Model Quantization Strategies for Edge Devices: Raspberry Pi & Jetson Nano

Deploying deep learning models to single-board computers or edge hardware forces you to reckon with strict memory and thermal envelopes. Floating-point 32 (FP32) models are often far too bloated for real-time inference on lower-tier hardware.

Transitioning models to FP16 or INT8 via post-training quantization (PTQ) or quantization-aware training (QAT) can slash model sizes by up to 75% with negligible loss in top-1 accuracy.

When working with ONNX Runtime or TensorRT on ARM architectures, ensuring your layers are fully supported by hardware accelerators is critical. Has anyone tested INT8 quantized models on Raspberry Pi 4 units running Pygame or OpenCV vision scripts?