Hello everyone,
I’m currently exploring how to integrate lightweight AI PC capabilities into a general embedded setup using NXP platforms. The goal is to combine standard embedded control with on-device AI features for applications such as vision processing, sensor intelligence, and real-time data interpretation.
I’m interested in learning about effective approaches for:
Optimizing system performance when running AI inference alongside regular tasks.
Organizing memory efficiently for both application logic and AI models.
Selecting suitable software frameworks, whether TensorFlow Lite, ONNX Runtime, or NXP’s own toolchains.
If anyone has experience implementing AI or ML workloads on NXP processors in a broad or platform-agnostic context, I’d really appreciate your insights. Recommendations on workflows, reference examples, or tools that support smooth development would be very helpful.
Looking forward to the community’s thoughts and shared experiences!
