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 Regulation01:37

Neural Regulation

39.6K
Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
39.6K
Neural Circuits01:25

Neural Circuits

1.3K
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...
1.3K

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

Quantum Neural Network Realization of XOR on a Desktop Quantum Computer.

Sensors (Basel, Switzerland)·2026
Same author

Energy-, Cost-, and Resource-Efficient IoT Hazard Detection System with Adaptive Monitoring.

Sensors (Basel, Switzerland)·2025
Same author

A Novel Implementation of a Social Robot for Sustainable Human Engagement in Homecare Services for Ageing Populations.

Sensors (Basel, Switzerland)·2024
查看所有相关文章

相关实验视频

Updated: Jul 24, 2025

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

583

在二进制神经网络上的边缘设备的预计算批量规范化参数.

Nicholas Phipps1,2, Jin-Jia Shang1,2, Tee Hui Teo1

  • 1Engineering Product Development, Singapore University of Technology and Design, Singapore 487372, Singapore.

Sensors (Basel, Switzerland)
|July 8, 2023
PubMed
概括

二元化神经网络 (BNNs) 为边缘设备优化批量规范化 (BN). 预先计算BN参数可以显著降低63%的内存使用量,而不会影响准确性.

关键词:
批量规范化 批量规范化双元化神经网络是一个双元化的神经网络.卷积神经网络是一种卷积神经网络.边缘设备 边缘设备推理推论是指一个推理.

更多相关视频

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

10.4K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

444

相关实验视频

Last Updated: Jul 24, 2025

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

583
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

10.4K
Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

444

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 二元化神经网络 (BNN) 是量子化卷积神经网络 (CNN),通过降低参数精度来减少模型大小.
  • 批量规范化 (BN) 层在BNN中至关重要,但其浮点运算在边缘设备上是计算上昂贵的.

研究的目的:

  • 为了减少边缘设备上的BNN的内存足迹.
  • 在推理过程中优化BN层的计算效率.

主要方法:

  • 在量子化之前预计算批量规范化 (BN) 参数,以便在推理过程中利用模型的固定性.
  • 使用MNIST数据集实施和验证拟议的BNN方法.

主要成果:

  • 减少了63%的内存使用量,实现了860字节的模型大小.
  • 保持了与传统计算方法相比的准确性.
  • 将BN层计算所需的循环数减少到两个边缘设备.

结论:

  • 预计算BN参数是边缘设备BNN内存和计算优化的有效策略.
  • 拟议的方法可以显著节省内存,而不会影响模型的准确性.