

Conclusion

AI at the edge is powerful, but timing rules the factory. With deterministic design, inference and control can coexist — safely, predictably, and in real time.