Your question is Embedded Perception Optimization. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
How would you optimize a perception model for deployment on an embedded platform?
Discuss a practical workflow for selecting and modifying the model, compressing it, compiling it with NVIDIA TensorRT, and validating accuracy after optimization. Address latency, memory, power, numerical precision, unsupported operators, calibration data, and differences between desktop and embedded performance. Include the experiments, profiling data, and deployment artifacts you would produce.