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Parallel Processing01:20

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EQ-ViT:算法-硬件共同设计,用于对实时视觉变压器推理在通用ACAP架构上的端到端加速.

Peiyan Dong1, Jinming Zhuang2, Zhuoping Yang2

  • 1Department of Electrical and Computer Engineering, Northeastern University, Boston, MA 02115, USA.

IEEE transactions on computer-aided design of integrated circuits and systems : a publication of the IEEE Circuits and Systems Society
|August 8, 2025
PubMed
概括
此摘要是机器生成的。

本研究介绍了EQ-ViT,这是一个加速框架,可实现实时视觉变压器 (ViT) 部署. 在AMD Versal ACAP上,EQ-ViT实现了显著的加速度和精度改进,克服了计算机视觉应用中的延迟挑战.

关键词:
在FPGA中,FPGA是指FPGA.设计用于太空探索的设计.嵌入式系统 嵌入式系统硬件/软件共同设计高层次的合成.建模 建模模型 建模模型性能优化 性能优化 性能优化可以重新配置的逻辑.

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科学领域:

  • 计算机视觉 计算机视觉
  • 硬件加速器 硬件加速器
  • 机器学习 机器学习

背景情况:

  • 视觉转换器 (ViT) 在计算机视觉方面表现有前途,但在实时 (<1毫秒) 应用中面临部署挑战.
  • 现有的平台 (CPU,GPU,FPGAs) 难以满足确定性低延迟要求,即使是模型定量化.
  • 修剪和稀疏技术减少模型大小,但往往导致准确性损失.

研究的目的:

  • 提出EQ-ViT,一个端到端加速框架,用于实时ViT部署.
  • 在AMD Versal ACAP上共同设计算法和硬件架构,以实现高效的ViT加速.
  • 为了克服当前ViT加速方法中的准确度-延迟权衡.

主要方法:

  • 深入的核心级性能概况,以识别现有加速解决方案中的瓶.
  • 开发一个空间和异质的EQ-ViT架构,利用ACAP的FPGA和AI引擎 (AIE) 资源.
  • 实施量子化意识训练策略 (EQ-ViT算法) 用于8位权重和激活量子化,包括非线性函数.
  • 在AMD Versal ACAP上为各种ViT应用部署EQ-ViT的自动化框架的设计.

主要成果:

  • 通过EQ-ViT.实现了2.4%的精度改进.
  • 获得的平均加速度是vCPU的315.0x,GPU的3.39x-59.5x,FPGA的3.38x-13.1x.
  • 与各种计算解决方案相比,显著提高了能源效率,从12.82倍到62.2倍.

结论:

  • 在AMD Versal ACAP上,EQ-ViT有效地实现了视觉变压器实时加速.
  • 拟议的框架实现了卓越的性能和能源效率,而不会影响准确性.
  • EQ-ViT提供了一个可行的解决方案,用于在实时场景中部署苛刻的计算机视觉任务.