Once for All: Train One Network and Specialize it for Efficient Deployment
We address the challenging problem of efficient deep learning model deployment, where the goal is to design neural network architectures that can fit different hardware platform constraints.Most of the traditional approaches either manually design or use Neural Architecture Search to find a specialized neural network and train it from scratch for each case, which is computationally expensive and unscalable.Our key idea is to decouple model training from architecture search to save the cost.To this end, we propose to train a once-for-all network that supports diverse architectural settings.Given a deployment scenario, we can then quickly get a specialized sub-network by selecting from the OFA network without additional training.To prevent interference between many sub-networks during training, we also propose a novel progressive shrinking algorithm, which can train a surprisingly large number of sub-networks simultaneously.Extensive experiments on various hardware platforms show that OFA consistently outperforms SOTA NAS methods while reducing orders of magnitude GPU hours and emission.In particular, OFA achieves a new SOTA 80.0% ImageNet top1 accuracy under the mobile setting.Code and pre-trained models are released at https://github.com/mit-han-lab/once-for-all.
We introduce techniques to train a single once-for-all network that fits many hardware platforms.Method results in a network from which one can extract sub-networks for various resouce constraints (latency, memory) which perform well without a need for retraining.This paper tries to tackle the problem of searching best architectures for specialized resource constraint deployment scenarios with a prediction based NAS method.