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相关概念视频

Transformers in Distribution System01:27

Transformers in Distribution System

98
Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
Distribution substation transformers come in various ratings and typically use mineral oil for insulation and cooling. To prevent moisture and air from entering the oil, some transformers use an inert gas like nitrogen to fill the...
98
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

129
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
129
Types Of Transformers01:16

Types Of Transformers

943
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
943
Energy Losses in Transformers01:21

Energy Losses in Transformers

819
In an ideal transformer, it is assumed that there are no energy losses, and, hence, all the power at the primary winding is transferred to the secondary winding. However, in reality,  the transformers always have some energy losses, and, hence, the output power obtained at the secondary winding is less than the input power at the primary winding due to energy losses.
There are four main reasons for energy losses in transformers.
The first cause can be  the high resistance of the...
819
Convolution Properties I01:20

Convolution Properties I

133
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:
133
Convolution Properties II01:17

Convolution Properties II

166
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...
166

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相关实验视频

Updated: May 25, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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ADFQ-ViT: 视觉转换器的激活-分发-友好的培训后量化

Yanfeng Jiang1, Ning Sun2, Xueshuo Xie3

  • 1College of Computer Science, Nankai University, Tianjin, China; Tianjin Key Laboratory of Network and Data Security Technology, Tianjin, China.

Neural networks : the official journal of the International Neural Network Society
|February 26, 2025
PubMed
概括
此摘要是机器生成的。

视觉变压器 (ViT) 通过使用新的激活-分布-友好量子化 (ADFQ-ViT) 方法进行量子化. 这种方法显著减少了低位量化中的精度损失,以实现高效的计算机视觉模型部署.

关键词:
分发方式 分发方式 分发方式培训后的量化定量化视觉变压器 视觉变压器

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科学领域:

  • 计算机视觉 计算机视觉
  • 深度学习 (Deep Learning) 是一种深度学习.
  • 模型优化模型优化

背景情况:

  • 视觉变压器 (ViT) 实现了高性能,但需要大量的计算资源.
  • 现有的量子化方法在ViT的低位场景中与精度损失作斗争.
  • 在ViT中不同的激活分布挑战了传统的量子化技术.

研究的目的:

  • 为视觉转换器 (ViT) 开发一种新的培训后量化框架,以尽量减少在低位宽度下的准确性损失.
  • 解决ViT内部独特的激活分布特征,这些特征阻碍了标准量子化方法.
  • 为了能够在资源有限的设备上高效地推断ViT.

主要方法:

  • 视觉转换器 (ADFQ-ViT) 拟议的激活-分配-友好的培训后量化.
  • 引入了Per-Patch异常值感知量化器,用于LayerNorm后的激活.
  • 设计的Shift-Log2量子化器用于GELU后不均的激活.
  • 实施了注意力评分增强的模块智能化优化,以减轻错误.

主要成果:

  • 在图像分类,对象检测和实例细分方面,ADFQ-ViT显示了与基线相比显著的改进,在4位.
  • 在ImageNet上实现了5.17%的Top-1精度增加,用于4位量子化ViT-B模型.
  • 在视觉转换器的低位量子化准确性方面超越了现有的方法.

结论:

  • ADFQ-ViT有效地处理ViT中的唯一激活分布,以获得准确的低位量化.
  • 拟议的框架允许在资源有限的硬件上高效地部署ViT,而不会大幅降低性能.
  • ADFQ-ViT代表了优化视觉转换器用于实际应用的重大进步.