Customized Cross-device Neural Architecture Search with Images

Cross-device scenarios have become increasingly common, where non-independently and identically distributed (non-IID) data is generated and stored in different devices. However, the existing cross-device NAS methods only search for a fixed architecture for different devices, neglecting that different devices have varying hardware characteristics and data distributions. In this paper, we propose a novel NAS framework that can customize the most suitable architecture for each device and its associated dataset. Specifically, we propose a decoupled data feature extractor and a device feature extractor to characterize the complex distributions of the different datasets and diverse hardware features. Then, we propose a prototype matcher to customize the operators and shape selection parameters of architectures. Experiments on ImageNet and CIFAR-10 show that our method can discover more efficient and effective architectures in cross-device scenarios than the existing approaches. To the best of our knowledge, this is the first exploration on customized cross-device NAS problem.