Deploying AI Inference on UAV Edge Compute

Problem definition

How do I pick an inference runtime and validate a model on my UAV's onboard compute before flight testing?

Steps

1. Define the runtime target

   Identify your onboard compute vendor and accelerator (NVIDIA GPU/Jetson, Intel, Rockchip NPU or a generic CPU). This is a hardware fact, not a compatibility claim.

2. Choose the runtime family

   Record which official runtime matches your target: TensorRT for NVIDIA, OpenVINO for Intel, RKNN-Toolkit2 for Rockchip, or ONNX Runtime for portable deployment.

3. Convert a representative model

   Use the official conversion toolchain on one representative model and record the exact version of the model, runtime and toolchain.

4. Measure on target hardware

   Measure latency and accuracy on the actual onboard hardware with a documented evaluation set. Do not assume desktop results transfer.

5. Record evidence

   Record the runtime, model, conversion toolchain, hardware and measured numbers with their dates in your integration notes.

Guide

Judgment criteria

• The model runs on the target hardware with recorded versions.
• No performance or compatibility claim is made without a measurement.

Common risks

• Assuming desktop-GPU benchmarks transfer to edge hardware.
• Inferring compatibility from a generic format such as ONNX alone.

Checklist