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Recent advances in Neural Architecture Search (NAS) such as one-shot NASoffer the ability to extract specialized hardware-aware sub-networkconfigurations from a task-specific super-network. While considerable efforthas been employed towards improving the first stage, namely, the training ofthe super-network, the search for derivative high-performing sub-networks isstill under-explored. Popular methods decouple the super-network training fromthe sub-network search and use performance predictors to reduce thecomputational burden of searching on different hardware platforms. We propose aflexible search framework that automatically and efficiently finds optimalsub-networks that are optimized for different performance metrics and hardwareconfigurations. Specifically, we show how evolutionary algorithms can be pairedwith lightly trained objective predictors in an iterative cycle to acceleratearchitecture search in a multi-objective setting for various modalitiesincluding machine translation and image classification.

A Hardware-Aware Framework for Accelerating Neural Architecture Search Across Modalities