Open Source Summit India 2026

Cameras are no longer just for pictures, they are now real-time data pipelines that send information to ISPs, NPUs, and control logic. This is because edge AI is becoming a most wanted vision systems. Zephyr is good at deterministic embedded control, but AI-driven camera workloads need new architectural features like zero-copy buffer sharing, accelerator coordination, bounded latency, metadata synchronization, and controlled backpressure.

This talks about what needs to change in Zephyr's camera and driver architecture to make AI vision work in the real world. Based on our experience with Linux media pipelines and setting up embedded cameras, we look at where traditional RTOS-style camera models fail and what simple abstractions are needed to make them work without adding too much complexity.

The goal is not to make Linux features equal, but to make the architecture better. This includes designing pipelines, structuring buffer ownership, making streaming states more predictable, and making things easier to see. The goal is to keep Zephyr lightweight while also allowing robotics, industrial, and mission-critical systems to work with the next generation of AI cameras.