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The prosperity of Internet of Things (IoT) calls for efficient ways of designing extremely compact yet accu- rate DNN models. Both the cell-based neural architec- ture search methods and the recently proposed graph based methods fall short in finding high quality IoT models due to the search flexibility, accuracy density, and node depen- dency limitations. In this paper, we propose a new graph- based neural architecture search methodology MSNAS for crafting highly compact yet accurate models for IoT de- vices. MSNAS supports flexible search space and can ac- cumulate learned knowledge in a meta-graph to increase accuracy density. By adopting structural wiring architec- ture, MSNAS reduces the dependency between nodes, which allows more compact models without sacrificing accuracy. The preliminary experimental results on IoT applications demonstrate that the MSNet crafted by MSNAS outperforms MobileNetV2 and MnasNet by 3.0% in accuracy, with 20% less peak memory consumption and similar Multi-Adds.