How do you optimize and deploy deep learning perception models for automotive embedded systems?

NN deployment optimization: 1) Model optimization - pruning (removing unimportant weights), quantization (FP32 to INT8), knowledge distillation (smaller student model), architecture search for efficiency; 2) Hardware selection - GPU for training, inference accelerators (NPU, DSP, TPU), memory and bandwidth considerations; 3) Framework and toolchain - TensorRT, OpenVINO, TensorFlow Lite, vendor-specific tools; 4) Latency optimization - layer fusion, memory layout optimization, batch size tuning, model partitioning across accelerators; 5) Validation - accuracy preservation after optimization, worst-case latency analysis, thermal performance. Power constraints critical for embedded deployment. Certification considerations include tool qualification, bit-reproducibility, and version control. Balancing accuracy versus latency versus power is key trade-off.