English(EN) Vision Transformers on the Edge: A Comprehensive Survey of Model Compression and Acceleration Strategies

综述详述面向边缘设备的视觉Transformer压缩技术

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-27 04:00

一篇新发表在arXiv上的综述论文详细介绍了在边缘设备上部署视觉Transformer（ViTs）的模型压缩与加速策略。该论文系统地回顾了各种模型压缩技术和面向硬件的加速方法，解决了与传统卷积神经网络（CNNs）相比，ViTs在计算和内存方面带来的重大挑战。它旨在通过分析不同平台（如GPU、ASIC和FPGA）上准确性、效率和硬件适应性之间的权衡，为研究人员和开发人员提供一份全面的指南。 AI

影响为在资源受限的边缘设备上实现强大的视觉Transformer提供了结构化的技术概述。

排序理由该集群包含一篇发表在arXiv上的综述论文，详细介绍了优化AI模型的技术策略。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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arXiv cs.CV TIER_1 English(EN) · Shaibal Saha, Lanyu Xu · 2026-10-27 04:00

边缘视觉 Transformer：模型压缩与加速策略的全面调研

arXiv:2503.02891v4 Announce Type: replace Abstract: In recent years, vision transformers (ViTs) have emerged as powerful and promising techniques for computer vision tasks such as image classification, object detection, and segmentation. Unlike convolutional neural networks (CNNs…

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边缘视觉 Transformer：模型压缩与加速策略的全面调研

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