MOSA.ic.AI Webinar Series

October 20, 2026  |  2:00 PM EST

 

Join Lynx and Ansys, part of Synopsys, for a discussion on how collaboration across the defense technology ecosystem can help accelerate the deployment of trusted AI at the edge. This session will explore approaches to interoperability, open architectures, and integrated solutions that simplify AI deployment for defense and aerospace applications while enabling greater flexibility, scalability, and mission readiness. More details to come soon!

 

 

 

AI innovation is accelerating rapidly, but deploying trained models into mission-critical embedded systems remains a significant challenge. While today's AI frameworks excel at model development, they were not designed for the deterministic inference, workload isolation, and certification requirements of aerospace, defense, and other safety-critical applications. LYNX MOSA.ic.AI bridges this AI deployment gap by optimizing AI inference for Size, Weight, and Power (SWaP)-constrained embedded systems, enabling validated models to execute predictably alongside real-time and safety-critical workloads.

In this webinar, you'll learn how to bring your own trained AI model into MOSA.ic.AI without changing your existing AI development workflow. We'll demonstrate how the platform separates model development from deployment while providing deterministic inference, unified CPU/GPU execution, mixed-criticality support, hardware portability, and open systems architecture to simplify integration and enable reliable AI deployment at the edge.

Attendees will learn how to:

• Bring existing AI models into MOSA.ic.AI without modifying established development workflows.
• Optimize AI inference for SWaP-constrained embedded systems while maintaining predictable, deterministic behavior.
• Deploy AI alongside real-time and safety-critical applications using a unified CPU/GPU architecture.
• Reduce integration risk with an open, hardware-portable platform designed for long-term mission evolution
  
  

September 15, 2026  |  2:00 PM EST

 

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 critical workloads.
• Isolate AI, real-time, and safety-critical applications on shared compute platforms.
• Coordinate CPU and GPU resources while maintaining timing and workload requirements.
• Support validation and certification efforts with observable, repeatable execution behavior.