Ptah: Teaching Computer Architecture through NLP-Controlled Drones with Edge AI and Kubernetes

This paper introduces Ptah, a project-based learning framework for teaching computer architecture by engaging students in designing and deploying NLP-controlled drones powered by edge computing and Kubernetes orchestration. Ptah provides a hands-on educational experience by integrating lightweight NLP models such as BERT into containerized microservices running on modular edge hardware---including Turing Pi 2.5 with Jetson Orin NX, TRK1 with 32 GB RAM, and Turing Pi 2.0 with Jetson Nano and Raspberry Pi Compute Module 4---coordinated via ClusterHAT 2.0 and DeskPi Super6C with Pi Zero 2 W nodes. As students issue spoken commands like "hover," "move forward," or "return home," they witness live end-to-end latencies (1 000-3 000 μs) as inputs flow through a scalable microservice pipeline orchestrated by K3s and RabbitMQ. Crucially, Ptah exposes L1/L2 cache miss rates and TLB-miss behavior via simple perf and /proc/pid/pagemap experiments on the Orin NX, linking textbook CPU/memory abstractions to real hardware measurements. Learners gain intuitive insights into core architectural concepts---CPU scheduling, I/O systems, memory access, parallelism, and system bottlenecks---through real-time feedback and visualization of how high-level AI-driven commands propagate through hardware and software layers. Deployed in the Reconfigurable Space Computing Lab and integrated into NASA MINDS and DoD-funded studies, Ptah demonstrates a replicable, future-ready model for bridging theory and practice in computer architecture education.