分析190种视觉Transformer在移动端的推理延迟，提出可预测新模型性能的方法。
A Study on Inference Latency for Vision Transformers on Mobile Devices
- 对比190个ViT和102个CNN，分析影响移动端延迟的关键因素
- 构建包含1000个合成ViT的延迟数据集，预测精度满足实际应用需求
- 适合移动AI优化、模型部署与性能预估的研究者参考
随着移动设备上机器学习技术的显著进步，尤其是在计算机视觉领域，本文定量研究了190个真实世界的视觉变压器（ViTs）在移动设备上的性能特征。通过与102个真实世界的卷积神经网络（CNNs）进行比较，我们深入探讨了影响ViT架构在移动设备上延迟的因素。基于这些洞察，我们构建了一个数据集，包含来自两个机器学习框架和六个移动平台的1000个具有代表性组件和前沿架构的合成ViT的实测延迟。利用该数据集，我们证明了新ViT的推理延迟可以以足够高的准确度进行预测，适用于实际应用。
原文摘要 · Abstract (English)
Given the significant advances in machine learning techniques on mobile devices, particularly in the domain of computer vision, in this work we quantitatively study the performance characteristics of 190 real-world vision transformers (ViTs) on mobile devices. Through a comparison with 102 real-world convolutional neural networks (CNNs), we provide insights into the factors that influence the latency of ViT architectures on mobile devices. Based on these insights, we develop a dataset including measured latencies of 1000 synthetic ViTs with representative building blocks and state-of-the-art architectures from two machine learning frameworks and six mobile platforms. Using this dataset, we show that inference latency of new ViTs can be predicted with sufficient accuracy for real-world applications.
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