公开(公告)号：US11531861B2

公开(公告)日：2022-12-20

申请号：US16258927

申请日：2019-01-28

Applicant: Google LLC

Inventor： Mingxing Tan ,  Quoc Le ,  Bo Chen ,  Vijay Vasudevan ,  Ruoming Pang

IPC: G06N3/04 ,  G06N20/10 ,  G06F17/15 ,  G06N3/08

Abstract: The present disclosure is directed to an automated neural architecture search approach for designing new neural network architectures such as, for example, resource-constrained mobile CNN models. In particular, the present disclosure provides systems and methods to perform neural architecture search using a novel factorized hierarchical search space that permits layer diversity throughout the network, thereby striking the right balance between flexibility and search space size. The resulting neural architectures are able to be run relatively faster and using relatively fewer computing resources (e.g., less processing power, less memory usage, less power consumption, etc.), all while remaining competitive with or even exceeding the performance (e.g., accuracy) of current state-of-the-art mobile-optimized models.