Once-for-All trains a single large neural network that can be specialized into many sub-networks optimized for different hardware platforms (e.g., mobile CPUs, GPUs, FPGAs) and latency constraints. The approach uses progressive shrinking to progressively train sub-networks with different widths, depths, and resolutions, enabling efficient deployment without retraining. It has been adopted by Sony, Maxim Integrated (for TinyML chips), and Alibaba, winning multiple computer vision challenges.