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

Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...

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

Hand Gesture Recognition on Edge Devices: Sensor Technologies, Algorithms, and Processing Hardware.

Sensors (Basel, Switzerland)·2025
Same author

End-to-End Ultrasonic Hand Gesture Recognition.

Sensors (Basel, Switzerland)·2024
Same author

Toward Sensor Measurement Reliability in Blockchains.

Sensors (Basel, Switzerland)·2023
Same author

RESEKRA: Remote Enrollment Using SEaled Keys for Remote Attestation.

Sensors (Basel, Switzerland)·2022
Same author

Air-Writing Character Recognition with Ultrasonic Transceivers.

Sensors (Basel, Switzerland)·2021
Same author

Object Positioning Algorithm Based on Multidimensional Scaling and Optimization for Synthetic Gesture Data Generation.

Sensors (Basel, Switzerland)·2021

相关实验视频

Updated: Jun 26, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K

深度学习方法的硬件实现,以实现可重新配置的智能表面的最佳配置.

Alberto Martín-Martín1,2, Rubén Padial-Allué2, Encarnación Castillo2

  • 1eesy-Innovation GmbH, 82008 Unterhaching, Germany.

Sensors (Basel, Switzerland)
|February 10, 2024
PubMed
概括

本研究介绍了一种基于人工智能的方法,用于优化无线网络中可重新配置的智能表面 (RIS). 现场可编程网关阵列 (FPGA) 在RIS配置方面表现出卓越的性能,增强了6G通信.

关键词:
6G 6G是什么意思在FPGA中,FPGA是指FPGA.人工智能的人工智能是人工智能.神经网络的神经网络的神经网络可重新配置的智能表面.

更多相关视频

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K
A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning
11:32

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

Published on: January 19, 2022

3.4K

相关实验视频

Last Updated: Jun 26, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.3K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K
A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning
11:32

A Flexible Platform for Monitoring Cerebellum-Dependent Sensory Associative Learning

Published on: January 19, 2022

3.4K

科学领域:

  • 无线通信无线通信
  • 人工智能的人工智能
  • 边缘计算 边缘计算

背景情况:

  • 可重新配置的智能表面 (RIS) 对于定制无线网络中的无线传播至关重要,特别是对于6G.
  • 优化RIS配置是由于系统约束而面临的重大挑战.
  • 现有的方法很难有效地解决复杂的RIS配置问题.

研究的目的:

  • 提出一种基于人工智能 (AI) 和深度学习 (DL) 的新方法,用于RIS配置.
  • 为边缘计算应用开发和评估一个定制的卷积神经网络 (CNN).
  • 在各种边缘设备上比较人工智能驱动的RIS配置的性能,包括FPGA.

主要方法:

  • 为基于边缘的RIS配置开发一个自定义的卷积神经网络 (CNN).
  • 在各种边缘计算平台上实现和比较CNN:商业AI设备和现场可编程门阵列 (FPGA).
  • 使用速度和效率等指标进行性能评估,包括与FP32 GPU加速和INT8量子化TPU加速实现进行比较.

主要成果:

  • FPGA实现实现了显著的性能提升:比FP32 GPU加速20倍,比INT8量子化TPU加速近3倍.
  • 使用了高级合成 (HLS) 工具,证明了没有定制加速器的有效实施.
  • 在人工智能驱动的RIS配置任务中,FPGA表现出卓越的性能和效率.

结论:

  • 人工智能和DL,特别是边缘设备上的CNN,为挑战RIS配置问题提供了可行的解决方案.
  • 对于支持人工智能的RIS,FPGA提供了一个高性能和高效的硬件平台,性能优于商业人工智能加速器.
  • 由于FPGA固有的可重配置性,它们成为未来在先进无线系统中的RIS应用的关键推动者.