  We achieve very efficient deep learning model deployment that designs neural
network architectures to fit different hardware constraints. Given a
constraint, most neural architecture search (NAS) methods either sample a set
of sub-networks according to a pre-trained accuracy predictor, or adopt the
evolutionary algorithm to evolve specialized networks from the supernet. Both
approaches are time consuming. Here our key idea for very efficient deployment
is, when searching the architecture space, constructing a table that stores the
validation accuracy of all candidate blocks at all layers. For a stricter
hardware constraint, the architecture of a specialized network can be very
efficiently determined based on this table by picking the best candidate blocks
that yield the least accuracy loss. To accomplish this idea, we propose
Progressive One-shot Neural Architecture Search (PONAS) that combines
advantages of progressive NAS and one-shot methods. In PONAS, we propose a
two-stage training scheme, including the meta training stage and the
fine-tuning stage, to make the search process efficient and stable. During
search, we evaluate candidate blocks in different layers and construct the
accuracy table that is to be used in deployment. Comprehensive experiments
verify that PONAS is extremely flexible, and is able to find architecture of a
specialized network in around 10 seconds. In ImageNet classification, 75.2%
top-1 accuracy can be obtained, which is comparable with the state of the arts.
