Pareto-Frontier-aware Neural Architecture Generation for Diverse Budgets

Abstract

Designing feasible and effective architectures under diverse computation budgets incurred by different applications/devices is essential for deploying deep models in practice. Existing methods often perform an independent architecture search for each target budget, which is very inefficient yet unnecessary. Moreover, the repeated independent search manner would inevitably ignore the common knowledge among different search processes and hamper the search performance. To address these issues, we seek to train a general architecture generator that automatically produces effective architectures for an arbitrary budget merely via model inference. To this end, we propose a Pareto-Frontier-aware Neural Architecture Generator (NAG) which takes an arbitrary budget as input and produces the Pareto optimal architecture for the target budget. We train NAG by learning the Pareto frontier (i.e., the set of Pareto optimal architectures) over model performance and computational cost (e.g., latency). Extensive experiments on three platforms (i.e., mobile, CPU, and GPU) show the superiority of the proposed method over existing NAS methods.

8 pages

References in corpus (4)

• NAT: Neural Architecture Transformer for Accurate and Compact Architectures
• Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search
• FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions
• PONAS: Progressive One-shot Neural Architecture Search for Very Efficient Deployment