As Artificial Intelligence continues to move from the cloud to the edge, developers face new challenges in deploying and debugging AI workloads on deeply embedded systems. Edge AI applications running on microcontrollers and using CMSIS or Zephyr RTOS must deliver real-time inference under tight memory and performance constraints. Diagnosing performance bottlenecks, timing anomalies, or inconsistent inference results becomes complex when traditional debugging tools offer limited visibility into system-level interactions.

This session demonstrates how the Arm CMSIS Debugger extension for Visual Studio Code enables developers to understand, optimize, and debug edge AI workloads more effectively. The talk introduces common issues in edge AI integration such as thread scheduling conflicts, inference latency, and memory exhaustion. It demonstrates how CMSIS Debugger provides fine-grained insight into runtime behavior through RTOS-aware debugging, thread inspection, and event tracing.

Attendees will see a live demonstration of debugging a CMSIS- and Zephyr-based AI application, showcasing how to identify timing issues, analyze performance, and correlate AI kernel execution with system events. By the end, participants will gain practical methods to transform AI debugging from a trial-and-error process into a structured, data-driven workflow resulting in shorter development cycles and improving reliability at the edge.