Abstract: A processor-implemented method for a neural architecture search (NAS) starts by generating an over-parameterized super network having multiple layers. The super network has multiple operator types. Each of the layers includes a largest super kernel corresponding to a search space. The method also includes performing gradient descent to evolve a largest super kernel to a small kernel corresponding to the search space in order to generate a range of kernel encodings. The method further includes identifying a subset of kernel encodings from the range of kernel encodings, for each layer of the super network, based on the gradient descent. The method determines a set of candidate architectures based on the subset of kernel encodings, each of the candidate architectures having a different model size. The method selects a target model, from the set of architectures, based on meeting hardware specifications, and then applies the target model.