Iterative Multiple-Path One-Shot NAS for the Optimized Performance

In this work, we develop the Iterative Multiple-path One-shot Network Architecture Search (NAS), (IMO-NAS), to effectively look for preferable neural network architectures in terms of accuracy, computational complexity, and parameter quantity. Our IMO-NAS includes three steps of full search, random search, and candidate retraining at an iterative operation manner. At each iteration, multiple scaling ratios are employed to multiply the channel numbers at all layers to get many combinations for channel searching where the conventional one-shot NAS cannot alter channel numbers. Based on the super neural network using the mixed depthwise convolutional kernels, IMO-NAS is employed to seek outstanding architectures using Cifar-10 and Cifar-100 datasets where the depthwise convolution layer has multiple blocks to establish multiple paths for search. Simulation results reveal that the architectures found by IMO-NAS can have advantageous performance on accuracy, computational complexity, and memory size, comparing to the conventional deep neural network architectures. Therefore, the proposed IMO-NAS can successfully discover new architectures or fine adjust the popularly-used architectures at fair search time for various artificial intelligence applications.