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Can we automatically design a Convolutional Network (ConvNet) with thehighest image classification accuracy under the runtime constraint of a mobiledevice? Neural architecture search (NAS) has revolutionized the design ofhardware-efficient ConvNets by automating this process. However, the NASproblem remains challenging due to the combinatorially large design space,causing a significant searching time (at least 200 GPU-hours). To alleviatethis complexity, we propose Single-Path NAS, a novel differentiable NAS methodfor designing hardware-efficient ConvNets in less than 4 hours. Ourcontributions are as follows: 1. Single-path search space: Compared to previousdifferentiable NAS methods, Single-Path NAS uses one single-pathover-parameterized ConvNet to encode all architectural decisions with sharedconvolutional kernel parameters, hence drastically decreasing the number oftrainable parameters and the search cost down to few epochs. 2.Hardware-efficient ImageNet classification: Single-Path NAS achieves 74.96%top-1 accuracy on ImageNet with 79ms latency on a Pixel 1 phone, which isstate-of-the-art accuracy compared to NAS methods with similar constraints(<80ms). 3. NAS efficiency: Single-Path NAS search cost is only 8 epochs (30TPU-hours), which is up to 5,000x faster compared to prior work. 4.Reproducibility: Unlike all recent mobile-efficient NAS methods which onlyrelease pretrained models, we open-source our entire codebase at:https://github.com/dstamoulis/single-path-nas.

Single-Path NAS: Designing Hardware-Efficient ConvNets in less than 4 Hours