Reviews: Constrained deep neural network architecture search for IoT devices accounting for hardware calibration

This paper studies the NAS problem in the IoT platform scenarios with specific hardware considerations. The idea is to break down the original whole search space into a set of subspaces through sampling, and the final, actual search is conducted in the typically much narrower space as the union of these sampled subspaces. In the IoT platform scenarios, specific hardware considerations are applied in sampling the space. The whole work of the paper is reported through a case study on image classification using CIFAR-10 dataset, and the better ever results are reported. The only methodological contribution as I see is the idea of breaking down the original space into subspaces and conducting the search in the narrow space as the union of the subspaces.