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相关概念视频

Parallel Processing01:20

Parallel Processing

164
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...
164
Clamper Circuit01:14

Clamper Circuit

447
A clamper circuit, also known as a DC restorer, represents a specialized variant of the rectifier circuit, notable for its method of taking the output across the diode rather than the capacitor. This configuration lends to several distinctive applications, particularly in handling square wave inputs.
Within this circuit, the diode's orientation prompts the capacitor to charge up to the level of the most negative peak of the input signal. Upon reaching this state, the diode ceases to...
447
Accelerators01:17

Accelerators

77
Accelerators in concrete serve as admixtures to speed up the hardening process, enabling the concrete to achieve early strength faster. Although accelerators do not necessarily impact the time it takes concrete to set, they reduce this time in practice. A common accelerator is calcium chloride, which is particularly useful for hastening early strength development in cold weather or for rapid repair jobs that require quick heat generation after mixing.
The effectiveness of calcium chloride can...
77
Reducing Line Loss01:18

Reducing Line Loss

156
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...
156
Clipper Circuit01:18

Clipper Circuit

458
A clipper circuit is a fundamental wave-shaping device that harnesses the unique properties of diodes to alter and control waveform characteristics. This technology is widely used in electronic devices, especially in television and radar communication systems, where it enhances waveform modulation in both transmitters and receivers.
The operation of a clipper circuit can be exemplified by analyzing a dual-clipper configuration setup that integrates two ideal diodes, each paired with a biasing...
458
Block Diagram Reduction01:22

Block Diagram Reduction

221
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
221

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相关实验视频

Updated: Jul 13, 2025

Microfluidic Imaging Flow Cytometry by Asymmetric-detection Time-stretch Optical Microscopy ATOM
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AoCStream:全芯片CNN加速器,具有基于流的线路缓冲架构和加速器意识的修剪.

Hyeong-Ju Kang1, Byung-Do Yang2

  • 1School of Computer Science and Engineering, Korea University of Technology and Education, 1600 Chungjeol-ro, Dongnam-gu, Cheonan 31253, Republic of Korea.

Sensors (Basel, Switzerland)
|October 14, 2023
PubMed
概括

本研究介绍了EdgeAI和TinyML中的卷积神经网络 (CNN) 的记忆减少技术. 这些方法使得在资源有限的设备上只使用芯片内存实现高效的CNN实现.

关键词:
美国有线电视新闻网 (CNN) 加速器卷积神经网络是一种卷积神经网络.修剪 修剪 修剪 修剪

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 嵌入式系统 嵌入式系统

背景情况:

  • 卷积神经网络 (CNN) 对EdgeAI和TinyML至关重要,但需要大量的内存.
  • 外部内存需求阻碍了在资源有限的EdgeAI和TinyML应用中使用CNN的可行性.

研究的目的:

  • 在算法和架构层面为CNN提出新的记忆减少方法.
  • 为了实现高性能CNN只使用实际设备的芯片内存.

主要方法:

  • 实现了加速器意识的修剪,以减少重量记忆.
  • 提出了一种基于流的线路缓冲架构,用于激活内存的减少.
  • 采用管道式方法,使用专用层块和线路缓冲器代替框架缓冲器.

主要成果:

  • 成功减少中间数据存储大小.
  • 在低端FPGA上展示了对象检测CNN (MobileNetV1/V2,SSDLite变体) 的实现.
  • 在不需要外部存储器的情况下实现了实现.

结论:

  • 拟议的减少内存技术对于在资源有限的EdgeAI和TinyML环境中部署CNN是有效的.
  • 在芯片内存实现CNN是可行的实际应用程序,如对象检测在低端FPGA.