Hello NXP Community,
I’m currently developing an edge AI application focused on real-time video analysis using a microcontroller-based system. The processing pipeline includes a camera input, light object tracking, and decision feedback via wireless communication. Performance and timing are critical, and I’m exploring ways to handle data more efficiently at the edge without always relying on cloud inference.
In reading about high-throughput consumer systems—like the gaming PC setups—I noticed how those machines handle large volumes of data quickly and manage heat and power consumption smartly. While obviously not using that hardware, I’m curious if similar logic applies to embedded architectures. Are there best practices here at NXP for handling bursty data streams, efficient buffering, or minimal-latency responses on devices like i.MX or LPC series?
Also, if anyone has experience using NXP’s ML toolkits or optimizing CMSIS-NN layers on constrained hardware, I’d love to hear your insights. I want to avoid performance bottlenecks at any step—whether it’s frame capture, preprocessing, or ML inference.
Really appreciate the expertise and support of the NXP Community. Looking forward to hearing how others have tackled similar challenges in edge AI or high-speed embedded workloads.
Thanks in advance!
