标题： ESM：一种用于构建硬件感知神经架构搜索有效代理模型的框架 显示英文标题

标题： ESM: A Framework for Building Effective Surrogate Models for Hardware-Aware Neural Architecture Search

摘要： 硬件感知神经架构搜索（NAS）是为资源受限设备设计高效深度神经网络（DNNs）最有前途的技术之一。 代理模型在硬件感知NAS中起着至关重要的作用，因为它们能够有效地预测不同候选模型在目标硬件设备上的性能特征（例如，推理延迟和能耗）。 在本文中，我们专注于构建硬件感知的延迟预测模型。 我们研究了不同类型的代理模型并强调了它们的优缺点。 我们进行了一项系统分析，以了解可能影响这些模型预测准确性的不同因素，旨在评估模型设计过程中每个阶段的重要性，并确定设计/训练有效估计模型所需的方法和策略，特别是针对GPU驱动的设备。 基于分析中获得的见解，我们提出了一种全面的框架，该框架考虑了模型生成管道中不同阶段的整体成本，从而实现了可靠的数据集生成和高效的模型生成。 显示英文摘要

摘要： Hardware-aware Neural Architecture Search (NAS) is one of the most promising techniques for designing efficient Deep Neural Networks (DNNs) for resource-constrained devices. Surrogate models play a crucial role in hardware-aware NAS as they enable efficient prediction of performance characteristics (e.g., inference latency and energy consumption) of different candidate models on the target hardware device. In this paper, we focus on building hardware-aware latency prediction models. We study different types of surrogate models and highlight their strengths and weaknesses. We perform a systematic analysis to understand the impact of different factors that can influence the prediction accuracy of these models, aiming to assess the importance of each stage involved in the model designing process and identify methods and policies necessary for designing/training an effective estimation model, specifically for GPU-powered devices. Based on the insights gained from the analysis, we present a holistic framework that enables reliable dataset generation and efficient model generation, considering the overall costs of different stages of the model generation pipeline.