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

Acceleration Vectors01:30

Acceleration Vectors

8.1K
In everyday conversation, accelerating means speeding up. Acceleration is a vector in the same direction as the change in velocity, Δv, therefore the greater the acceleration, the greater the change in velocity over a given time. Since velocity is a vector, it can change in magnitude, direction, or both. Thus acceleration is a change in speed or direction, or both. For example, if a runner traveling at 10 km/h due east slows to a stop, reverses direction, and continues their run at 10 km/h...
8.1K
Reducing Line Loss01:18

Reducing Line Loss

154
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
154
Convolution Properties I01:20

Convolution Properties I

152
Convolution computations can be simplified by utilizing their inherent properties.
The commutative property reveals that the input and the impulse response of an LTI (Linear Time-Invariant) system can be interchanged without affecting the output:
152
Parallel Processing01:20

Parallel Processing

152
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...
152
Convolution Properties II01:17

Convolution Properties II

203
The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
203
Convolution: Math, Graphics, and Discrete Signals01:24

Convolution: Math, Graphics, and Discrete Signals

262
In any LTI (Linear Time-Invariant) system, the convolution of two signals is denoted using a convolution operator, assuming all initial conditions are zero. The convolution integral can be divided into two parts: the zero-input or natural response and the zero-state or forced response, with t0 indicating the initial time.
To simplify the convolution integral, it is assumed that both the input signal and impulse response are zero for negative time values. The graphical convolution process...
262

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

A Feature Extraction Method Based on Differential Entropy and Linear Discriminant Analysis for Emotion Recognition.

Sensors (Basel, Switzerland)·2019
Same author

Templated synthesis of a bifunctional Janus graphene for enhanced enrichment of both organic and inorganic targets.

Chemical communications (Cambridge, England)·2019
Same author

Heavy metals in maternal and cord blood in Beijing and their efficiency of placental transfer.

Journal of environmental sciences (China)·2019
Same author

Molecular basis for feedback inhibition of tyrosine-regulated 3-deoxy-d-arabino-heptulosonate-7-phosphate synthase from Escherichia coli.

Journal of structural biology·2019
Same author

Viruslike Element-Tagged Nanoparticle Inductively Coupled Plasma Mass Spectrometry Signal Multiplier: Membrane Biomarker Mediated Cell Counting.

Analytical chemistry·2019
Same author

Identification of Potential Long Noncoding RNA Biomarker of Mercury Compounds in Zebrafish Embryos.

Chemical research in toxicology·2019

相关实验视频

Updated: Jul 5, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K

卷积神经网络推理加速和性能优化对边缘智能进行研究.

Yong Liang1,2, Junwen Tan1,2, Zhisong Xie2

  • 1Key Laboratory of Advanced Manufacturing and Automation Technology (Guilin University of Technology), Education Department of Guangxi Zhuang, Autonomous Region, Guilin 541006, China.

Sensors (Basel, Switzerland)
|January 11, 2024
PubMed
概括

边缘智能 (EI) 面临着电力和计算方面的挑战. 研究人员在FPGA上优化了LeNet-5加速器,发现管道显著提高了性能,同时减少了与CPU和GPU相比的能源消耗.

关键词:
在FPGA中,FPGA是指FPGA.在 HLS HLS 中.深度学习是一种深度学习.边缘情报 边缘情报 边缘情报不同质的计算方式.

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

556
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

575

相关实验视频

Last Updated: Jul 5, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K
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

556
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

575

科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 硬件加速器 硬件加速器

背景情况:

  • 边缘智能 (EI) 集成了边缘计算和AI,使边缘设备上的AI算法成为可能.
  • 实际的EI部署受到计算能力,能源消耗,尺寸和成本限制的阻碍.
  • 不同质的并行计算平台对于克服EI的性能-功率权衡至关重要.

研究的目的:

  • 设计和实施优化的硬件加速器,用于边缘设备上的深度学习.
  • 评估EI应用的不同优化技术的性能和功率效率.
  • 调查量子化对FPGA对EI资源利用的影响.

主要方法:

  • 利用了Xilinx Zynq 7000异质计算平台.
  • 用于加速器设计的高层次合成 (HLS).
  • 实施并比较了两台使用循环解卷和管道优化技术的LeNet-5加速器.

主要成果:

  • 在100MHz的PIPELINE加速器,比UNROLL加速器显示了14.972倍的加速度,而功耗仅增加了8.09%.
  • 与CPU相比,PIPELINE加速器实现了70.387倍的加速度,并将功耗降低了91.37%.
  • 与GPU相比,PIPELINE加速器可以减少93.35%的功耗.

结论:

  • 与循环展开相比,管道优化为FPGA上的EI应用提供了卓越的性能.
  • 基于FPGA的加速器在EI的传统CPU和GPU上提供了显著的功率和速度优势.
  • 该研究提供了实用的硬件加速方案和对边缘智能应用的量子化效应的见解.