Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Parallel Processing01:20

Parallel Processing

182
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
182
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

455
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
455

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

6G-Enabling the New Smart City: A Survey.

Sensors (Basel, Switzerland)·2023
Same author

When Social Networks Meet D2D Communications: A Survey.

Sensors (Basel, Switzerland)·2019
查看所有相关文章

相关实验视频

Updated: Jul 23, 2025

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
06:25

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Published on: May 16, 2025

241

硬件-软件分区用于实时对象检测使用动态参数优化.

Corneliu Zaharia1, Vlad Popescu1, Florin Sandu1

  • 1Department of Electronics and Computers, Transilvania University, Bdul Eroilor 29, 500068 Brașov, Romania.

Sensors (Basel, Switzerland)
|July 11, 2023
PubMed
概括

本研究引入了一种混合硬件-软件方法,以增强实时对象检测. 人工智能管理FPGA上的硬件组件,提高计算机视觉算法性能和效率.

科学领域:

  • 计算机工程 计算机工程
  • 人工智能的人工智能
  • 嵌入式系统 嵌入式系统

背景情况:

  • 实时计算机视觉算法在内存带宽和能源消耗方面面临着挑战.
  • 智能手机和汽车系统等设备中的当前实现需要优化.
  • 对象检测对于各种监控和安全应用至关重要.

研究的目的:

  • 为了提高实时物体检测计算机视觉算法的质量.
  • 建议混合硬件-软件实现以提高性能.
  • 探索如何有效地将算法组件分配到硬件 (IP Cores) 和软件中.

主要方法:

  • 开发了一个混合硬件-软件实施策略.
  • 研究了将算法组件分配到硬件IP核心的方法.
  • 利用嵌入式人工智能进行动态硬件配置和参数调整.
  • 在Xilinx Zynq-7000 SoC FPGA演示器上实现了该解决方案.

主要成果:

  • 在对象检测用例中证明了显著的性能增长.
  • 展示了混合硬件-软件实现的好处.
  • 突出了人工智能管理的知识产权核心所取得的重大改进.
关键词:
在FPGA中,FPGA是指FPGA.适应性硬件资源集成资源的整合.人工智能的人工智能是人工智能.计算机视觉 计算机视觉嵌入式系统 嵌入式系统硬件加速器 硬件加速器混合实现 混合实现记忆带宽 记忆带宽对象检测检测对象检测对象检测

更多相关视频

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

581
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.0K

相关实验视频

Last Updated: Jul 23, 2025

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
06:25

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Published on: May 16, 2025

241
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

581
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.0K
  • 在实用的FPGA系统上验证了该方法.

    • 结论:

    • 混合硬件-软件实现为实时计算机视觉提供了巨大的好处.
    • 人工智能驱动的硬件IP核心的管理可以带来重大的性能提升.
    • 拟议的方法有效地解决了对物体检测的内存带宽和能源消耗方面的挑战.