AI and ML are transforming embedded systems across sectors like automotive, defense, and healthcare. They offer powerful capabilities for autonomy and decision-making, but their adoption in safety-critical embedded applications introduces complexities. Embedded systems often have strict limitations on processing power, memory, and response time—constraints that can conflict with AI/ML model demands. Also, the inherent unpredictability of AI behavior raises safety concerns when reliability is nonnegotiable.
Ensuring safe deployment requires locking AI/ML models after training to prevent behavior drift in the field. Regulatory compliance with standards like ISO 26262 and IEC 62304 is essential—it requires rigorous validation. Static code analysis, unit testing, and hardware-in-the-loop play a pivotal role in confirming that AI-enabled functions meet safety and performance expectations.
This presentation explores navigating the intersection of AI/ML innovation and stringent embedded safety requirements. We’ll examine current testing methodologies, implementation challenges, and emerging solutions that balance AI benefits with the need for verifiable, deterministic behavior.
Key topics:
Foundations of AI/ML in embedded applications
Safety-critical implementation hurdles
Innovations making AI/ML viable for embedded environments
Strategies for predictable, safe AI behavior
Testing techniques for validating AI/ML in embedded systems
AI-enhanced testing workflows