Abstract For the goal of automated design high-performance deep convolutional neural networks (CNNs), architecture search (NAS) methodology is becoming increasingly important for both academia and industries. Due to costly stochastic gradient descent training CNNs performance evaluation, most existing NAS methods are computationally expensive real-world deployments. To address this issue, we fi...