Integrating AI and machine learning into embedded systems unlocks new capabilities—from predictive maintenance to dynamic autonomy. But how can such powerful—and often uncertifiable—technologies be integrated without destabilizing critical real-time environments? This TechCast episode explores safe integration approaches.
Hidden Insights: The Power of Observability
AI and ML can reveal patterns and anomalies that traditional monitoring misses—often uncovering entirely unanticipated issues. Deploying AI close to the infrastructure enables early detection and proactive optimization of systems.
Autonomy, Tamed: Controlled AI Execution
True autonomy poses risks in embedded systems. By deploying AI within isolated partitions—managed by hypervisor mechanisms—developers can ensure that ML workloads enhance rather than disrupt real-time operations. This controlled execution preserves both system stability and autonomy benefits.
Certified meets uncertified: The Partition Strategy
High-performance AI workloads (e.g., GPU-based inferencing) are often impractical to certify. By isolating these within stricter partitions, they can run alongside certified operations without requiring full certification. Controlled interfaces ensure safe interaction across the boundary.
The uncertain Path forward
Looking ahead, embedded systems might trend toward higher-level application development with less low-level hardware involvement. However, maintaining expertise in low-level architecture remains key to controlling complexity and assuring safety.
Conclusion
Embedding AI into real-time systems is no longer hypothetical—it’s near. The challenge lies in integrating uncertainty and autonomy safely. Partitioning via RTOS/hypervisor architectures provides a bridge between powerful, uncertified AI models and certified, deterministic applications—enabling innovation without sacrificing reliability.
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