Making AI Execution Predictable: Deterministic AI for Safety-Critical Systems

AI workloads introduce timing variability that can make them difficult to deploy in mission- and safety-critical environments. Dynamic resource demands, shared CPU and GPU resources, and unpredictable execution times can interfere with real-time applications and complicate system validation. To deploy AI confidently at the edge, system architects need a way to control how AI workloads execute and demonstrate that critical timing requirements will be met.

In this webinar, we’ll explore how LYNX MOSA.ic.AI enables predictable, bounded AI inference within mixed-criticality embedded systems. Attendees will learn how workload isolation, controlled resource allocation, and unified CPU/GPU execution help reduce interference and establish consistent runtime behavior. We’ll also examine how these capabilities support system validation, certification objectives, and the long-term evolution of AI-enabled platforms.

Attendees will learn how to:

• Identify common sources of variability in embedded AI execution.
  
• Establish predictable and bounded inference performance for safety-critical applications.
  
• Isolate AI, real-time, and safety-critical applications on shared compute platforms.
  
• Coordinate CPU and GPU resources while maintaining timing and workload requirements.
  
• Support system validation and certification efforts with observable, repeatable execution behavior.

This session is part of the The LYNX MOSA.ic.AI Webinar Series. Registration/event page here.