# A Hardware-Aware Framework for Accelerating Neural Architecture Search Across Modalities Source: [A Hardware-Aware Framework for Accelerating Neural Architecture Search Across Modalities]() Collection index: [llms.txt]() Data updated: 2025-10-26T00:13:46.432Z Authors: Daniel Cummings, Anthony Sarah, Sharath Nittur Sridhar, Maciej Szankin, Juan Pablo Munoz, Sairam Sundaresan Published: 2022-05-19T20:41:01.000Z ## Abstract Recent advances in Neural Architecture Search (NAS) such as one-shot NAS offer the ability to extract specialized hardware-aware sub-network configurations from a task-specific super-network. While considerable effort has been employed towards improving the first stage, namely, the training of the super-network, the search for derivative high-performing sub-networks is still under-explored. Popular methods decouple the super-network training from the sub-network search and use performance predictors to reduce the computational burden of searching on different hardware platforms. We propose a flexible search framework that automatically and efficiently finds optimal sub-networks that are optimized for different performance metrics and hardware configurations. Specifically, we show how evolutionary algorithms can be paired with lightly trained objective predictors in an iterative cycle to accelerate architecture search in a multi-objective setting for various modalities including machine translation and image classification. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()