LYNX MOSA.ic.AI Webinar Series

Join us for a three-part webinar series explores how MOSA.ic.AI enables secure, deterministic AI deployment for mission- and safety-critical edge applications. Across the series, Lynx experts will share practical guidance on bringing your own AI models to the platform, achieving deterministic AI execution, and collaborating with ecosystem partners to accelerate AI innovation.

Whether you're developing next-generation defense systems or evaluating AI for embedded applications, these sessions will provide technical insights and real-world strategies for deploying AI with confidence.

Webinar Sessions

Session 1: Bridging the AI Deployment Gap with MOSA.ic.AI: Your Trained Model, Deployed for Deterministic Inference in Constrained, Safety-Critical Environments

August 27, 2026 | 2:00 PM EST

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.

SPEAKER

Ernie Harrison

Principal Software Engineer, Lynx

Ernie Harrison is a Principal Engineer at Lynx, where he leads the development of safety-critical GPU compute and AI technologies based on Vulkan SC. With more than 15 years of experience in embedded software, real-time operating systems, and safety-critical graphics, he has helped architect and deliver DO-178 certifiable GPU drivers, graphics libraries, and compute technologies for aerospace, defense, and other mission-critical industries. Throughout his career at Wind River, CoreAVI, and now Lynx, Ernie has worked closely with leading aerospace and defense organizations and technology partners to bring high-performance graphics and compute capabilities to certifiable embedded platforms. His work spans embedded Linux, virtualization, real-time operating systems, GPU drivers, OpenGL SC, Vulkan SC, and deterministic GPU compute. Today, Ernie's focus is enabling safety-critical AI through Vulkan SC Compute, helping customers deploy deterministic, certifiable GPU-accelerated AI applications on embedded platforms. In this webinar, he will introduce Lynx's new Vulkan SC-based Compute AI products and discuss how they enable next-generation AI capabilities for safety-critical systems.

Session 2: Making AI Execution Predictable: Deterministic AI for Safety-Critical Systems

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.

SPEAKER

Ethan Salehi

Technical Account Manager, Lynx

Ethan Salehi is an Technical Account Manager at Lynx and a Ph.D. researcher at Kennesaw State University, where his research focuses on safe and secure real-time operating systems for multicore embedded systems. He has extensive experience supporting defense and aerospace programs with certifiable software solutions aligned with MOSA, FACE, and DO-178C standards. Ethan previously served as a FACE Verification Authority at LDRA and contributed to multicore interference analysis initiatives in collaboration with the U.S. Army. His work bridges theoretical architectures and practical deployment of OS-level standardization, supporting mission-critical environments such as degraded visual operations and autonomous systems.

LYNX MOSA.ic.AI Webinar Series

Webinar Sessions

SPEAKER

Ernie Harrison

Principal Software Engineer, Lynx

SPEAKER

Ethan Salehi

Technical Account Manager, Lynx

Join Us