  State-of-the-art deep networks are often too large to deploy on mobile
devices and embedded systems. Mobile neural architecture search (NAS) methods
automate the design of small models but state-of-the-art NAS methods are
expensive to run. Differentiable neural architecture search (DNAS) methods
reduce the search cost but explore a limited subspace of candidate
architectures. In this paper, we introduce Fine-Grained Stochastic Architecture
Search (FiGS), a differentiable search method that searches over a much larger
set of candidate architectures. FiGS simultaneously selects and modifies
operators in the search space by applying a structured sparse regularization
penalty based on the Logistic-Sigmoid distribution. We show results across 3
existing search spaces, matching or outperforming the original search
algorithms and producing state-of-the-art parameter-efficient models on
ImageNet (e.g., 75.4% top-1 with 2.6M params). Using our architectures as
backbones for object detection with SSDLite, we achieve significantly higher
mAP on COCO (e.g., 25.8 with 3.0M params) than MobileNetV3 and MnasNet.
