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We address the challenging problem of efficient inference across many devicesand resource constraints, especially on edge devices. Conventional approacheseither manually design or use neural architecture search (NAS) to find aspecialized neural network and train it from scratch for each case, which iscomputationally prohibitive (causing $CO_2$ emission as much as 5 cars'lifetime) thus unscalable. In this work, we propose to train a once-for-all(OFA) network that supports diverse architectural settings by decouplingtraining and search, to reduce the cost. We can quickly get a specializedsub-network by selecting from the OFA network without additional training. Toefficiently train OFA networks, we also propose a novel progressive shrinkingalgorithm, a generalized pruning method that reduces the model size across manymore dimensions than pruning (depth, width, kernel size, and resolution). Itcan obtain a surprisingly large number of sub-networks ($> 10^{19}$) that canfit different hardware platforms and latency constraints while maintaining thesame level of accuracy as training independently. On diverse edge devices, OFAconsistently outperforms state-of-the-art (SOTA) NAS methods (up to 4.0%ImageNet top1 accuracy improvement over MobileNetV3, or same accuracy but 1.5xfaster than MobileNetV3, 2.6x faster than EfficientNet w.r.t measured latency)while reducing many orders of magnitude GPU hours and $CO_2$ emission. Inparticular, OFA achieves a new SOTA 80.0% ImageNet top-1 accuracy under themobile setting ($<$600M MACs). OFA is the winning solution for the 3rd LowPower Computer Vision Challenge (LPCVC), DSP classification track and the 4thLPCVC, both classification track and detection track. Code and 50 pre-trainedmodels (for many devices & many latency constraints) are released athttps://github.com/mit-han-lab/once-for-all.

Once-for-All: Train One Network and Specialize it for Efficient Deployment