Abstract
Hardware-Aware Neural Architecture Search (HW-NAS) has emerged as a powerful tool for designing efficient deep neural networks (DNNs) tailored to edge devices. However, existing methods remain largely impractical for real-world deployment due to their high time cost, extensive manual profiling, and poor scalability across diverse hardware platforms with complex, device-specific energy behavior. In this paper, we present PlatformX, a fully automated and transferable HW-NAS framework designed to overcome these limitations. PlatformX integrates four key components: (i) an energy-driven search space that expands conventional NAS design by incorporating energy-critical configurations, enabling exploration of high-efficiency architectures; (ii) a transferable kernel-level energy predictor across devices and incrementally refined with minimal on-device samples; (iii) a Pareto-based multi-objective search algorithm that balances energy and accuracy to identify optimal trade-offs; and (iv) a high-resolution runtime energy profiling system that automates on-device power measurement using external monitors without human intervention. We evaluate PlatformX across multiple mobile platforms, showing that it significantly reduces search overhead while preserving accuracy and energy fidelity. It identifies models with up to 0.94 accuracy or as little as 0.16 mJ per inference, both outperforming MobileNet-V2 in accuracy and efficiency. Code and tutorials are available at this http URL.
Abstract (translated)
硬件感知神经架构搜索（HW-NAS）已成为设计适用于边缘设备的高效深度神经网络（DNNs）的强大工具。然而，现有的方法由于高昂的时间成本、大量的人工分析以及在具有复杂且特定于设备的能量行为的不同硬件平台上的可扩展性差等问题，在实际部署中仍然存在较大挑战。本文介绍了PlatformX，这是一种全自动且跨平台转移的HW-NAS框架，旨在克服上述限制。PlatformX整合了四个关键组成部分：(i) 一个以能量驱动的搜索空间，通过纳入临界能耗配置来拓展传统的NAS设计，从而探索高效率架构；(ii) 设备间可转移的内核级能耗预测器，可通过少量设备样本逐步优化；(iii) 基于帕累托前沿的多目标搜索算法，平衡能量与准确率以识别最优权衡点；以及(iv) 高分辨率运行时能耗分析系统，自动执行无须人工干预的设备功耗测量。我们在多个移动平台上对PlatformX进行了评估，结果显示它显著减少了搜索开销，并且在保持准确性的同时也维持了能源效用水平。所发现的模型能够达到高达0.94的准确率或低至0.16毫焦每推理能耗，在精度和效率方面均优于MobileNet-V2。代码和教程可在提供的URL上获取。
URL
https://arxiv.org/abs/2510.08993
